Indicative is the leading customer analytics platform offering businesses the ability to analyze customer behavior across all digital touchpoints, without requiring involvement from BI specialists or engineers, or even the knowledge of complicated code or SQL

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**LONDON, United Kingdom, May 16th, 2024/Chainwire/--**Talos, the premier provider of digital asset trading technology for institutions, and One Trading, a crypto trading venue headquartered and regulated in the European Union, announced an integration designed to expand liquidity access for institutions. As part of the collaboration, One Trading joins the Talos network of liquidity providers, expanding the possible destinations for their shared clients looking to achieve best execution. Talos users will now be able to access One Trading’s high-speed trading platform with a matching engine time of just 1 microsecond. In addition to expanding its potential reach to Talos’s institutional clients, One Trading also adopts the Talos trading platform as a sell-side client to support its OTC trading desk. The award-winning Talos trading platform will help One Trading efficiently source liquidity to better serve its clients’ needs. The multi-faceted relationship between the two firms highlights the different ways that the Talos network and platform can enhance a partner’s business. “We are thrilled to welcome One Trading into the Talos network of liquidity providers,” said Daniel Packham, VP and Head of Operations, EMEA. “They have built a leading regulated trading venue in Europe using cutting-edge technology to build an incredibly fast and secure exchange, holding themselves to the highest standards of security and transparency that are important to Talos and our institutional clients. In addition, as a client themselves using our trading platform, One Trading is a great example of how an OTC dealer can leverage Talos to help manage liquidity efficiently.” Commenting on the integration, Joshua Barraclough, CEO of One Trading, said, “Talos shares our goal of bridging the gap between traditional and crypto asset trading. That’s why we’re excited to integrate with Talos as a liquidity provider as well as a sell-side trading client. They provide us with the same institutional grade of technology that we seek to provide to our own clients.” One Trading is an EU-based trading venue built by a highly experienced ex-TradFi team. One Trading provides an institutional-grade digital asset exchange for both retail customers and institutional clients, with a focus on achieving market-leading execution speed, deep order books, and low fees. The exchange provides zero fees for both maker and taker transactions. One Trading operates a regulated spot trading venue and an OTC business with plans to roll out a MiFID II regulated derivatives business in the near future. As the Markets in Crypto Assets (MiCA) European regulation elevates the importance of best execution, Talos’s integration with One Trading will empower shared clients with an additional option for sourcing the best available liquidity. About Talos Talos provides institutional-grade technology that supports the full digital asset trading lifecycle, including liquidity sourcing, price discovery, trading, settlement, lending, borrowing, and portfolio management. Engineered by a team with unmatched experience building institutional trading systems, the Talos platform connects institutions to key participants in today’s digital asset ecosystem – exchanges, OTC desks, prime brokers, lenders, custodians, and more – through a single point of entry. By streamlining the entire trading process, Talos helps mitigate intermediary risk and facilitate best execution. For additional information, visit www.talos.com. Talos Disclaimer: Talos offers software-as-a-service products that provide connectivity tools for institutional clients. Talos does not provide clients with any pre-negotiated arrangements with liquidity providers or other parties. Clients are required to negotiate bilaterally and independently with liquidity providers and other parties. Talos is not a party to any of these arrangements. Services and venues may not be available in all jurisdictions. About One Trading One Trading is a leading European digital asset trading platform with a VASP registration in Italy with Organismo Agenti e Mediatori (OAM). The One Trading platform has various offerings: Exchange, Instant Trade, and an OTC desk. The Exchange is where registered customers can access the fastest trading venue in the world with zero fees — also boasting a transparent order book with deep liquidity, and charting tools for technical analysis. The team behind One Trading has strong ex-TradFi expertise and is focused on providing an unparalleled product experience. Instant Trade offers a simplified UI for trading a wide range of fiat, stablecoin, and altcoin pairs at 0% additional commission. One Trading retrieves the best prices for customers by plugging into a number of major liquidity providers with access to deep liquidity. These relationships have been established through the over-the-counter (OTC) offering and are typically only reserved for HNWs or institutional customers. Through Instant Trade, all trader types can access this unique trading mechanism through a simple UI. The OTC desk offers a high-touch trading team to work with clients, typically institutional partners, to facilitate trades of any size and digital asset, with access to deep pools of liquidity, rapid settlement, and large asset coverage. One Trading Disclaimer: This material is for informational purposes only and is not intended to provide legal, tax, financial, or investment advice. Past performance is not necessarily indicative of the future nor a reliable indicator of the likely performance of any investment. Recipients should consult their own advisors before making these types of decisions. One Trading has no responsibility or liability for any decision made or any other acts or omissions in connection with the Recipient’s use of this material. Contact Andy Keelaghan One Trading info@3webdigital.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. **LONDON, United Kingdom, May 16th, 2024/Chainwire/--**Talos, the premier provider of digital asset trading technology for institutions, and One Trading, a crypto trading venue headquartered and regulated in the European Union, announced an integration designed to expand liquidity access for institutions. As part of the collaboration, One Trading joins the Talos network of liquidity providers, expanding the possible destinations for their shared clients looking to achieve best execution. Talos users will now be able to access One Trading’s high-speed trading platform with a matching engine time of just 1 microsecond. In addition to expanding its potential reach to Talos’s institutional clients, One Trading also adopts the Talos trading platform as a sell-side client to support its OTC trading desk. The award-winning Talos trading platform will help One Trading efficiently source liquidity to better serve its clients’ needs. The multi-faceted relationship between the two firms highlights the different ways that the Talos network and platform can enhance a partner’s business. “We are thrilled to welcome One Trading into the Talos network of liquidity providers,” said Daniel Packham, VP and Head of Operations, EMEA. “We are thrilled to welcome One Trading into the Talos network of liquidity providers,” said Daniel Packham, VP and Head of Operations, EMEA. “They have built a leading regulated trading venue in Europe using cutting-edge technology to build an incredibly fast and secure exchange, holding themselves to the highest standards of security and transparency that are important to Talos and our institutional clients. In addition, as a client themselves using our trading platform, One Trading is a great example of how an OTC dealer can leverage Talos to help manage liquidity efficiently.” “They have built a leading regulated trading venue in Europe using cutting-edge technology to build an incredibly fast and secure exchange, holding themselves to the highest standards of security and transparency that are important to Talos and our institutional clients. In addition, as a client themselves using our trading platform, One Trading is a great example of how an OTC dealer can leverage Talos to help manage liquidity efficiently.” Commenting on the integration, Joshua Barraclough, CEO of One Trading, said, “Talos shares our goal of bridging the gap between traditional and crypto asset trading. That’s why we’re excited to integrate with Talos as a liquidity provider as well as a sell-side trading client. They provide us with the same institutional grade of technology that we seek to provide to our own clients.” “Talos shares our goal of bridging the gap between traditional and crypto asset trading. That’s why we’re excited to integrate with Talos as a liquidity provider as well as a sell-side trading client. They provide us with the same institutional grade of technology that we seek to provide to our own clients.” One Trading is an EU-based trading venue built by a highly experienced ex-TradFi team. One Trading provides an institutional-grade digital asset exchange for both retail customers and institutional clients, with a focus on achieving market-leading execution speed, deep order books, and low fees. The exchange provides zero fees for both maker and taker transactions. One Trading operates a regulated spot trading venue and an OTC business with plans to roll out a MiFID II regulated derivatives business in the near future. As the Markets in Crypto Assets (MiCA) European regulation elevates the importance of best execution, Talos’s integration with One Trading will empower shared clients with an additional option for sourcing the best available liquidity. About Talos Talos provides institutional-grade technology that supports the full digital asset trading lifecycle, including liquidity sourcing, price discovery, trading, settlement, lending, borrowing, and portfolio management. Engineered by a team with unmatched experience building institutional trading systems, the Talos platform connects institutions to key participants in today’s digital asset ecosystem – exchanges, OTC desks, prime brokers, lenders, custodians, and more – through a single point of entry. By streamlining the entire trading process, Talos helps mitigate intermediary risk and facilitate best execution. For additional information, visit www.talos.com . www.talos.com www.talos.com Talos Disclaimer: Talos offers software-as-a-service products that provide connectivity tools for institutional clients. Talos does not provide clients with any pre-negotiated arrangements with liquidity providers or other parties. Clients are required to negotiate bilaterally and independently with liquidity providers and other parties. Talos is not a party to any of these arrangements. Services and venues may not be available in all jurisdictions. About One Trading One Trading is a leading European digital asset trading platform with a VASP registration in Italy with Organismo Agenti e Mediatori (OAM). The One Trading platform has various offerings: Exchange, Instant Trade, and an OTC desk. The Exchange is where registered customers can access the fastest trading venue in the world with zero fees — also boasting a transparent order book with deep liquidity, and charting tools for technical analysis. The team behind One Trading has strong ex-TradFi expertise and is focused on providing an unparalleled product experience. One Trading One Trading Exchange Exchange Instant Trade offers a simplified UI for trading a wide range of fiat, stablecoin, and altcoin pairs at 0% additional commission. One Trading retrieves the best prices for customers by plugging into a number of major liquidity providers with access to deep liquidity. These relationships have been established through the over-the-counter (OTC) offering and are typically only reserved for HNWs or institutional customers. Through Instant Trade, all trader types can access this unique trading mechanism through a simple UI. Instant Trade Instant Trade The OTC desk offers a high-touch trading team to work with clients, typically institutional partners, to facilitate trades of any size and digital asset, with access to deep pools of liquidity, rapid settlement, and large asset coverage. OTC OTC One Trading Disclaimer: This material is for informational purposes only and is not intended to provide legal, tax, financial, or investment advice. Past performance is not necessarily indicative of the future nor a reliable indicator of the likely performance of any investment. Recipients should consult their own advisors before making these types of decisions. One Trading has no responsibility or liability for any decision made or any other acts or omissions in connection with the Recipient’s use of this material. Contact Andy Keelaghan One Trading info@3webdigital.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here . here here

One Trading Partners With Talos To Extend Institutional Trading Services Across Europe

Colliding Magnetospheres in The Young High-Eccentricity Binary DQ Tau: Discussion

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Deepfake – a combination of ‘deep learning’ and ‘fake’ – refers to advanced artificial intelligence used towards the creation of synthetic yet highly realistic images, audio, and video hoaxes[1]. This can be done by creating entirely original material, all of which is entirely false, or by taking pre-existing material and swapping audio/ visual elements with that of another person. Either way, the impact of deepfakes on public trust and wider society has been widely discussed, especially since the proliferation of fake news in 2016 political events[2] (Trump in the US and Brexit in the UK). While deepfakes were once a somewhat intangible threat, to be dealt with in the future, they have now evolved to a level of sophistication that presents genuine, immediate threats to many aspects of society. The likes of OpenAI’s release of text-to-video generative AI, Sora[3], catalyze such risk. In this article, we will explore how deepfakes can erode trust within the legal system, affecting court procedures, evidence gathering, and even free speech. A Brief History of Technology and Court Evidence Types Evidence plays a crucial role in any courtroom but is only ever as good as every one’s ability to trust in it. We started with witness testimony and physical artifacts which are now trumped by voice recordings, photographs, and videos such as CCTV. Technology-based evidence creates more concrete arguments: a video of someone committing a murder is a bit more convincing than the murder weapon being found in their possession. Hence, “historically, audio and video evidence are considered [the] gold standard”[3]. But with all types of evidence, there is room for manipulation. An eyewitness could lie or misinterpret something they see. A murder weapon can be planted, and fingerprints can be tampered with. Once technology is introduced, these manipulations scale in complexity. Voice editing software and photo-editing techniques, e.g., photoshop, can cast doubt on the authenticity of audio and visual evidence. Where with traditional dishonesty there is often a physical trail of evidence to follow, technological manipulation requires a higher level of skill to be able to trace it, e.g., inconsistent time stamps, audio splicing, and minor discrepancies indicative of tampering[4]. With the evolution of technology, the legal system has therefore had to adapt its best practice procedures over time. With regards to evidence gathering and use, this has meant more robust and stringent measures to test authenticity and reliability, as well as a new direction of forensic sourcing/ testing (digital forensics[5]) and chain of custody checks[6]. However, deepfakes now present an even more significant leap in evidence manipulation capabilities. These sophisticated AI-generated media can convincingly depict individuals saying or doing things they never actually did. Unlike previous forms of manipulation, deepfakes go so far as to blur the line between reality and fabrication, posing unprecedented challenges for courts in assessing the authenticity and reliability of audio-visual evidence. Case Studies The most obvious ways deep fakes can be used to help circumvent the law are: The fabrication of evidence to provide alibis for activities and prove innocence.
The fabrication of evidence showing someone’s guilt and involvement in a crime.
Using the possibility of deepfakes to protest the legitimacy of authentic yet incriminating material7. Our first case study looks at fabricating evidence to incriminate someone wrongfully. In 2019, A deepfaked recording was used in a UK custody battle to discredit the father’s worthiness of shared custody. According to the father’s lawyer, Byron James, a “heavily doctored recording” had been presented to the court in which the father is heard making “direct and violent” threats to his wife[8]. However, after further examination, it was found that the recording presented in court had been manipulated to include words that had not been used by the father. In fact, demonstrative of how increasingly accessible editing technologies have become, the mother had used “software and online tutorials to put together a plausible file”[8]. While James had not previously encountered AI-doctored evidence in the courtroom, he has since commented on how it calls into question what kind of evidence can actually be trusted these days. Had they not discovered the tampering, and managed to obtain the original file and metadata, the mother may have successfully persuaded the courtroom as to the father’s fictitiously violent character. While the outcome of, and those involved in the hearing are confidential, it highlights the dangers of taking audio/visual evidence at face value. Not only this, but with the increasingly widespread ease and access to deepfake technologies, versus the relatively older age of judges, the necessary weariness of deep learning technologies may not be fully understood. Our second case study looks at people claiming real videos are deepfakes. In a more high-profile case, Elon Musk’s lawyers attempted to get a lawsuit against Tesla dismissed by claiming Musk had been the victim of deepfake videos. In 2016, Musk was videoed at a tech conference speaking about the extreme safety of Tesla’s Model S and Model X self-driving autonomous features. The videos from this interview have been on YouTube for 7 years now. However, in 2023, the videos resurfaced when a man died after his Tesla crashed while in self-driving mode, and the man’s family and lawyers cited those 2016 claims. Musk’s lawyers tried to deny their authenticity[7]. Being such a public figure, Musk is indeed the subject of deepfakes, which is what his lawyers tried to argue in this instance. The video here was, of course, real though, and the courts did not buy Musk’s lawyers’ claims. However, this highlights how in the age of deepfakes people can not only fabricate but also deny reality. “As people become more aware of how easy it is to fake audio and video, bad actors can weaponize that scepticism”[7]. Especially for public figures, deepfakes offer a layer of protection and immunity behind which they can hide, and avoid taking ownership of reality. Even for non-public figures, instances like this have been seen in courts before, and continue to increase in commonality. Deepfakes and International Regulation Recent regulations targeting deepfakes have emerged globally, with notable initiatives like California's disclosure requirement for AI-generated political ads[9] and the UK's Online Safety Act[10] mandating social media platforms to crack down on harmful content, explicitly including deepfakes. This said, the majority of deepfake regulation currently only really targets deepfakes used with pornographic intent, rather than to circumvent justice systems. While these regulations offer frameworks for accountability and deterrence, their effectiveness hinges on enforcement, resource allocation, skill, and adaptability. Furthermore, although regulation will only grow in necessity for deepfakes under many applications, governments and policymakers will have to consider the potential for negative unintended consequences. This includes impacts on freedom of speech and expression. Vaguely worded laws could lead to censorship or self-censorship, reducing participation in public dialogue and harming democracy. Balancing the objective of combatting deceptive deepfake media with safeguarding human rights will continue as a nuanced challenge, showing the complexities of the ethical considerations surrounding deepfakes, society, and public trust. References 1 What is deepfake AI? A definition from TechTarget 2 Exposure to untrustworthy websites in the 2016 U.S. election - PMC 3 AI deepfakes are poised to enter court proceedings at time of low trust in legal system 4 Authentication of Digital Audio Recordings 5 Understanding Digital Forensics: Process, Techniques, and Tools 6 Digital Forensics and the Chain of Custody: How Is Electronic Evidence Collected and Safeguarded? 7 People are trying to claim real videos are deepfakes. The courts are not amused 8 'Deepfake' audio evidence used in UK court to discredit Dubai dad 9 California Looks to Boost Deepfake Protections Before Elections 10 New laws to better protect victims from abuse of intimate images - GOV.UK Deepfake – a combination of ‘deep learning’ and ‘fake’ – refers to advanced artificial intelligence used towards the creation of synthetic yet highly realistic images, audio, and video hoaxes[1]. This can be done by creating entirely original material, all of which is entirely false, or by taking pre-existing material and swapping audio/ visual elements with that of another person. Either way, the impact of deepfakes on public trust and wider society has been widely discussed, especially since the proliferation of fake news in 2016 political events[2] (Trump in the US and Brexit in the UK). While deepfakes were once a somewhat intangible threat, to be dealt with in the future, they have now evolved to a level of sophistication that presents genuine, immediate threats to many aspects of society. The likes of OpenAI’s release of text-to-video generative AI, Sora[3], catalyze such risk. In this article, we will explore how deepfakes can erode trust within the legal system, affecting court procedures, evidence gathering, and even free speech. A Brief History of Technology and Court Evidence Types Evidence plays a crucial role in any courtroom but is only ever as good as every one’s ability to trust in it. We started with witness testimony and physical artifacts which are now trumped by voice recordings, photographs, and videos such as CCTV. Technology-based evidence creates more concrete arguments: a video of someone committing a murder is a bit more convincing than the murder weapon being found in their possession. Hence, “historically, audio and video evidence are considered [the] gold standard”[3]. But with all types of evidence, there is room for manipulation. An eyewitness could lie or misinterpret something they see. A murder weapon can be planted, and fingerprints can be tampered with. Once technology is introduced, these manipulations scale in complexity. Voice editing software and photo-editing techniques, e.g., photoshop, can cast doubt on the authenticity of audio and visual evidence. Where with traditional dishonesty there is often a physical trail of evidence to follow, technological manipulation requires a higher level of skill to be able to trace it, e.g., inconsistent time stamps, audio splicing, and minor discrepancies indicative of tampering[4]. With the evolution of technology, the legal system has therefore had to adapt its best practice procedures over time. With regards to evidence gathering and use, this has meant more robust and stringent measures to test authenticity and reliability, as well as a new direction of forensic sourcing/ testing (digital forensics[5]) and chain of custody checks[6]. However, deepfakes now present an even more significant leap in evidence manipulation capabilities. These sophisticated AI-generated media can convincingly depict individuals saying or doing things they never actually did. Unlike previous forms of manipulation, deepfakes go so far as to blur the line between reality and fabrication, posing unprecedented challenges for courts in assessing the authenticity and reliability of audio-visual evidence. Case Studies The most obvious ways deep fakes can be used to help circumvent the law are: The fabrication of evidence to provide alibis for activities and prove innocence. The fabrication of evidence showing someone’s guilt and involvement in a crime. Using the possibility of deepfakes to protest the legitimacy of authentic yet incriminating material7. The fabrication of evidence to provide alibis for activities and prove innocence. The fabrication of evidence showing someone’s guilt and involvement in a crime. Using the possibility of deepfakes to protest the legitimacy of authentic yet incriminating material7. Our first case study looks at fabricating evidence to incriminate someone wrongfully. In 2019, A deepfaked recording was used in a UK custody battle to discredit the father’s worthiness of shared custody. According to the father’s lawyer, Byron James, a “heavily doctored recording” had been presented to the court in which the father is heard making “direct and violent” threats to his wife[8]. However, after further examination, it was found that the recording presented in court had been manipulated to include words that had not been used by the father. In fact, demonstrative of how increasingly accessible editing technologies have become, the mother had used “software and online tutorials to put together a plausible file”[8]. While James had not previously encountered AI-doctored evidence in the courtroom, he has since commented on how it calls into question what kind of evidence can actually be trusted these days. Had they not discovered the tampering, and managed to obtain the original file and metadata, the mother may have successfully persuaded the courtroom as to the father’s fictitiously violent character. While the outcome of, and those involved in the hearing are confidential, it highlights the dangers of taking audio/visual evidence at face value. Not only this, but with the increasingly widespread ease and access to deepfake technologies, versus the relatively older age of judges, the necessary weariness of deep learning technologies may not be fully understood. Our second case study looks at people claiming real videos are deepfakes. In a more high-profile case, Elon Musk’s lawyers attempted to get a lawsuit against Tesla dismissed by claiming Musk had been the victim of deepfake videos. In 2016, Musk was videoed at a tech conference speaking about the extreme safety of Tesla’s Model S and Model X self-driving autonomous features. The videos from this interview have been on YouTube for 7 years now. However, in 2023, the videos resurfaced when a man died after his Tesla crashed while in self-driving mode, and the man’s family and lawyers cited those 2016 claims. Musk’s lawyers tried to deny their authenticity[7]. Being such a public figure, Musk is indeed the subject of deepfakes, which is what his lawyers tried to argue in this instance. The video here was, of course, real though, and the courts did not buy Musk’s lawyers’ claims. However, this highlights how in the age of deepfakes people can not only fabricate but also deny reality. “As people become more aware of how easy it is to fake audio and video, bad actors can weaponize that scepticism”[7]. Especially for public figures, deepfakes offer a layer of protection and immunity behind which they can hide, and avoid taking ownership of reality. Even for non-public figures, instances like this have been seen in courts before, and continue to increase in commonality. Deepfakes and International Regulation Recent regulations targeting deepfakes have emerged globally, with notable initiatives like California's disclosure requirement for AI-generated political ads[9] and the UK's Online Safety Act[10] mandating social media platforms to crack down on harmful content, explicitly including deepfakes. This said, the majority of deepfake regulation currently only really targets deepfakes used with pornographic intent, rather than to circumvent justice systems. While these regulations offer frameworks for accountability and deterrence, their effectiveness hinges on enforcement, resource allocation, skill, and adaptability. Furthermore, although regulation will only grow in necessity for deepfakes under many applications, governments and policymakers will have to consider the potential for negative unintended consequences. This includes impacts on freedom of speech and expression. Vaguely worded laws could lead to censorship or self-censorship, reducing participation in public dialogue and harming democracy. Balancing the objective of combatting deceptive deepfake media with safeguarding human rights will continue as a nuanced challenge, showing the complexities of the ethical considerations surrounding deepfakes, society, and public trust. References 1 What is deepfake AI? A definition from TechTarget What is deepfake AI? A definition from TechTarget What is deepfake AI? A definition from TechTarget 2 Exposure to untrustworthy websites in the 2016 U.S. election - PMC Exposure to untrustworthy websites in the 2016 U.S. election - PMC Exposure to untrustworthy websites in the 2016 U.S. election - PMC 3 AI deepfakes are poised to enter court proceedings at time of low trust in legal system AI deepfakes are poised to enter court proceedings at time of low trust in legal system AI deepfakes are poised to enter court proceedings at time of low trust in legal system 4 Authentication of Digital Audio Recordings Authentication of Digital Audio Recordings Authentication of Digital Audio Recordings 5 Understanding Digital Forensics: Process, Techniques, and Tools Understanding Digital Forensics: Process, Techniques, and Tools Understanding Digital Forensics: Process, Techniques, and Tools 6 Digital Forensics and the Chain of Custody: How Is Electronic Evidence Collected and Safeguarded? Digital Forensics and the Chain of Custody: How Is Electronic Evidence Collected and Safeguarded? Digital Forensics and the Chain of Custody: How Is Electronic Evidence Collected and Safeguarded? 7 People are trying to claim real videos are deepfakes. The courts are not amused People are trying to claim real videos are deepfakes. The courts are not amused People are trying to claim real videos are deepfakes. The courts are not amused 8 'Deepfake' audio evidence used in UK court to discredit Dubai dad 'Deepfake' audio evidence used in UK court to discredit Dubai dad 9 California Looks to Boost Deepfake Protections Before Elections California Looks to Boost Deepfake Protections Before Elections California Looks to Boost Deepfake Protections Before Elections 10 New laws to better protect victims from abuse of intimate images - GOV.UK New laws to better protect victims from abuse of intimate images - GOV.UK New laws to better protect victims from abuse of intimate images - GOV.UK

AI and the Law: Deepfakes, Eroding Trust, and the Legal Tightrope

Security Tends Toward Reactivity Securing organizations tends toward response if not just reactivity – backups are often at the end of the day and not usually tested to ensure that a restore would be valid; activity logs are aggregated and analyzed later on, so if anything happened, it’s discovered later; typical alerts are overabundant and take time to search through the bits and bytes in efforts to cut through the high amount of false positives to find any true culprit; and cameras come into play after something happens to see in hindsight what may have occurred. The search for real-time detection and analysis is evergreen. This real-time process has reached a highly respectable level with malware, email spoofing/phishing, and dangerous domain detection and protection, as demonstrated by the ubiquitous inclusion of features such as EDR, XDR, and in-depth email analysis in antimalware and internet security suites. A major driver of security solutions is the advancement in crime. Much of modern-day crime deals with stealing peoples’ data, so the tactics for that theft are improving. Additionally, the attack surface of data repositories is growing, making it easier for criminals to steal and harder for defenders to protect. Data is Treasure Data resembles treasure, and just like real treasure, it holds immense value but requires effort to be obtained and protected. · Importance: Just like some treasures are rare and irreplaceable, certain types of data are crucial and hold significant value. This could be financial data, intellectual property, personal information, or medical records. · Potential:  Like treasure chests, raw data might not reveal its true worth until analyzed and interpreted. Hidden patterns, trends, and insights can be extracted through data analysis, leading to valuable discoveries. · Vulnerability and Need for Protection: Similar to physical treasure, data requires robust security measures to prevent unauthorized access, breaches, and leaks. · Discovery and Exploration: The thrill of the hunt is a big part of the treasure hunting. Data exploration can lead to groundbreaking scientific discoveries, advancements in healthcare, and solving complex problems across various fields. Unlike a chest full of gold, data is constantly being generated and can be easily replicated. The true value lies in how it's managed, analyzed, and used. Just as a skilled treasure hunter needs tools and knowledge to find value, organizations need proper data management strategies and analytical tools to unlock the true potential of their data. Principles and Components of DDR According to a by ISC2 and Cloud Security Insiders: “Organizations are rapidly shifting workloads to the cloud, with 39% already operating more than half of their workloads in the cloud.” And in their study on adoption of cloud services, “Large companies saw an increase of 67%, and smaller companies increased cloud service use by 38%.” 2023 joint report according to Cloudwards To further underscore the value of data: “…data is the lifeblood of an organization…It’s not realistic to stop employees from collaborating with data or moving it to new places and systems to make better use of it. follows data across all these different assets.” Data Detection and Response Data Detection and Response (DDR) is an iteration of data security technology designed to address the challenges of protecting sensitive data. DDR focuses on the data itself, rather than just relying on perimeter defenses, and involves continuously monitoring and analyzing data flows in real-time across networks, endpoints, and cloud environments. The key principles of DDR include: · Classifying data based on its lineage and behavior, not just content alone · Tracking data movement across different assets and systems to maintain visibility · Taking real-time action to prevent data exfiltration and stop potential threats The primary components of DDR are: Monitoring: Continuously scan data activity across various sources (logs, endpoints, cloud environments). Detection: Identify anomalous data access and suspicious behavior using threat intelligence, analytics, and machine learning. Alerting: Generate timely and actionable alerts for security teams to investigate. Response: Take steps to isolate threats, contain breaches, remediate vulnerabilities, and recover compromised data. Data Detection – Your Resident Expert Data detection employs a combination of algorithms, statistical analysis, and machine learning to analyze vast amounts of data for irregularities. Security teams are : By establishing baseline patterns of normal behavior, data detection can swiftly identify deviations indicative of potential threats. This continuous monitoring of data streams, identifying those outliers, and flagging suspicious activities for further investigation is a complex dance necessary for proper discovery of relevant and real threats. Through this proactive approach, data detection acts as a frontline defense, enabling organizations to swiftly respond to emerging threats and safeguard their data assets. looking for anomalies “Anomaly detection, also known as outlier detection, identifies data objects or patterns that deviate from a dataset’s normal behavior. and identify anomalies that could signal potential threats.” Importance of Data Response Data response refers to the actions taken once potential threats or anomalies are detected in the data environment. This involves implementing real-time incident response measures to mitigate risks rapidly and effectively. DDR frameworks include incident response plans that empower organizations to respond swiftly to incidents, reducing the time it takes to detect and remediate threats and limiting the damage. Importance of proactive monitoring and continuous improvement. Proactive monitoring and continuous improvement are paramount in maintaining robust cybersecurity measures. By actively monitoring data systems, organizations can better detect and respond to threats before they escalate into full-scale breaches. Continuous improvement ensures that security measures progress alongside evolving threats and technological advancements. This proactive approach a) enhances resilience and b) fosters an organization-wide culture of vigilance and adaptability. Ultimately, it enables businesses to stay ahead of cyber threats and safeguard their data assets effectively. The Road Ahead Data is rapidly increasing in amount and its storage in cloud services. The better one’s ability to detect data motion and location, the better one can protect everyone involved. What’s your next step in protecting that treasure (whose whereabouts are known to you, not hidden)?

Data Security in the Cloud: Why You Need Data Detection and Response (DDR)

Channels in Go are a core part of the language's concurrency model, which is built around the principles of Communicating Sequential Processes (CSP). A channel in Go is a powerful tool for communication between goroutines, and Go's lightweight threads, enabling a safe and synchronized exchange of data without the need for traditional lock-based synchronization techniques. Channels serve 2 primary purposes in Go: : They ensure that data exchanges between goroutines are synchronized, preventing race conditions and ensuring data integrity. Synchronization : Channels facilitate the passing of data between goroutines, acting as a conduit for data flow in concurrent Go applications. Communication According to the Go specification: Channels are typed by the values they convey. This means that a channel has a specific type, and all communications through that channel must adhere to this type, ensuring type safety across concurrent operations. Key characteristics of channels: : Each channel is associated with a specific type of data it can transport. This type is determined at the time of channel creation. Typed : By default, sending and receiving operations on a channel block until the other side is ready. This blocking behavior is essential for synchronization, allowing goroutines to coordinate their execution flow. Blocking Operations : Go supports both buffered and unbuffered channels. Unbuffered channels do not store any values and are used for direct communication between goroutines. In contrast, buffered channels have a capacity and can hold a finite number of values for asynchronous communication. Buffered and Unbuffered Declaring channels In Go, channels are created using the function, which initializes and returns a reference to a channel. make The syntax for declaring a channel is: chanVar := make(chan Type) Where is the variable name of the channel, and specifies the type of data that the channel is intended to transport. It's important to note that the data type must be specified, as channels are strongly typed. chanVar Type Example of declaring a channel: messageChannel := make(chan string) This line of code creates a channel for transmitting strings, referred to by the variable . messageChannel Internals of Go channels Understanding the internals of Go channels involves delving into the struct, which forms the backbone of channel operations in Go. Let's break down how channels work under the hood, focusing on the struct and the initialization process when a channel is created with . hchan hchan make A channel in Go is more than just a conduit for communication between goroutines. It's a well-defined structure in memory, represented by . The struct is crucial for managing the state and operations of a channel, including sending, receiving, and synchronization. hchan hchan Here's an overview of its main elements: : This field holds the total number of elements currently in the queue. It's essential for understanding the channel's current load. qcount : Specifies the size of the circular queue. For an unbuffered channel, this is 0; for a buffered channel, it's the capacity defined at the creation time. dataqsiz : A pointer to an array where the channel's data elements are stored. The size of this array is determined by . buf dataqsiz : The size, in bytes, of each element in the channel. This ensures that the memory allocated for the channel's buffer is properly managed according to the type of data the channel is meant to hold. elemsize : A flag indicating whether the channel has been closed. Once a channel is closed, no more data can be sent on it, although data can still be received until the buffer is empty. closed : Points to a data structure that describes the type of elements the channel can hold. This is critical for maintaining type safety in Go's statically typed system. elemtype and : These indices manage the positions where the next send and receive operations will occur, respectively, enabling the circular queue functionality. sendx recvx and : Wait queues for goroutines that are blocked on receiving from or sending to the channel, respectively. These queues are implemented as linked lists. recvq sendq : A mutex lock to synchronize access to the channel, preventing race conditions when multiple goroutines interact with the channel concurrently. lock When a channel is created using the function, Go allocates memory for and initializes an instance of the struct. This process involves setting up the struct fields to their default values or the specified capacity for buffered channels. make hchan For example: ch := make(chan int, 10) This line creates a buffered channel of integers with a capacity of 10. We will delve deeper into the nuances of channel types and capacities in the upcoming sections of this article. Internally, Go does the following: Allocates a struct on the heap. hchan Sets to 10, reflecting the channel's capacity. dataqsiz Allocates an array of 10 integers ( will describe an integer type, and will be the size of an int on the current architecture) and assigns its address to buf. elemtype elemsize Initializes , , and to 0, indicating an empty channel. qcount sendx recvx Sets to 0, indicating that the channel is open. closed The initialization process ensures that the channel is ready for use, with a clear and safe protocol for sending and receiving data. This is crucial here. It's used to synchronize access to the channel, ensuring that concurrent operations are safe and that the channel's state remains consistent. lock Understanding the struct and the initialization process provides insight into how Go channels efficiently manage data exchange and synchronization between goroutines. This intricate design allows developers to leverage channels for robust concurrent programming without delving into the complexities of traditional thread synchronization mechanisms. hchan Operations on channel Once a channel is declared, it can be used for sending and receiving data. These operations are at the heart of channel based communication in Go. To send data to a channel, you use the channel variable followed by the send operator, and the value to be sent. The syntax looks like this: <- chanVar <- value In this example, a string is sent to the messages channel from within a new goroutine. The main function continues its execution without waiting for the send operation to complete, illustrating the non-blocking nature of send operations in buffered channels or when the receive is ready in unbuffered channels. Receiving data from a channel is done by placing the channel variable on the right side of the receive operator, . This operation is blocked by default. It waits until there's data to be received. The syntax for receiving data from a channel is: <- value := <-chanVar In this code snippet, the main function blocks at the receive operation until the goroutine sends a string to the channel. Once the message is received, it's stored in the variable and then printed to the console. messages msg Channel direction In Go, channels can be directional. This means a channel can be specified to only send or only receive values. Directional channels enhance type safety by ensuring a channel is used only for its intended purpose, whether it's to send data, receive data, or both. This concept is particularly useful in function signatures, where you want to enforce the role of a channel within the context of goroutine communication. A send-only channel can only be used to send data to a channel. Attempts to receive data from a send-only channel will result in a compilation error, ensuring that the channel's directionality is respected. The syntax for declaring a send-only channel is as follows: chanVar := make(chan<- Type) In this example, the function takes a send-only channel as an argument ( ) and sends a string into this channel. The main goroutine receives the string from and prints it. The function cannot receive data from , as it's a send-only channel, showcasing the enforcement of channel directionality. sendData sendCh chan<- string messageChannel sendData sendCh Conversely, a receive-only channel is used exclusively for receiving data. Sending data to a receive-only channel will result in a compilation error. The syntax for a receive-only channel is: chanVar := make(<-chan Type) Here, the function is designed to accept a receive-only channel ( ) from which it reads a message. The anonymous goroutine in the function sends a string to , which is then received by . This setup ensures that function cannot send data back through , adhering to its receive-only designation. receiveData receiveCh <-chan string main messageChannel receiveData receiveData receiveCh Capacity of a channel The capacity of a channel refers to the number of values that the channel can hold at a time. This is pertinent to buffered channels, which are declared with a specified capacity. The function is used to determine the capacity of a channel. cap() In this example, a buffered channel with a capacity to hold 5 integers is created. Using the function, we print out the capacity of , which is 5. cap() ch Length of a channel While the capacity of a channel is static, the length of a channel is dynamic and represents the number of elements currently queued in the channel. The function is used to find out how many items are currently stored in the channel. len() This code snippet demonstrates a buffered channel where two integers are sent into the channel. The function shows that the length of is 2, indicating two items are currently in the channel. len() ch Close operation on a channel Closing a channel indicates that no more values will be sent on it. This can be useful to signal to the receiving goroutines that the channel has finished sending data. The function is used for this purpose. close() After closing the channel , we attempt to read from it. The second value returned by the channel read operation indicates whether the channel is open or closed. In this example, after reading all the elements, becomes , signaling the channel is closed. ch open false Nil channel A nil channel is a channel with no reference. Both sending and receiving operations on a nil channel block forever, making nil channels useful for disabling a channel operation dynamically. Operations on a nil channel never proceed, making them distinct in their behavior compared to non-nil channels. For range loop on a channel The loop can be used to receive values from a channel until it is closed. This idiomatic way of reading from a channel ensures that all sent values are received. for range In this code, we iterate over a channel using a loop, printing each value received from the channel until it's closed. ch for range Advanced channel techniques Let's consider a scenario where multiple goroutines send data to a channel, and a single receiver processes the data in a synchronized manner. In this example, we spawn goroutines that send unique to the messages channel. The is used to wait for all sender goroutines to finish before closing the channel. The receiver goroutine uses a loop to receive messages until the channel is closed. numSenders messages sync.WaitGroup for range Next example showcases using the select statement to process data from multiple channels and a timeout feature. In the above code, we set up a pool of workers that receive from the jobs channel and send results to the channel. The function sends jobs to the workers and uses a statement to handle results with a mechanism. The timeout channel created with ensures that if results are not received within a certain period, the main program will not wait indefinitely. jobs results main select timeout time.After Both of these examples in this section present more complex use cases of channels in Go, demonstrating how channels can be used for synchronization, timeout handling, and concurrent processing patterns. Summary In wrapping up our exploration of Go channels, we've seen how these powerful tools facilitate communication and synchronization in concurrent programming. The behavior of channels varies depending on their state (open or closed) and type (buffered or unbuffered). To distill our understanding, let's review a summary table showcasing the outcomes of various operations across different channel states: Key Takeaways: : An attempt to send on a closed channel results in a panic, highlighting the need for careful channel management. Conversely, sending on a nil channel illustrates an indefinite block, a scenario typically indicative of a programming error or oversight. Send Operation : The nuanced behavior of receiving from a closed channel returning the default value if empty underscores Go's emphasis on safety and predictability in concurrent execution. Receive Operation : Successfully closing open channels ensures a clean state transition, while attempting to close a nil or already closed channel flags a clear misuse through a panic. Close Operation : These introspective operations provide visibility into the channel's current load and capacity, crucial for performance tuning and debugging. Length and Capacity Go channels offer a nuanced, powerful model for concurrency, balancing simplicity with depth. Understanding their behavior across different states and operations empowers developers to craft efficient, robust, and deadlock-free concurrent applications. As we've seen, mastery of channels is not just about knowing how to use them, but understanding their behavior under various conditions, ensuring our Go applications perform harmoniously even in the face of complex synchronization challenges.

Mastering Go Channels for Elegant Synchronization

As an AI expert, let me be blunt - cyber threats are a massive, unrelenting risk that no business with an online presence can afford to ignore these days. Whether you're running an e-commerce site, logistics operation, or tech firm, phishing, ransomware attacks, or data breaches - these scourges can cripple a company, leading to devastating financial losses, irreparable brand damage, and even regulatory penalties that could bury you. I've personally seen the aftermath of cyber strikes on SMBs with limited security resources and tight budgets. In those cases, traditional cybersecurity just doesn't cut it against today's attack of threats. But that's exactly where artificial intelligence (AI) emerges as a total game-changer. discovered that organizations leveraging AI and automation extensively saved a mind-blowing $1.76 million on average compared to their less AI-savvy counterparts. IBM's 2023 Cost of Data Breach Report And I can testify that AI-powered cyber defense solutions truly earn those savings. By unleashing cutting-edge AI techniques like machine learning, deep learning models, and natural language processing, modern AI cybersecurity acts like an indispensable digital force multiplier reinforcing our defenses against even the wiliest adversaries. As an AI expert, I've deployed these capabilities for clients - it's like having an entire squad of cybersecurity sentries patrolling the digital premises 24/7 to keep the bad actors at bay. Moving forward, I don't believe adopting advanced AI will be a luxury for organizations - it's a flat-out necessity we can't ignore. Whether you're a small business or a giant enterprise, you'd better get proactive and start leveraging AI's offensive cybersecurity potential to safeguard your vital digital assets and ensure continuous operations. In my opinion, AI is quickly separating the cybersecurity haves from the have-nots. AI's Cyber Offense: How It Turbocharges Threat Detection I can't overstate how AI is revolutionizing cybersecurity by unleashing cutting-edge threat detection and response capabilities never before possible. Just look at the rapidly growing market - projects that AI in the cybersecurity sector will explode from $22.4 billion in 2023 to $60.6 billion by 2028. And there are good reasons behind that meteoric growth. MarketsandMarkets Machine learning algorithms excel at quickly identifying patterns and anomalies within massive datasets - making them invaluable assets for recognizing potential cyber threats amid tons of network traffic, user activity logs, and other data. By training these algorithms on historical data, we can have them recognize known attack signatures and deviations signaling new threats before they can wreak havoc. One study I find particularly compelling is from MDPI, showing machine learning models achieved up to 99% malware detection accuracy using techniques like Decision Trees, Convolutional Neural Networks, and Support Vector Machines. That's the level of precision AI can deliver against emerging threats. There are deep learning models that I've deployed to rapidly analyze and classify malware samples with incredible accuracy. The latest deep-learning architectures can see patterns invisible to the naked eye. However, some of the most exciting AI cybersecurity applications my team has tested involve reinforcement learning for adaptive threat response. These self-learning systems can dynamically adjust defensive actions on the fly based on their experiences against evolving attack vectors. Allied Market Research predicts reinforcement learning will see explosive 41.5% annual growth from 2023 to 2032, with cybersecurity as a prime use case. Let's not overlook natural language processing (NLP) for automating incident analysis. I've personally overseen NLP deployments that can rapidly comprehend human language data from reports, intelligence feeds, and more - streamlining our incident response workflows and prioritizing genuine threats over false positives. Combining all these AI disciplines is delivering incredible offensive capabilities to counter even the trickiest cyber adversaries. As an expert and founder in this space, I truly believe AI's ability to autonomously detect and shut down threats before impact will utterly transform cybersecurity over the coming years. Why UEBA and User Behavior Monitoring Matter (And How AI Excels) The cyber risk from malicious insiders or compromised user accounts is one we can't overlook either. That's why I consider user and entity behavior analytics (UEBA) powered by advanced AI/ML models to be paramount for comprehensive, 360-degree security visibility. At a basic level, these AI-driven UEBA solutions leverage machine learning to establish baselines modeling each user's normal behavior based on their activity patterns. Once those baselines are established, the AI can continuously monitor for deviations that may indicate insider threats, privilege abuse, compromised accounts, and other insider risk vectors. What makes this application of AI so powerful is its ability to dynamically score the risk level of unusual user actions based on contextual factors like their role, access privileges, the sensitivity of data/systems involved, and more. This enables real-time flagging of potentially malicious insider activity for further investigation - something that was highly prone to human error with traditional, rigid rule-based approaches. Another area where I've seen UEBA with AI shine is detecting compromised privileged user accounts, which are prime targets for external threat actors seeking a foothold. By understanding the unique behavior patterns of different privileged user roles, AI can rapidly identify the subtle anomalies of a privileged account under hostile control - giving our teams a chance to shut down that breach before catastrophic damage occurs. In my opinion, deploying AI for insider and privileged user threat detection by understanding human behavior is an absolute game-changer. Monitoring our people and accounts is just as vital as external threat monitoring - without it, we're leaving a gaping hole in our defenses that skilled adversaries will inevitably find and exploit. Of course, my teams always emphasize that AI-powered UEBA is just one layer in a comprehensive, defense-in-depth strategy. But it's a crucial layer that lets us get in front of insider incidents before they escalate - buying time that can mean the difference between a contained issue and a full-blown crisis. The Flip Side: Defending Against AI-Powered Threats As amazing as AI's cybersecurity offensive capabilities are, I have to address the flip side too: hackers are actively working on ways to weaponize AI against us. Adversarial attacks designed to bypass, poison, or steal our AI models are a clear and present danger we can't ignore. One disturbing forecast from research analysts indicates: Up to 30% of AI cyberattacks will leverage training data poisoning, model stealing, or adversarial examples to attack AI-powered systems. But we're not defenseless - there are proven ways to harden AI systems against these tactics if we invest in robust AI security controls from the start. Approaches like adversarial training, where models are intentionally exposed to deceptive inputs during the learning process, can improve resilience against evasion. Differential privacy techniques can also obscure model behavior to thwart extraction attacks. We follow best practices around secure AI development Life Cycle, subjecting our models to rigorous adversarial testing and employing defensive AI techniques to stay ahead of emerging threats. I don't mean to sound alarmist, but the cybersecurity industry at large needs to prioritize battling AI-powered threats just as much as leveraging AI for defense. It's a two-way street - we must secure the AI models underpinning our cyber defenses with the same vigor we apply AI offensively. Neglecting AI security WILL be exploited by sophisticated hackers. So while I'm incredibly confident about AI's potential to revolutionize cyber threat detection and response, I'm also pragmatic. We simply can't ignore the dark side and must proactively invest in developing and deploying AI securely to maintain a decisive strategic advantage. Integrating AI Across Security Operations Of course, AI alone is not a silver bullet for cybersecurity. Its true power comes from thoughtful integration across existing processes, teams, and technology stacks. As an enthusiastic AI Expert, I emphasize this force-multiplier effect to clients constantly. AI should augment and empower human expertise, not replace it entirely. That's why the most successful deployments I've seen use AI to offload tedious, repetitive tasks to machines - freeing up skilled personnel to focus on higher-level analysis, investigation, and decision-making that requires human judgment. That's also why AI is rapidly becoming essential for SOC analysts and incident response teams: by automating alert triage, log analysis and basic response workflows, AI drastically reduces burnout from low-value "grunt work" while improving overall efficiency. Research from Capgemini revealed organizations using AI for security operations saw a 12% increase in breach detection rates and an impressive 19% reduction in time-to-respond. Those are major gains I've validated through my own startup's AI deployments. AI and machine learning are also driving huge advances in security orchestration, automation, and response (SOAR) tools that streamline entire incident response processes. With SOAR, AI handles tasks like correlating disparate security signals, surfacing relevant threat intelligence, and even executing semi-automated response playbooks. It's a huge step towards smarter, more autonomous incident handling. Ultimately though, AI is a complement to skilled human teams, not a wholesale replacement. Cybersecurity operations will always require human expertise, analysis, and judgment. But by intelligently augmenting our people with AI automation and assistance, we can concentrate their talents on higher-impact duties while optimizing productivity and efficiency. I already see this human-machine symbiosis enhancing the industry's ability to outpace threats. As AI integration continues maturing, I'm confident embracing this collaborative model is the only way we'll realistically close the cybersecurity skills gap plaguing our field. AI's Cybersecurity Superpowers By rapidly analyzing network data, user activities, system logs, and more, AI can identify malicious behavior patterns and potential threats with superhuman speed - giving us an early warning to shut down attacks before escalation. Threat Detection & Prevention: While phishing lures and malware grow more devious, AI algorithms can precisely distinguish malicious files and emails from legitimate ones by evaluating content, sender metadata, behavioral signals, and other artifacts that often evade human analysis. Phishing and Malware Detection: Rather than rigid rules, AI lets us dynamically model normal user activity baselines. It can then rapidly detect anomalies indicative of insider threats, compromised accounts, data exfiltration attempts, and more - all in real-time. User Behavior Analytics: I've deployed AI to prioritize vulnerabilities based on their risk and exploit potential, automating the triaging process. AI can also correlate vulnerabilities with active threats, showing us which exposures need patching first. Automated Vulnerability Management: Modern AI-powered SIEM solutions can correlate security events across networks, endpoints, and other sources to rapidly identify genuine incidents amidst the noise. AI also streamlines response workflows based on alert contexts. Smart SIEM & Incident Response: Across incident triage, alert analysis, threat hunting, and more, AI is automating repetitive, labor-intensive security tasks - amplifying the impact of human personnel while reducing burnout and fatigue. Security Ops Automation: I could go on, but you get the idea. As an AI expert and entrepreneur, I've seen these capabilities utterly transform cybersecurity operations into an amazingly proactive, autonomous counter-threat force. And AI's offensive cyber capabilities are still rapidly evolving. The Way Forward: Practical AI Cyber Defense But when I gaze into the future of AI-powered cybersecurity, I see incredible potential beyond just better threat detection and response. Used wisely, I believe AI can fundamentally reshape and elevate our entire profession into a truly proactive, predictive security posture - finally outpacing even the wiliest cyber adversaries. However, realizing this AI-driven future won't happen automatically. It requires us as an industry to thoughtfully and securely integrate AI across our people, processes, and technology stacks with a keen focus on augmenting human expertise and capabilities rather than wholesale automation. At an operational level, this will mean embracing increasingly autonomous security workflows offloaded to AI systems - handling tedious triaging and basic response while escalating only the most critical issues to skilled analysts for analysis and key decisions. It's a pragmatic balance of human and machine capabilities. We'll also need a new class of cybersecurity professionals fluent in interpreting AI system outputs and translating them into strategic guidance. These AI-savvy analysts must be adept at not just using but explaining and justifying AI system behaviors and decisions to leadership stakeholders. It plays a crucial role in building organizational trust and accountability around AI systems. Additionally, organizations must adopt secure AI system development lifecycles, proactively battling adversarial AI threats through approaches like robust model training, adversarial testing, privacy-preserving analytics, and defensive AI architectures. We simply can't leave our AI systems vulnerable to adversarial subversion. Thankfully, I see positive momentum towards these goals across the AI security startup ecosystem and cybersecurity community. But we must keep pressing forward holistically - investing in skilling our people, refining our processes, and pioneering new AI-centric technologies securely and ethically. Only then can we responsibly wield AI's incredible cyber defense potential without enabling worse nightmares. Despite all the work ahead, I'm hugely optimistic as an AI expert and founder. I've already deployed these transformative AI capabilities in the field, experiencing their game-changing impact firsthand. AI gives us a truly decisive advantage in combating even the most sophisticated threat actors - if we successfully fuse machine intelligence and human expertise. It won't be easy, but I'm convinced the payoff will be a safer, more secure digital world actively hardening itself against cyber threats in real-time. A world where novel attacks are neutralized before impact, where vulnerabilities are patched before exploitation, and where malicious insiders and compromised accounts are stopped in their tracks. Best of all, AI cyber defense done right should free up our human personnel for higher-impact strategic work, not tedious data grunt work. AI should empower us to be the cybersecurity experts, creative problem-solvers, and strategic leaders we always aspired to be - not menial attack analysts. So to my fellow AI professionals, founders, and innovators - the path ahead is clear. Let's blaze this trail TOGETHER, pioneering the new era of practical, intelligent, and ethical AI-powered cyber defense. The future of digital safety depends on us doing this right. And with our unique combination of human ingenuity and machine intelligence, I'm confident we're up to the immense challenge. Let's get to work.

Applications of Artificial Intelligence in Cybersecurity: Boosting Threat Defense System

AI has radically reshaped predictive analytics, making forecasting more precise than ever before. Businesses in all industries are taking advantage, capitalizing on AI’s wide-ranging capabilities to anticipate future trends and prepare for what’s coming around the corner. The accuracy of any forecast hinges on a number of factors: the sophistication of , the robustness of predictive models, and the quality and quantity of the data they’re trained on, to name a few. AI algorithms is a generative pre-trained model specifically designed for predicting time-series data. It’s a powerful asset that will allow you to forecast future outcomes with greater precision, giving you the foresight to capitalize on upcoming opportunities and dodge possible pitfalls. Nixtla’s TimeGPT Below, we’ll put TimeGPT under the microscope, analyze it from all angles, and show how you can integrate it with your database to maximize the full potential of predictive analytics. What is TimeGPT? TimeGPT is a pre-trained generative model for time-series data forecasting. Its core functionality is to predict future values in a time series based on historical data. The application processes data sequentially, using a windowing technique to analyze historical data points and recognize patterns. This enables it to predict future values by extrapolating these identified patterns. An overview of TimeGPT : Nixtla's TimeGPT applies pre-trained models to time-series data, using the data collected to predict new datasets without specific training, a departure from traditional models. Innovative Pre-training : TimeGPT excels in identifying complex patterns in time series data, surpassing many traditional models in trend, seasonality, and anomaly detection. Advanced Pattern Recognition : By using advanced neural networks, TimeGPT offers efficient forecasting, reducing the time and resources typically needed for time series analysis by eliminating dataset-specific retraining. Efficient Forecasting : Unlike static traditional models, TimeGPT has the potential for ongoing learning and adaptation, maintaining accuracy as new data emerges. Continuous Learning and Adaptation TimeGPT’s features and benefits As one of the leading predictive analytics models, TimeGPT offers advanced capabilities that provide a variety of unique features, making it a powerful asset for managing complex forecasting scenarios. These include: Anomaly Detection Leveraging predictive modeling techniques, TimeGPT excels in identifying unusual patterns or outliers in time series data, which are often indicative of significant but unexpected events. Feature: This feature is crucial for businesses and analysts in quickly detecting and responding to unusual occurrences, ensuring that forecasting models remain accurate and reliable, even in the presence of aberrations. Benefit: Exogenous Variables Handling TimeGPT can incorporate external factors or exogenous variables (like weather conditions for retail sales forecasting) into its analysis. Feature: This ability allows for more nuanced and accurate forecasts by considering the impact of external elements, which is particularly useful in scenarios where external factors significantly influence the data trends. Benefit: Fine-tuning While TimeGPT is a pre-trained model, it offers the capability for fine-tuning to adapt to specific datasets or unique forecasting needs. Feature: This feature enables users to tailor the model more closely to their specific requirements, enhancing the accuracy and relevance of the forecasts in specialized contexts. Benefit: Historical Forecasting TimeGPT can analyze past data to generate historical forecasts, allowing users to understand how the model would have predicted past events. Feature: This is particularly beneficial for validating the model’s accuracy and reliability, giving users confidence in its future predictions. Benefit: Managing complex forecasting scenarios TimeGPT’s features are especially beneficial when handling complex scenarios such as holidays, special dates, and irregular timestamps. These events can significantly impact trends in time series data. TimeGPT’s ability to detect anomalies and incorporate exogenous variables helps in accurately forecasting around these dates, considering their unique impact. Holidays and Special Dates: Time series data can sometimes be irregular or have missing points. TimeGPT’s sophisticated algorithms can handle such irregularities, ensuring consistent and accurate forecasting even with non-uniform data. Irregular Timestamps: Deploying TimeGPT models in MindsDB MindsDB is the platform for building custom AI from enterprise data. It works by connecting any source of data with any AI/ML model or framework and automating how real-time data flows between them. MindsDB bridges data sources with advanced AI/ML models like TimeGPT. This allows you to easily create, train, and manage these models, making it particularly beneficial to those who have minimal experience in machine learning. Integrating TimeGPT into MindsDB begins with creating a TimeGPT ML engine using the below statement and providing the TimeGPT API key. CREATE ML_ENGINE timegpt_engine
FROM timegpt
USING timegpt_api_key = ‘timegpt-api-key’; Next, users can employ the statement to create, train, and deploy the TimeGPT model. CREATE MODEL CREATE MODEL model_table
FROM data_source
  (SELECT * FROM data_table)
PREDICT column_to_be_predicted
ORDER BY date_column
GROUP BY column_name, column_name, ...
HORIZON 3 -- model forecasts the next 3 rows
USING ENGINE = 'timegpt_engine'; Now you can query the model to get forecasts: SELECT m.date_column, m.column_to_be_predicted
FROM data_table AS d
JOIN model_table AS m
WHERE d.date_column > LATEST; With MindsDB, you can automate forecasts and generate them constantly for any downstream usage. It is especially useful when you have dynamic data that gets updated regularly. Then, the model should be fine-tuned with the latest data to provide as accurate forecasts as possible. Here, we create a job that fine-tunes the model with the latest data and saves daily forecasts in a database table. CREATE JOB automate_forecasts (

   FINETUNE model_table
   FROM data_source
      (SELECT * FROM data_table);

   CREATE TABLE daily_forecasts_{{START_DATETIME}} (
      SELECT m.date_column, m.column_to_be_predicted
      FROM data_table AS d
      JOIN model_table AS m
      WHERE d.date_column > LATEST
   )
)
EVERY 1 day; To see a complete example, visit our . You can find more . docs page here forecasting use cases here How to use TimeGPT in predictive analytics Companies must be agile enough to react to minute-by-minute market changes. Using predictive analytics techniques, such as those enabled by TimeGPT, will help keep you a step ahead and even give you a competitive advantage. Businesses can utilize TimeGPT in several key areas, including: Financial Forecasting TimeGPT’s proficiency in processing and interpreting time-based data leads to more precise forecasts in the fluctuating realm of finance. Its ability to identify current trends and subtle nuances within financial datasets helps to create more informed investment strategies. Demand Prediction In industries like retail and manufacturing, TimeGPT aids in predicting product demand. This is crucial for inventory management, supply chain optimization, and strategic planning. By analyzing historical sales data and market trends, TimeGPT can forecast future demand patterns, helping businesses to prepare and adapt effectively. Anomaly Detection The model's approach to sequential learning proves advantageous in detecting anomalies, like spotting unusual patterns in network traffic for cybersecurity or identifying machine performance irregularities for predictive maintenance. This function is crucial in averting potential problems before they intensify. Predictive analytics use cases Here are just a few ways to use TimeGPT for accurate predictive analytics. TimeGPT performs well compared to traditional methods in stock price prediction, aiding investors and analysts. Stock Market Analysis: : It predicts healthcare trends, which is important for resource allocation and planning. Healthcare Trend Anticipation TimeGPT predicts future demands in logistics, improving efficiency and reducing costs. Supply Chain Optimization: It forecasts retail buying trends, aiding in inventory management and targeted marketing. Retail Customer Behavior Analysis: TimeGPT identifies potential equipment failures, minimizing downtime and maintenance costs. Manufacturing Predictive Maintenance: TimeGPT in the real world Global business is more complex and interconnected than ever. One false move in a single industry can have a ripple effect in several others, making it crucial for companies to leverage predictive analytics to foresee, prepare, and react to future events. With its advanced time-series analysis, deep learning capabilities, adaptive learning, and customizable scalable models, TimeGPT has proven to be an effective tool for more accurate predictive analysis. Integrating TimeGPT with MindsDB creates a powerful combination that enhances the precision and applicability of predictive insights directly within business databases. This gives you the agility to anticipate and react swiftly to a rapidly evolving global market. How would TimeGPT perform on your company’s data? Check out the . tutorial here Also published . here

How TimeGPT Transforms Predictive Analytics with AI

টাইমজিপিটি কীভাবে AI এর সাথে ভবিষ্যদ্বাণীমূলক বিশ্লেষণকে রূপান্তরিত করে

Wie TimeGPT Predictive Analytics mit KI transformiert

Cómo TimeGPT transforma el análisis predictivo con IA

Comment TimeGPT transforme l'analyse prédictive avec l'IA

TimeGPT कैसे AI के साथ पूर्वानुमानात्मक विश्लेषण को बदलता है

TimeGPT が AI を使用して予測分析をどのように変革するか

TimeGPT가 AI를 통해 예측 분석을 혁신하는 방법

Como o TimeGPT transforma a análise preditiva com IA

Как TimeGPT преобразует прогнозную аналитику с помощью ИИ

TimeGPT, Yapay Zeka ile Tahmine Dayalı Analitiği Nasıl Dönüştürüyor?

Cách TimeGPT biến đổi phân tích dự đoán bằng AI

TimeGPT 如何利用 AI 改变预测分析

**AUSTRALIA, South Australia, March 8th, 2024/Chainwire/--**Bavarsis: Pioneering Innovation in the Cryptocurrency Market. March 2024, Australia – In the dynamic landscape of investment, Bavarsis emerges as a beacon of innovation and opportunity, offering a realm of innovation and potential that transcends mere user-friendliness. Leveraging the capabilities of Artificial Intelligence (AI) and trading bots, Bavarsis facilitates rapid arbitrage trading, presenting a versatile framework, diverse cryptocurrency options, transparency, and a steadfast dedication to enduring viability. Delving into the core of Bavarsis unveils the myriad reasons why it stands as a premier option for contemporary investors. Bavarsis, a leading player in the cryptocurrency market, proudly announces remarkable achievements in its recent financial report. The company boasts a substantial increase in its crypto-assets by 4.1% over the past month, indicative of robust growth and effective investment strategies.Furthermore, Bavarsis reports an impressive 24% growth on its assets, attributed to the dynamic nature of the cryptocurrency market. These results underscore the company's sound investment decisions and its adeptness at capitalizing on market trends effectively. "We are delighted to share these positive outcomes with our partners and clients," stated the Bavarsis team. "Our commitment to strategic investment and prudent decision-making has not only strengthened our position in the market but has also yielded significant benefits for our valued clients." In a testament to its commitment to expansion and global presence, Bavarsis inaugurated several new offices in key regions including Russia, Taiwan, China, and Europe. This strategic move underscores the company's intent to establish a strong foothold in emerging markets and enhance accessibility to clients worldwide. Moreover, Bavarsis marks a significant milestone with the launch of its new exchange platform, aptly named Bavarsis Exchange. The launch of this exchange reflects the company's dedication to innovation and providing cutting-edge solutions to its clientele. "We are thrilled to announce the launch of Bavarsis Exchange, a platform designed to meet the evolving needs of cryptocurrency traders and investors," remarked the Bavarsis team. "With advanced features and a user-friendly interface, Bavarsis Exchange aims to offer a seamless trading experience to our users." As Bavarsis continues to expand its presence and explore new opportunities in the cryptocurrency space, the company remains steadfast in its commitment to fostering long-term partnerships and delivering value to its stakeholders. In addition to its impressive achievements and expansion initiatives, Bavarsis operates under the jurisdiction of two key regions, further solidifying its strategic positioning and growth prospects. The company is registered in two jurisdictions, namely Hong Kong and Australia, each offering distinct advantages for its operations. Hong Kong, renowned for its business-friendly environment and regulatory framework, provides an ideal setting for setting up and scaling an exchange platform like Bavarsis Exchange. Leveraging the robust infrastructure and regulatory support in Hong Kong, Bavarsis aims to expand its exchange operations and cater to a global clientele seamlessly. On the other hand, Australia emerges as an ideal jurisdiction for Bavarsis' arbitrage activities, primarily due to its favorable tax policies. With low taxes on arbitrage activities, Australia presents lucrative opportunities for Bavarsis to maximize its profitability and optimize its investment strategies effectively. By capitalizing on the advantageous tax environment in Australia, Bavarsis can enhance its competitiveness and solidify its position as a leader in the cryptocurrency market. For more information about Bavarsis and its latest developments, visit their official website at bavarsis.com Contact Bavarsis exchange conquers new heights Bill Bavarsis Office@bavarsis.com +61 451 602 190 This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here.

BAVARSIS EXCHANGE Conquers New Heights

How do you choose the most appropriate model for your application? An analysis of evaluating and building applications on open-source large language models. The computational complexity of AI models is growing exponentially, while the compute capability provided by hardware is growing linearly. Therefore, there is a growing gap between those two numbers, which can be seen as a supply and demand problem. On the demand side, we have everyone wanting to train or deploy an AI model. On the supply side, we have Nvidia and a number of competitors. Currently, the supply side is seeing earnings skyrocket, and the demand side is stockpiling and vying for access to compute. It’s a simple formulation but one that works well to explain the current situation in AI. If anything, the generative AI explosion in the last couple of years has validated this formulation. How can this supply vs. demand imbalance be resolved then? One way would be to introduce , and some people are working on that. If you are not one of them, the other option would be to work on the software side of things. That is, to try and come up with ways to make AI model training and inference less computationally intensive. more AI hardware with more capabilities This is what CEO and co-founder Yonatan Geifman has been working on since 2019. Deci is focused on building AI models that are more efficient and accurate. Initially, , later adding a range of language models to its portfolio. Deci Deci released computer vision models Interestingly, most of these models are open source. We caught up with Geifman to explore the landscape of open-source AI, aiming to address some key questions. Do the benefits of open source and the criteria to consider in the classical build vs. buy dilemma apply to AI large language models (LLMs)? And how can LLMs be evaluated and tailored to the specific needs organizations have for their applications? https://youtu.be/1GD9J84Bw34?embedable=true Open source Large Language Models are different To begin with, let’s examine what “open source” means in the context of LLMs. Open-source software has been around for a long time and is sufficiently well understood. , and different licensing schemes have their peculiarities. Open source has gone through some turbulence Still, in software, things are relatively straightforward. It’s all about the source code and its availability. Having the source code and the right dependencies and environment means being able to build and deploy software. But what about AI models? Yes, we also have the source code. However, there are additional artifacts on the way from source code to deployment. We have the dataset used to train the model. We have the training process and . Some model releases also include metadata such as metrics and . Can the same open-source definition apply? weights for the source code model cards Geifman believes that the most important artifacts are the source code and the weights, as having those shared enables people to use and build on top of other people’s work and collaborate around those derivatives that are open source. Metadata is nice to have but not essential in his view. But there’s also the crucial issue of data and training process. Deci, like Mistral, is not sharing data or details about the process of training their open-source models. Geifman noted that there are trade secrets around those. To get a model that works well, it’s not just about having the right data but also about using the right parts of the data in the right way, he added. Geifman thinks that sharing the data and the training process is significantly less important than sharing the model itself. By contrast, that “trained on the same dataset for long enough, pretty much every model with enough weights and training time converges to the same point.” OpenAI’s James Betker claims What probably everyone will agree to is the fact that open-source software and open-source models are not exactly the same thing. As well as the fact that if open sourcing of model architectures and software had not happened, AI would not be at the point where it is today. Either way, there is a whole spectrum from having everything open to having everything closed, and most models fall somewhere in between. Geifman noted that there is a tendency in the community to use the term models instead of open-source models. open weights Open source Large Language Models vs. GPT-4 and Claude Open weights or open source, however, what’s probably more important is how these models can be used by organizations. This is where things look more like traditional software. Organizations have many choices. They can use open-source models hosted in their own infrastructure and in some third-party providers' infrastructure, or they can use hosted, closed-source models such as GPT-4 or Claude 3 via an API. What Geifman sees is that many organizations embark on their AI journey by leveraging closed-source model APIs. He thinks the reason is it’s easier to get started this way, as there is less investment in time and expertise. However, over time, that may not be the more sensible approach. Prices may fluctuate, but as closed-source APIs come with a premium, costs will start adding up. But this may not even be the most serious drawback. Closed source models are black boxes controlled by a third party, and there are implications to this. When things go well, using a thing party model may mean lowering the bar to entry and having peace of mind. But the flip side is that if the model is compromised, goes down, or suffers from a bug or degradation in performance, applications that use it will suffer the consequences. Similar effects will also occur even when updating models, as their behavior may change inconsistently. Prompt engineering is brittle enough as it is, so adding this parameter makes building applications on top of closed-source LLMs an exercise in non-determinism. There are also privacy and security concerns to be aware of, as sensitive data may be exposed. This is the reason why some organizations are not only refraining from using closed-source models but outright banning their employees from using them, too. Large Language Model customization: Fine-tuning vs. RAG Most of the above is not specific to AI models or LLMs. What is specific, and more often than not required for real-world applications, is customization. Whether it’s via , LLMs need to be tailored to users' needs and datasets. fine-tuning or RAG Even though fine-tuning and RAG aim to achieve similar goals, the way they work is quite different, as Geifman pointed out. Fine-tuning changes the actual model for a specific data set, while RAG focuses on injecting data into the generation phase. Geifman finds that with RAG, there is not a lot of difference if you’re looking to apply it to an open-source or a closed-source model. Fine-tuning, on the other hand, will be much easier to do for an open-source model. In the debate of fine-tuning versus RAG, Geifman thinks the future is a combination of both. “There are some benefits in using RAG, mostly on data privacy. You don’t want to train the model on private data, but in RAG you can supplement the model with private data in the prompt. That’s one of the benefits of using RAG, and also the fact that you don’t need to fine tune the models all the time in order to have updated data”, Geifman said. It’s worth mentioning that both fine-tuning and RAG are constantly evolving, with techniques such as and , respectively. Overall, however, Geifman thinks that building more complex use cases will be much easier to do in open source versus closed source in the future, especially when the accuracy gap is bridged. LoRA+ Corrective RAG The reference to accuracy brings us to another multi-variant equation, namely, how to evaluate LLM performance. At the moment, as Geifman mentioned, it seems that closed-source models such as OpenAI’s GPT-4 or Anthropic’s recently released Claude 3 have an edge in terms of accuracy over open-source models. But there is a lot of nuance here. Evaluating Large Language Models Geifman predicted that the gap will be closed by the end of 2024. However, as has recently been demonstrated, carefully fine-tuned models specializing in specific tasks can outperform larger models for these tasks. LoRA Land, a collection of 25 fine-tuned open-source Mistral-7B models, was shown to consistently outperform base models by 70% and GPT-4 by 4-15%, depending on the task. What’s more important, however, is evaluating performance holistically. Typically, people tend to equate LLM performance with accuracy. While accuracy is an important metric, there are many other parameters that need to be considered as well. These all revolve around the nature of the application for which the LLM will be used. Even a typical use case such as document summarization, for example, has different requirements depending on the type of document and what the summary is used for. Latency and throughput are also very important, again depending on the application requirements. Some applications may be more tolerant than others. And then there’s , obviously. Even though this is not a metric per se, it’s something that needs to be factored in as well. cost Geifman’s recommendations? First, if you need speed, you’d better work with smaller, fine-tuned, tailored models for your applications. Second, for most B2B applications, you don’t need GPT-4-level accuracy. If you’re trying to summarize documents, you can do it with . mid-sized language models with good performance, lower latency, and better value for money As to how to evaluate models holistically, that’s still an unsolved problem. The two main alternatives at the moment are human evaluation and using an LLM to evaluate another LLM. Both have pros and cons and as usual, there are lots of nuances and blind spots. You might be tempted to note that it’s all fine and well, but not everyone needs to be familiar with all the nuances of LLM evaluation. There is always the , which has become the de facto evaluation ground for all LLMs. Just like any other benchmark, however, these metrics are only indicative. Hugging Face leaderboard The bottom line is that if you want to assess whether a model is cut out for the task at hand, you can stand on the shoulders of giants, but you’ll have to put in the work yourself. And that means formulating your requirements, developing realistic evaluation scenarios, and putting LLMs to the test. Market outlook In the end, the fact that best practices and principles from other areas also apply at least partially to LLMs is probably good news for organizations looking to incorporate LLMs in their applications. As Geifman noted, the excitement from LLMs and their capabilities and the potential of integrating them in enterprises across many verticals has energized the market. Geifman shared that Deci’s AI platform has seen its market more than triple in 2023. Deci’s market is divided into two segments, each with its own characteristics. One of them is computer vision, which is more mature. People know what they need, and they are looking towards production use cases with multiple use cases per company and team, as Geifman put it. Then, there is the GenAI segment, in which people are more in an exploratory phase. Geifman believes 2024 will be the year we’ll see many GenAI-based projects going into production. He sees document summarization and customer care as the dominant use cases. https://pod.co/orchestrate-all-the-things-podcast-connecting-the-dots-with-george-anadiotis/evaluating-and-building-applications-on-open-source-large-language-models-featuring-deci-ceo-co-founder-yonatan-geifman?embedable=true Deci addresses both computer vision and GenAI with open-source models. includes SuperGradients, a library for training PyTorch-based computer vision models, DeciLM 6B and DeciLM 7B, which Geifman noted offers similar accuracy to Mistral 7B with better efficiency and performance, the DeciCoder 1B and DeciCoder 6B code generation models as well as the DeciDiffusion text-to-image model. Deci’s offering These models are free to use, with additional tools to customize, fine-tune, and deploy them. Deci’s deployment environment for LLMs is called Infery-LLM. It’s an open-core approach in which the models are open source, and the value-add tools are commercial. To showcase the value that Infery-LLM brings, Geifman shared that running DeciLM 7B with and without Infery-LLM gets 4,5x vs. 2x the performance of Mistral 7B, respectively. Deci is also working on releasing more LLMs with more parameters and better performance. Also published . here

Open Source LLMs: Evaluating and Building Applications on Open Source

The Results of Our Research: Our Exoplanet Study

In the ever-evolving landscape of digital security, 2023 has been a year marked by an alarming increase in the sophistication and frequency of cyber attacks. As organizations across various sectors grapple with the challenges posed by these threats, the financial implications of cyber breaches have become a focal point of concern. With the global average cost of a data breach reaching a $4.45 million, the economic impact of these incidents cannot be overstated. This figure serves as a baseline for understanding the broader financial damage inflicted across industries, highlighting an urgent need for enhanced cybersecurity measures. staggering Particularly vulnerable sectors such as and healthcare have experienced even higher average costs, at $5.97 million and $6 million respectively, underscoring the targeted nature of cyber threats and the critical importance of safeguarding sensitive information. financial services approximately The education and energy sectors are behind, with average breach costs of $3.86 million and $4.72 million, illustrating the widespread vulnerability across the board. An assumed average for IT services, based on sector vulnerabilities and attack frequencies, is estimated at around $5 million, further emphasizing the pervasive risk of cyber incidents. not far These data points not only shed light on the financial toll of cyber breaches but also serve as a clarion call for organizations to reassess and fortify their digital defenses. As we delve deeper into the specifics of these breaches, including the sectors most affected and the types of data compromised, the imperative for a robust and proactive cybersecurity strategy becomes increasingly evident. This article aims to explore the data-driven problem statement surrounding cyber security breaches in 2023, offering insights into the scale of the challenge and the critical steps needed to navigate this digital minefield effectively. The Frontline Defenders Information Security Teams, Fraud and Risk Officers, and Online Business Teams are on the cybersecurity frontlines. These professionals confront a complex array of challenges that not only threaten the integrity of organizational data but also the trust and safety of their customers. Below, we delve into the specifics of these challenges, dissecting the mechanisms of attack and outlining strategies for defense. Phishing Emails: The Evolving Threat of Cyber Deception Phishing attacks have become a sophisticated and pervasive threat in the cyber landscape, aiming to manipulate recipients into compromising their sensitive information or inadvertently facilitating the installation of malicious software. These deceptive emails are cunningly crafted to appear as if sent from trustworthy sources, using social engineering techniques to prey on human vulnerabilities. Phishing communications often harbor dangerous links or attachments which, upon interaction, deploy code that infects the victim's system with malware. The range of malware distributed through phishing includes, but is not limited to, ransomware, which encrypts user data demanding ransom for access, and spyware, which covertly collects and transmits user information to the attacker. Defense Strategies : Enhanced education programs to empower employees with the knowledge to identify and neutralize phishing threats. Comprehensive Training : Deployment of sophisticated email security technologies to filter out phishing emails efficiently. Advanced Email Security : Adoption of a fortified security posture, integrating endpoint detection and response (EDR) tools for proactive threat identification and resolution. Robust Security Infrastructure Website Spoofing: A Growing Concern The creation of counterfeit websites that closely mimic legitimate ones is an increasingly common tactic used by cybercriminals. These fake sites deceive individuals into believing they are engaging with reputable entities, leading to the unauthorized capture of personal and financial details. According to , a new phishing site emerges every 11 seconds in 2023, highlighting the escalating challenge of website spoofing. These fraudulent sites often remain active for extended periods, significantly increasing the risk of widespread information theft. recent research Defense Strategies : Leveraging cutting-edge technologies for the immediate detection of spoofed sites and swift issuance of takedown notices. Dynamic Monitoring : Strengthening public awareness about verifying website authenticity, emphasizing the importance of secure connections (HTTPS). User Education : Implementation of Two-Factor Authentication (2FA) to safeguard access to sensitive accounts, providing an additional layer of security. Enhanced Authentication Credential Stuffing: Automated Access Threat Credential stuffing attacks utilize stolen login credentials to gain unauthorized access to multiple accounts across platforms. These automated assaults exploit the prevalence of password reuse, with attackers employing bots to systematically test stolen credentials on various websites. Defense Strategies : Deployment of intelligent systems capable of detecting and alerting on anomalous login behaviors indicative of credential stuffing. Unusual Activity Alerts : Promoting the use of unique, complex passwords and educating users on maintaining strong password hygiene. Password Management Policies : Mandating MFA to significantly diminish the likelihood of unauthorized account access, safeguarding user identities even when credentials are compromised. Multi-Factor Authentication (MFA) Memcyco: A Paradigm Shift in Cyber Defense introduces a groundbreaking approach to cyber defense, offering comprehensive protection against phishing, website spoofing, credential stuffing, and other cyber threats. By embedding a real-time active tracking sensor on legitimate sites, Memcyco delivers unparalleled attack visibility, ensuring robust protection for organizations and their clientele. Memcyco Securing the Digital Frontier: Introducing Memcyco's Cybersecurity Innovations The proliferation of website spoofing and phishing attacks not only poses a significant risk to sensitive data but also erodes the trust that companies work tirelessly to build with their customers. Amidst this backdrop, Memcyco emerges as a beacon of innovation and reliability, offering a robust suite of cybersecurity solutions designed to fortify digital defenses and enhance user confidence. As we delve into the features and safety measures provided by Memcyco, it's clear that their approach is not just about defense; it's about establishing a new paradigm in digital trust and security. The following table offers a comprehensive analysis of Memcyco's offerings, showcasing how their unique solutions stand to benefit businesses and their clientele alike, ensuring a safer digital future for all. Feature/Measure Description Benefit to Companies Benefit to Customers Implementation Ease Real-Time Detection of Fake Sites Memcyco uses an active tracking sensor embedded in the company’s authentic site to detect fake sites in real-time. Maximizes attack visibility and offers full protection, reducing the risk of data breaches and financial losses. Reduces the likelihood of falling victim to scams, enhancing personal data security. Agentless solution; fast and simple implementation. Watermark for Website Authenticity Utilizes a watermark to confirm the authenticity of a website, making it easier to distinguish from fake sites. Protects brand reputation by ensuring the company’s website is easily identifiable as genuine. Increases confidence in the authenticity of the site they are visiting, reducing fear of scams. Fast, requiring minimal changes to the existing website setup. Real-Time Red Alerts Issues immediate alerts to users and companies when a fake site is detected or when there is an attempt to visit one. Enables rapid response to potential threats, decreasing the impact of attacks and workload on security teams. Warns users about potential scams in real-time, preventing credential theft and financial loss. Simple to integrate, providing immediate benefits without complex setup. Forensics on Attacks Provides detailed information on the attack, the attacker, and the targeted victims. Assists in understanding attack vectors, improving defense strategies, and regulatory compliance. Not directly applicable, but contributes to overall security improvements and trust in the company. Requires integration with the company’s security infrastructure but offers significant long-term value. Attack Mitigation Techniques Implements various innovative techniques to stop attackers and take down fake sites. Reduces financial losses due to customer reimbursement and lowers the operational burden on SoC, fraud, and customer care teams. Minimizes the chance of encountering scams, enhancing trust and safety in online interactions. Involves coordination with legal and cybersecurity teams but is streamlined through Memcyco’s services. Customer and Brand Protection Through its end-to-end solution, Memcyco ensures the security of both enterprises and their customers against spoofing and brandjacking. Lowers cost for the company by reducing customer reimbursements and improving operational efficiency. Enhances digital trust, reducing churn and fear of scams, encouraging safer online interactions. Designed for easy integration with existing systems, supporting a wide range of industries. Beyond Defense: Memcyco's Commitment to Digital Trust and Security Navigating through the complexities of cyber threats requires more than just reactive measures; it necessitates a proactive and comprehensive strategy that anticipates potential vulnerabilities and acts swiftly to neutralize them. Memcyco's suite of solutions embodies this ethos, providing businesses with the tools they need to stay ahead in the digital arms race. The analysis above highlights the depth and breadth of Memcyco's capabilities, underlining its role as a pivotal ally for companies aiming to protect their digital domains and maintain the trust of their customers. In embracing Memcyco's innovative approach to cybersecurity, businesses can not only safeguard their assets but also reinforce their commitment to digital integrity and trustworthiness. As we forge ahead in the digital age, Memcyco stands as a testament to what can be achieved when cutting-edge technology meets strategic foresight, ensuring that businesses and their customers can navigate the digital realm with confidence and security. Innovative Protection Measures : Utilization of watermarks to confirm the genuineness of websites, mitigating the risk of spoofing. Website Authenticity : Instantaneous Red Alerts to inform users and organizations about potential spoofing attempts, enabling prompt counteraction. Real-Time Alerts : Agile identification and neutralization of fake sites, minimizing exposure and impact. Takedown Services Customer-Centric Benefits : Diminished success rate of scams, lowering the risk of account takeover (ATO) and enhancing customer security. Reduced Attack Impact : Strengthening digital confidence, encouraging secure interactions and transactions. Enhanced Trust and Engagement : Assisting organizations in adhering to existing and forthcoming regulations regarding online fraud and customer protection. Regulatory Compliance Support Tailored Cybersecurity Solutions Across Industries Strategic Defense in High-Stakes Sectors 's cybersecurity framework is meticulously designed to serve the unique needs of high-transaction, high-stakes sectors, including Financial Services, e-Commerce, Tourism, Logistics, Charity, and Education. These industries, characterized by their substantial digital footprints and the critical nature of their transactions, are often the prime targets for sophisticated cyber threats such as website spoofing and brandjacking. Memcyco The complexity and diversity of these sectors demand a cybersecurity approach that is both versatile and robust, capable of addressing the specific challenges and regulatory landscapes inherent to each field. Financial Services and e-Commerce, for instance, operate under stringent regulatory standards due to the sensitive nature of financial transactions and personal data they handle. The potential for significant financial loss and reputational damage from a successful spoofing attack in these sectors is substantial. Similarly, the Tourism, Logistics, Charity, and Education sectors, while varied in their operations, share a common vulnerability to the erosive effects of cyber threats on consumer trust and operational integrity. 's strategic deployment of sector-specific cybersecurity solutions leverages deep industry insights to mitigate these risks. By offering tailored protection mechanisms that align with the regulatory, operational, and reputational priorities of each sector, Memcyco ensures that its clients are not only defended against current threats but are also prepared for emerging challenges. Memcyco Elevating Digital Trust Through Advanced Security At the heart of 's mission lies the commitment to redefining digital trust. This ambition transcends the mere provision of cybersecurity solutions to address the broader implications of cyber threats on the digital ecosystem. Memcyco understands that in today’s interconnected world, the impact of website spoofing and brandjacking extends beyond individual organizations, threatening the very foundation of digital trust that supports the global economy. Memcyco s agentless, real-time protection mechanism is a cornerstone of this mission. By offering immediate detection and response capabilities, Memcyco not only narrows the critical "window of exposure" during which organizations and their customers are most vulnerable but also provides a proactive defense that adapts to the evolving cyber threat landscape. This approach not only protects the tangible assets of an organization but also safeguards its intangible assets — trust and reputation — which are increasingly recognized as critical to long-term success in the digital age. Memcyco' Moreover, Memcyco positions itself as a partner in the creation of a secure digital ecosystem. By championing authenticity, security, and trust, Memcyco not only defends against the immediate threats of website spoofing and brandjacking but also contributes to the establishment of a more resilient digital environment. This commitment is manifested in the comprehensive nature of Memcyco's solutions, which encompass not just the technical aspects of cybersecurity but also the strategic, regulatory, and educational dimensions necessary for holistic protection. A Strategic Ally in Cyber Defense The year 2023 has underscored this transformation, revealing a landscape fraught with sophisticated cyber threats that not only challenge our technological defenses but also test the resilience of our digital trust and economic stability. The staggering economic repercussions of cyber breaches, with an soaring to $4.45 million, serve not merely as statistical evidence of this challenge but as a clarion call for a paradigmatic shift in how we approach digital security. average global cost As we stand at the precipice of an increasingly digitalized world, the narrative surrounding cybersecurity has shifted from a peripheral concern to a central axis around which the integrity of global business operations pivots. Memcyco's tailored solutions for diverse industries and its overarching mission to elevate digital trust represent a forward-thinking approach to cybersecurity. For cybersecurity experts and corporate leaders alike, Memcyco offers not just a line of defense but a strategic ally in the quest to navigate the complexities of the digital age securely. By integrating cutting-edge technology with deep sectoral insights and a commitment to digital integrity, Memcyco is reshaping the landscape of cybersecurity solutions, making a secure, trusted digital ecosystem not just an aspiration but a tangible reality. 's offerings reveals a multi-faceted approach to cybersecurity, one that transcends traditional defense mechanisms to incorporate a comprehensive ecosystem of real-time detection, advanced authentication, and strategic mitigation. This not only ensures robust protection against a myriad of cyber threats but also enhances the resilience of businesses against the potentially devastating impacts of data breaches, website spoofing, and phishing attacks. Memcyco As we navigate the complexities of the digital age, the role of cybersecurity solutions like Memcyco becomes increasingly critical. Their ability to provide a dynamic and adaptive defense mechanism against an ever-evolving threat landscape is pivotal for businesses striving to maintain operational integrity, regulatory compliance, and, most importantly, customer trust. The emphasis on creating a secure and trusted digital ecosystem reflects a broader commitment to elevating the standards of digital engagement, ensuring that businesses and their customers can interact with confidence and security. Don’t forget to like and share the article!

The $4.45 Million Cyber Catastrophe: Can Your Business Afford the Risk?

$4.45 মিলিয়ন সাইবার বিপর্যয়: আপনার ব্যবসা কি ঝুঁকি বহন করতে পারে?

Die 4,45-Millionen-Dollar-Cyberkatastrophe: Kann sich Ihr Unternehmen das Risiko leisten?

La cibercatástrofe de 4,45 millones de dólares: ¿puede su empresa afrontar el riesgo?

La cybercatastrophe de 4,45 millions de dollars : votre entreprise peut-elle supporter le risque ?

$4.45 मिलियन की साइबर आपदा: क्या आपका व्यवसाय जोखिम वहन कर सकता है?

445 万ドルのサイバー大惨事: あなたのビジネスはリスクに耐えられますか?

445만 달러 규모의 사이버 재앙: 귀하의 비즈니스가 위험을 감당할 수 있습니까?

A catástrofe cibernética de US$ 4,45 milhões: sua empresa pode arcar com o risco?

Киберкатастрофа стоимостью 4,45 миллиона долларов: может ли ваш бизнес позволить себе такой риск?

4,45 Milyon Dolarlık Siber Felaket: İşletmeniz Riski Karşılayabilir mi?

Thảm họa mạng trị giá 4,45 triệu USD: Doanh nghiệp của bạn có thể chấp nhận rủi ro không?

价值 445 万美元的网络灾难：您的企业能承受风险吗？

Authors: (1) Reza Ghaiumy Anaraky, New York University; (2) Byron Lowens; (3) Yao Li; (4) Kaileigh A. Byrne; (5) Marten Risius; (6) Xinru Page; (7) Pamela Wisniewski; (8) Masoumeh Soleimani; (9) Morteza Soltani; (10) Bart Knijnenburg. Table of Links Abstract & Introduction Background Research Framework Methods Results Discussion Limitations and Future Work Conclusion & References Appendix 3 Research Framework In the sections below, we justify information disclosure and privacy concerns as the two outcome variables of interest when examining the influence of dark-pattern designs. We also explain the 2x2x5 experimental design (where the conditions consist of varying defaults, framing, and justifications). Figure 1 shows our hypotheses via our research model. We investigate these hypotheses using a path model. In a path model, variables can function as both dependant variables (DVs) and independent variables (IVs). In our case, privacy concerns is a DV where we study the effects of dark pattern designs on it, and is an IV when we study how it predicts disclosure behaviors. 3.1 Disclosure Behavior Oversharing information on social media can lead to negative consequences for users [65]. Therefore, social media platform users employ a wide range of privacy management strategies to manage their interpersonal boundaries, such as managing their relationship boundaries (e.g., by adding a new friend or unfriending someone) and their territorial boundaries (e.g., by tagging or untagging oneself or someone else in/from photos) [94]. Existing literature regards tagging as a form of disclosure [95], since photo tagging can reveal the tagged person’s online information (e.g. name, social media page) to a broader audience when the photo is shown on friends’ timelines. Tagging other people in ones’ photos is a contentious issue [95]: on the one hand, it can increase group cohesion and build social capital [56, 68], but on the other hand, it can lead to an interpersonal privacy violation if the others prefer not to be tagged [26, 81]. 3.2 Privacy Concerns The privacy literature has not reached a consensus about the relationship between privacy concerns and disclosure behaviors. On the one hand, many studies suggest a negative association between privacy concerns and disclosure behaviors [21, 34, 73]. The general argument in these studies is that a high concern for privacy motivates individuals to refrain from disclosing their data. On the other hand, many studies have found that despite their high privacy concerns, individuals freely give up their personal information—a phenomenon that is so prominent that it has been dubbed the “privacy paradox” [8]. The privacy paradox suggests that privacy concerns have little or no relationship with self-reported or observed disclosure behaviors [57, 79, 83]. For example, Tufekci [87] studied students’ self-reported disclosure behaviors on social network sites. Her results show “little to no relationship” between online privacy concerns and disclosure on online social network sites. Despite these contradictory findings, we pose the following hypothesis reflecting the base expectation that privacy concerns are predictive of disclosure behaviors: H1: Individuals with higher privacy concerns are less likely to disclose. In the following two sub-sections, we build on our RQ1 by posing six hypotheses about the relationships between dark pattern design, disclosure behavior, and privacy concern. We then address our RQ2 by posing two hypotheses regarding the effect of age on disclosure behavior and privacy concerns. 3.3 Behavioral Effects of Dark Pattern Designs: Inducing Disclosure In this section, we discuss the behavioral effects of framing, defaults, and justification messages. Particularly, we draw upon the “privacy dark patterns” literature to hypothesize how these design features are being used to increase disclosure. 3.3.1 Framing and Defaults Framing and default effects have been extensively studied in the privacy domain. Johnson et al. [30] and Lai and Hui [43] independently found framing and default effects to have a significant impact on users’ decisions. In both studies, a positive framing and an opt-out default setting increased the likelihood of users accepting the disclosure requests. In line with these findings, we study two conditions of framing (positive “tag me in the photos” vs. negative “do not tag me in the photos”), and two conditions of the default (opt-out vs. opt-in). We hypothesize the following: H2: A positive framing will increase disclosure. H3: An opt-out default will increase disclosure. 3.3.2 Justification Messages Existing literature suggests that prompting individuals with a message about a product or service can influence their decisions in favor of the message content [27, 39, 45, 92]. Dark pattern designs sometimes use this feature to promote disclosure [12, 54]. For example, they show messages about a product’s popularity in the form of reviews [17] which can sometimes be fake and misleading [80, 84, 91]. Hanson and Putler [27] showed participants a normative justification: an arbitrary integer ostensibly representing the number of downloads of a software program. Participants who saw a higher number were more likely to download the software for themselves. This is arguably due to a herding effect [6], where individuals follow the footsteps of the majority. In addition to such normative justifications, dark pattern designs sometimes use descriptive justification— pushing the benefits of the product—to promote product use or disclosure [12, 54]. For example, in the context of a travel advisor app, designers prompted users with a message of “by signing in, you can download over 300 cities, locations, and reviews to your phone” [12]. Reading about the benefits of the product is arguably a motivating message to sign in and use the app. Having higher quality reviews will increase the likelihood of one buying an online product [39], or talking about the effectiveness (pros) of treatment rather than the cons will increase the likelihood of accepting it [10]. In line with these findings, we study five conditions of justification messages (positive/negative normative, positive/negative rationale-based, none). We pose the following hypothesis: H4: Positive (as opposed to negative) justification messages will increase disclosure. 3.4 Attitudinal Effects of Dark Pattern Designs: Inducing Concerns The effects of framing, defaults, and justification messages on disclosure behavior are evident in the literature [1, 5, 27, 30, 33, 43]. The compliance-inducing effects of such design interventions meet the goal of dark pattern designs. However, research shows that the behavioral effects of these dark pattern strategies may not actually map to their attitudinal effects [7, 38, 41]. In this section, we examine the effects of dark pattern designs on individuals’ state of privacy concerns. Privacy concerns are often measured as an individual trait, rather than a dynamic state [73]. However, this assumption may not be valid. Few studies consider privacy concerns as a dynamic state [67]. We further contribute to the literature by highlighting this perspective and presenting privacy concerns as a dynamic state which can change in response to different design strategies. 3.4.1 Framing and Defaults Online firms have an incentive to collect as much data about their users as they can. Data is an important asset in the industry [13, 29, 78]. For example, having data about customers’ preferences and needs can help firms better target their advertisements to individuals likely to need their product and avoid the unnecessary costs of contacting consumers whose preferences do not match the product [29]. Therefore, some companies adopt dark-pattern design strategies to collect more user data [12]. Framing and default are compliance-inducing mechanisms [4] that are often used in dark pattern designs [25, 53, 54]. Dark pattern default options are designed to encourage sharing of personal information and to maximize online firms’ collected data [12]. Likewise, dark pattern designs use positive framing in the choice statements to endorse disclosure [54]. Whereas the behavioral effect of such dark pattern designs are outlined in Section 3.3, here we note how these interventions can adversely influence users’ attitudes towards the system [12, 47]. For instance, Knijnenburg and Kobsa [41] found that an opt-out default would increase perceived oversharing threats compared to an opt-in default. Therefore, we pose the following hypothesis: H5: A positive framing will increase privacy concerns. H6: An opt-out default will increase privacy concerns. 3.4.2 Justification Messages In the context of dark-pattern design, we argue that while a positive justification message might be intended to encourage individuals to use an app or increase disclosure, it could also increase users’ privacy concerns, especially when they realize that the justification message is used as a dark-pattern strategy. Conversely, users may find a negative justification (i.e., cautioning them about the cons of the product) a sincere message [38] that is indicative of the developer’s benevolence and/or integrity (two of the primary components of trust [55]). For example, communicating potential risks in a study report will increase its perceived trustworthiness [72]. Since trust and privacy concerns are inter-related [19], we argue that a negative justification will result in lower privacy concerns: : H7 Negative (as opposed to positive) justification messages will decrease privacy concerns. Next, we explain the effects relating to age and our second research question. 3.5 Examining Differences between Older Adults and Young Adults Literature suggests a positive association between age and privacy concerns, indicating higher concerns for older users [90]. However, the literature shows mixed findings in terms of the main effect of age on disclosure behaviors. While some studies do not report a significant relationship between age and disclosure [35], other studies show higher disclosure rates for older adults [71]. Despite these mixed findings, we pose the following hypotheses to investigate these effect: H8: Older adults will have higher levels of privacy concerns than young adults. H9: Older adults will disclose more information than young adults. 3.5 Examining Differences between Older Adults and Young Adults Literature suggests a positive association between age and privacy concerns, indicating higher concerns for older users [90]. However, the literature shows mixed findings in terms of the main effect of age on disclosure behaviors. While some studies do not report a significant relationship between age and disclosure [35], other studies show higher disclosure rates for older adults [71]. Despite these mixed findings, we pose the following hypotheses to investigate these effect: H8: Older adults will have higher levels of privacy concerns than young adults. H9: Older adults will disclose more information than young adults. To the best of our knowledge, we are the first to study the difference between older and younger adults responses to the dark pattern designs. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

Unveiling the Impact of Dark Pattern Designs on Privacy Concerns and Disclosure Behavior

Just like the Chiefs clinched victory in the 2024 Super Bowl, certain brands also emerged as winners in the fiercely competitive arena of Super Bowl advertising. Leveraging 's advanced analysis the last three years of Super Bowl ads were dissected providing a data-driven glimpse into what captivates audiences and drives conversion rates on social media platforms during one of the most watched events in the world. Alison.AI (In case you didn’t see the best ads during the Big Game, this is a .) great recap on CNBC Key Insights for Winning Ad Strategies Contrary to the bombastic visuals often associated with Super Bowl ads, minimalistic visuals have proven more effective, showing a 35% increase in conversion rates. This trend suggests that simplicity in design can cut through the noise, capturing audience attention more efficiently. Minimalistic Imagery: Ads that employ dynamic camera movements and unconventional angles have seen a 48% increase in conversion rates. This approach adds a cinematic quality to ads, elevating the narrative and engaging viewers through visual storytelling. Dynamic Cinematography: Non-linear narratives and user-driven storylines have significantly impacted conversion rates, with increases of 52% and 61%, respectively. By offering interactive elements or unexpected plot twists, brands can keep viewers engaged from start to finish, enhancing brand recall. Innovative Storytelling: The introduction of iconic mascots or the inclusion of celebrity cameos has led to substantial increases in conversion rates, 40% and 55% respectively. Memorable characters can forge a stronger connection with the audience, making the ad—and by extension, the brand—more memorable. Memorable Characters: Leveraging spatial audio technology or commissioning custom soundtracks can result in a 44% and 37% increase in conversion rates, respectively. Sound plays a crucial role in ads, enhancing the emotional impact and aiding in brand recall. Immersive Audio Experiences: Ads that incorporate UGC, such as fan tributes or content from challenges and contests, have seen significant increases in conversion rates, 58% and 63% respectively. This strategy adds authenticity to campaigns, resonating well with audiences. User-Generated Content (UGC): The insights provided by Alison.AI are not just reflective of past successes but are indicative of a shift towards more strategic, evidence-based advertising. The AI-driven analysis of over a billion data points from platforms like Facebook, Instagram, and Twitter offers actionable strategies for brands. This approach moves away from traditional guesswork, enabling brands to craft ads with a precision that resonates with their target audience. As we look towards future Super Bowls, the interplay between AI insights and creative execution will continue to define the battleground for audience attention, setting new standards for advertising excellence.

Super Bowl Ads in the Age of AI: Data Drives Winning Strategies

Revolutionizing Blockchain Privacy with COTI V2's MPC Protocol Introduction to Multi-Party Computation in Blockchain The recent technological advancements within the COTI ecosystem have set a precedent for privacy-centric layer 2 (L2) solutions on the Ethereum blockchain. With the successful integration of the Multi-Party Computation (MPC) protocol in COTI V2, a significant stride has been made towards enhancing blockchain privacy and security. This milestone not only marks a pivotal point in COTI's development journey but also showcases the potential for MPC technology to redefine the norms of blockchain privacy. The Essence of Multi-Party Computation Multi-party computation (MPC) stands at the forefront of cryptographic innovation, facilitating a mechanism where multiple parties can jointly compute a function over their inputs while keeping those inputs private. The crux of MPC lies in its ability to safeguard the confidentiality of input data, allowing for a collaborative computation process without revealing sensitive information to other participants. This cryptographic technique is instrumental in scenarios where data privacy is paramount, yet there is a need for collective computation. Architectural Innovations: MPC Endpoints and Extended EVM The integration of MPC within the COTI ecosystem introduces the concept of MPC endpoints, integral to the Extended Ethereum Virtual Machine (EVM) execution module. This approach enhances the Ethereum blockchain's capabilities by marrying the traditional EVM state with a private extension managed by the MPC protocol. Such an architectural innovation not only improves security but also broadens the applicability of decentralized applications (DApps) by introducing a layer of privacy where necessary. Exploring the Architecture and Workflow The architecture of the MPC protocol is ingeniously designed, comprising cryptographic key material, a pool of encrypted data, and pseudo-contracts. This design facilitates a seamless interaction between the submission of encrypted data by users and the execution of computations by the MPC "black box". Users initiate this process by generating their key material, followed by the submission of encrypted data. Subsequently, a workload can be executed securely on this encrypted data, producing results that are either public or encrypted under a specific user's key, thereby maintaining privacy integrity. Delving into an MPC Endpoint An in-depth examination of an MPC endpoint reveals the intricate processes occurring before and after the execution of a workload. Initially, the endpoint stores the key material and encrypted data set to undergo computation. Post-execution, the outcome includes private variables encrypted under a network key, demonstrating the protocol's ability to execute secure computations without exposing sensitive information. The Role of Garbling in MPC The garbling process is central to the MPC's operation, transforming a pseudo-contract into a garbled circuit that represents a one-time executable function. This ensures that each computation is isolated, enhancing the system's security by preventing the reuse of garbled circuits. This process exemplifies how MPC maintains privacy and security, emphasizing the protocol's significance in achieving a privacy-centric blockchain ecosystem. Milestone Achievements and Future Outlook The completion of this milestone has not only demonstrated the successful implementation of the MPC protocol within COTI V2 but also laid the groundwork for future advancements. This achievement underscores the potential of MPC technology in revolutionizing blockchain privacy, setting the stage for further developments in COTI V2's journey towards creating a more secure and private blockchain infrastructure. The journey towards realizing a fully privacy-centric L2 solution on Ethereum through COTI V2 represents a bold leap forward in blockchain technology. As we look ahead, the continued development and integration of MPC and other privacy-enhancing technologies promise to expand the horizons of blockchain utility, security, and confidentiality. Stay tuned for the unfolding of this groundbreaking endeavor, as we pave the way for a new era of blockchain privacy. The Future Horizon: Advancements and Implications of COTI V2's MPC Protocol The Path Forward: Enhancing Blockchain's Privacy Landscape The successful integration of the Multi-Party Computation (MPC) protocol within COTI V2 not only signifies a major milestone in blockchain privacy but also paves the way for a future where blockchain technology can be leveraged securely and privately across various domains. This advancement heralds a new era of privacy-centric applications, promising to address the longstanding challenges of data privacy and security in decentralized networks. Expanding the Use Cases of MPC in Blockchain The versatility of the MPC protocol extends beyond the realms of financial transactions and into sectors where privacy and security are of paramount importance. Healthcare, supply chain management, and voting systems stand to benefit significantly from the adoption of MPC, enabling the secure and confidential computation of sensitive data. The successful deployment of MPC within COTI V2 showcases the potential for broader application, encouraging the exploration of new use cases that were previously constrained by privacy concerns. The Intersection of MPC and Regulatory Compliance As regulatory scrutiny around data privacy intensifies, the need for technologies that can reconcile the demands of regulatory compliance with the benefits of blockchain becomes increasingly critical. The MPC protocol offers a viable solution by enabling the secure sharing and processing of data without compromising privacy. This capability is particularly relevant in the context of regulations such as the General Data Protection Regulation (GDPR) in the European Union, where the protection of personal data is a legal requirement. The adoption of MPC can facilitate compliance while fostering innovation within the blockchain space. Advancements in Cryptographic Techniques and Their Implications The development and integration of the MPC protocol within COTI V2 are indicative of the rapid advancements in cryptographic techniques. These innovations not only enhance the privacy and security of blockchain transactions but also contribute to the maturation of the blockchain ecosystem as a whole. By adopting cutting-edge cryptographic solutions, blockchain platforms can address the critical challenges of scalability, privacy, and interoperability, thereby unlocking new possibilities for decentralized applications. Challenges and Opportunities Ahead Despite the significant progress achieved with the MPC protocol, challenges remain in the quest for widespread adoption. Scalability, computational efficiency, and user-friendly interfaces are among the hurdles that need to be overcome to fully realize the potential of MPC in blockchain. However, these challenges also present opportunities for innovation and collaboration, driving the continued evolution of blockchain technology. Envisioning a Privacy-Preserving Future with COTI V2 The journey of COTI V2, marked by the successful integration of the MPC protocol, represents a significant leap towards achieving a privacy-centric blockchain ecosystem. This milestone not only demonstrates the feasibility of secure and private computations on the blockchain but also sets the stage for future innovations that could redefine the landscape of decentralized technologies. As we look towards the horizon, the ongoing development of COTI V2 and its MPC protocol holds the promise of a more secure, private, and inclusive blockchain future, heralding a new chapter in the evolution of decentralized systems. In conclusion, the advancements achieved with COTI V2's MPC protocol illuminate the path towards a more privacy-centric blockchain infrastructure. By addressing the pivotal challenges of privacy and security, COTI V2 lays the foundation for a blockchain ecosystem that is not only more secure and efficient but also more adaptable to the diverse needs of users across different sectors. The continued exploration and integration of such privacy-enhancing technologies will undoubtedly play a critical role in shaping the future of blockchain, driving its adoption and utility in a world where privacy and security are increasingly valued. Don’t forget to like and share the story! This author is an independent contributor publishing via our brand-as-author program. Be it through direct compensation, media partnerships, or networking, the author has a vested interest in the company/ies mentioned in this story. HackerNoon has reviewed the report for quality, but the claims herein belong to the author. #DYOR Vested Interest Disclosure:

Navigating the Future of Blockchain with COTI V2's Privacy Innovations

Software Repositories and Machine Learning Research in Cyber Security: Abstract & Introduction 

Imagine you're developing on your laptop, using Docker to containerize your applications for consistency and ease of deployment. Your current project involves using MinIO for object storage, and you've set it up beautifully in Docker. But here's the twist: your workflow requires MinIO to interact with a Flask application running on your localhost for processing events and executing functions (referred to as and in MinIO's context). Event Notifications Object Lambdas One of the common hurdles you might encounter in this setup is a networking-related error, typically manifesting as “ error (flask-notification:webhook): dial tcp 127.0.0.1:5000”…. This error is indicative of a fundamental networking misalignment between the Docker container where MinIO is running and the Flask application hosted on your localhost. Essentially, when MinIO inside Docker tries to send event notifications to the Flask app, it attempts to connect to localhost:5000. However, within the Docker context, localhost refers to the Docker container itself, not the host machine where your Flask app is running. This results in MinIO attempting to establish a connection with a service on localhost:5000 inside its container, where no such service exists, leading to the connection being refused. This scenario underscores the need for a clear understanding of Docker networking, particularly how localhost is interpreted differently inside a Docker container. This guide will subsequently delve into effective strategies to realign this communication, ensuring that MinIO inside Docker can successfully send event notifications to the Flask app running on your host machine. By addressing this challenge, we create a development environment that closely simulates a production setup, improving the reliability and effectiveness of your workflow. This walkthrough will guide you through the process of setting up this environment. We'll start by setting up MinIO within Docker, ensuring it's configured correctly to emit events. Then, we'll move on to the Flask application, designed to respond to these events. The crucial part of this guide will focus on enabling communication between the MinIO container and the Flask app running on your localhost. We'll tackle common hurdles such as Docker networking, address resolution and data consistency. By the end of this guide, you'll have a functional local development setup where , mimicking a production-like environment on your laptop. This scenario is not just about making things work; it's about creating a development environment that's as close to production as possible, enhancing the reliability of your development and testing processes. MinIO events trigger actions in your Flask app Understanding Docker Networking in Local Dev Strategies As we progress towards integrating our Flask application, it's imperative to first comprehend a crucial aspect of Docker networking - the communication dynamics between Docker containers and the host machine. This knowledge is particularly indispensable for developers orchestrating local development environments, such as on laptops, where there is a need for Docker containers and host services, like our Flask application, to interact without hiccups. The term ' ' within the context of a Docker container has a unique implication. Contrary to its usual reference to the host machine, inside a Docker container, 'localhost' points to the container itself. This distinction is of paramount importance, especially when Dockerized services, such as MinIO, attempt to reach services on the host machine. localhost To illustrate this point, let's consider an error scenario as shown in the following image: When MinIO, running inside a Docker container, fails to connect to the localhost – in this case, the Flask app on the host. The connection error we encountered in our Docker-MinIO screenshot, characterized by the message , initially appears complex. However, a closer analysis reveals its underlying cause. The segment flask-notification:webhook identifies the source of the issue – it's MinIO’s mechanism to interact with our Flask application. The phrase signifies MinIO's effort to establish a TCP connection with the Flask app, presumably located at . The critical part, , indicates that MinIO is unable to access the Flask app at the specified address, underscoring a fundamental challenge in containerized networking. (flask-notification:webhook): dial tcp 127.0.0.1:5000: connect: connection refused dial tcp 127.0.0.1:5000 localhost:5000 connection refused To correct this, a clear and direct communication pathway between MinIO and Flask is essential. For scenarios where Flask is hosted on the same machine but external to Docker, the solution involves configuring MinIO to connect to . This special DNS address acts like a direct line, enabling MinIO within its container to reach out to the Flask application on the host. host.docker.internal:5000 This offers a solution for Mac and Windows users through , a DNS name that allows containers to correctly reference the host, thus facilitating the desired interaction between containerized services and host-based applications. Understanding and utilizing this Docker feature is critical for ensuring that local development setups mirror production environments as closely as possible, thereby enhancing development reliability and efficiency. host.docker.internal Overview of Assets The foundation of our guide lies in the prepared assets available at the in the directory. Here I have provided the source code for anything discussed in this guide, at the heart of this repository, you'll find: GitHub minio/blog-assets repository docker_to_localhost This contains the relevant commands to preform the actions taken in Smooth Sailing from Docker to Localhost(this article). README File: README.md : This file constitutes the Flask application, serving as a crucial component in our setup. It is tailor-made to interact efficiently with the MinIO service. Flask App Files main.py : This is the blueprints for creating your Docker container. It defines the environment in which your Flask applications will be built and ran, ensuring consistency across different setups. Docker File for Flask App dockerfile : Acting as the orchestrator, this define how multiple containers (like MinIO and Flask) coexist and communicate within your Docker environment. Docker Compose File docker-compose.yaml Deploying Flask App for MinIO Event Handling In the context of integrating a Flask application with a Dockerized MinIO service, let's delve into the configuration of the Flask app. Specifically, I've set up a Flask endpoint with an identifier of that is intended to serve as the receiver for MinIO's webhook events via , as highlighted in the following screenshot: “flask-minio-event” http://host.docker.internal:5000/minio_event Below is the code for our demonstration Flask App that runs on our local machine and serves out a route of on : /minio_event port 5000 from flask import Flask, request, jsonify
import logging
import os
from dotenv import load_dotenv

app = Flask(__name__)

load_dotenv()
logging.basicConfig(level=logging.INFO)
I
@app.route('/minio_event', methods=['POST'])
def log_bucket_event():
    """
    Logs events received from MinIO to the Python logger.
    """
    event_data = request.json
    logging.info(f"Event received: {event_data}")
    return jsonify({'message': 'Event logged successfully'})

if __name__ == '__main__':
    port = int(os.getenv('FLASK_PORT', 5000))
    debug_mode = os.getenv('FLASK_DEBUG', 'False') == 'True'
    app.run(host='0.0.0.0', port=port, debug=debug_mode) In the previous section, we explored how Docker containers interpret 'localhost', which is a significant consideration in ensuring that the Flask app correctly receives notifications from a Dockerized MinIO instance. This Flask application in more specific detail, is configured to receive and log events from MinIO. The endpoint /minio_event is set up to listen for POST requests, which are expected to be the event notifications from MinIO. Note that the application runs on port 5000 and is configured to be accessible from all network interfaces (0.0.0.0), which is crucial for receiving requests from MinIO running inside a Docker container. Deploying MinIO Container with Docker Networking Deploying the MinIO container to interact with localhost services like a Flask API requires a specific Docker network configuration. This setup is crucial for enabling seamless communication between the container and your local machine, particularly for local development or testing scenarios. To achieve this, the MinIO container must be configured to use the host's network. This approach allows the container to directly interact with services running on the host machine, such as a Flask application. It's especially beneficial in development environments where the Flask app needs to access or store data in MinIO. The command for this configuration is straightforward yet powerful: docker run --name minio --network="host" minio/minio server /data Using the option removes the complexities of Docker's default network bridging and port forwarding. It's a simpler, more direct way to ensure that the MinIO container can access any service running on localhost, not just limited to the Flask app. This makes the MinIO container versatile for a variety of local services, streamlining the development and testing processes. --network="host" Deploying MinIO and Flask App Containers with Docker-Compose You can use Docker networking features, such as a custom network or Docker Compose, to facilitate communication between containers. By placing both containers on the same network, they can communicate using their container names as hostnames. Example using Docker Compose to facilitate the communication between a MinIO container named minio, and a Flask app container named flaskapp specified below as services: version: '3.8'
services:
  minio:
    image: minio/minio
    command: server /data
    ports:
      - "9000:9000"
    extra_hosts:
      - "host.docker.internal:host-gateway"
    networks:
      - mynetwork

  flaskapp:
    image: python:3.9 # Use an official Python image
    command: >
      sh -c "pip install Flask
      && FLASK_APP=main.py FLASK_RUN_HOST=0.0.0.0 flask run" # Install Flask and run the app
    volumes:
      - ./app:/app # Mount your Flask app directory
    working_dir: /app # Set working directory to your app directory
    ports:
      - "5000:5000"
    depends_on:
      - minio
    networks:
      - mynetwork

networks:
  mynetwork:
    driver: bridge This setup maps host.docker.internal to the host machine's IP address, allowing containers to access services on the host by adding the extra_hosts parameter. This setup allows MinIO to recognize host.docker.internal as the host machine, bridging the container-to-host communication gap. It's a smart workaround for a common Docker challenge, ensuring that MinIO can connect to services on the host, such as our Flask app. Additionally by including the Flask installation in the docker-compose file we will make the app listen on 0.0.0.0. This change is vital for accessibility from other containers and the host, broadening the communication scope of our Flask app. It’s an essential step for setups where Flask might also be containerized. The depends_on directive in the Flask service ensures an orderly startup sequence. By starting the Flask app only after MinIO is operational, we avoid potential timing issues. This arrangement mimics production environments where service dependencies are carefully managed, adding robustness to our local development setup. These examples are tailored for a Dockerized MinIO setup, ensuring effective communication with services running on the host. Be mindful of the security implications, especially when adjusting network settings or exposing ports, and choose the solution that aligns with your security and architectural requirements. Both methods will allow the MinIO service to communicate with your Flask application, depending on whether the Flask app is running on the host or inside another Docker container. Seamless Integration in a Dockerized World We've journeyed through the nuances of Docker networking, unraveling how to efficiently bridge Docker containers with localhost environments so a Dockerized MinIO service can effectively communicate with a Flask application running on your host machine. These insights are crucial for developers looking to harness the power of Docker without compromising on the flexibility and convenience of local development. Understanding Docker networking is more than a technical necessity; it's a step toward mastering containerized environments. The ability to set up and manage these interactions is a requirement in today's cloud-native development landscape. Whether you're developing on a laptop or deploying on a global scale, these skills ensure your applications remain robust, scalable, and secure. It's important to understand and implement best practices when developing cloud-native software. The Docker ecosystem is ever-evolving. You can quickly tap into the latest and greatest cloud-native technologies with an architecture based on containers and object storage. Stay curious and keep exploring. MinIO enables this exploration. Containerized, Kubernetes-native and S3 API compatible, MinIO frees you to write code that runs anywhere, consistently and reliably. Good luck on your journey with Docker and MinIO. May your development be robust, your solutions secure, and your learning continuous. If you have any questions we’re here to help, at or by joining the . hello@min.io Slack community

Smooth Sailing: Transitioning from Docker to Localhost

স্মুথ সেলিং: ডকার থেকে লোকালহোস্টে রূপান্তর

Reibungsloses Segeln: Übergang von Docker zu Localhost

Navegación tranquila: transición de Docker a Localhost

Navigation fluide : transition de Docker vers Localhost

सहज सेलिंग: डॉकर से लोकलहोस्ट में संक्रमण

順風満帆: Docker から Localhost への移行

원활한 항해: Docker에서 Localhost로 전환

Navegação suave: transição do Docker para o Localhost

Плавный ход: переход с Docker на Localhost

Sorunsuz Yolculuk: Docker'dan Localhost'a Geçiş

Thuận buồm xuôi gió: Chuyển đổi từ Docker sang Localhost

顺利进行：从 Docker 过渡到 Localhost

Have you ever felt like you're playing against a gaming wizard in your favorite online video game? Not the cool kind, but the kind that unnaturally shoots through walls and disappears in a flash. Guess what - that's not a wizard; that's a cheater. And if you've ever encountered one, you know it's more than just annoying; it's deeply frustrating. With over worldwide, gaming has risen above being a mere hobby. It has emerged as a crucial component of contemporary digital culture, extending its influence across the realms of entertainment and technology. However, this remarkable growth has not been without its share of challenges, and the widespread problem of in online video games . 3 billion gamers cheating tops the list of challenges Throughout history, cheating has posed a persistent threat to the gaming industry. It not only disrupts the gaming experience for honest players but also poses significant challenges for gaming companies. Cheating has also an immense financial impact on the industry, with 79% of developers identifying it as a significant concern. To address this pressing issue, the gaming community has witnessed the development of various anti-cheat solutions over the past decades. is one of the most recent solutions that has been introduced to the gaming cybersecurity arena. SARD While SARD may be a new name in the market, . They come with a decade of experience at Frost Security, where they honed their expertise in gaming cybersecurity solutions. This background sets the stage for SARD to potentially revolutionize how we tackle cheating in the gaming world. the brains behind SARD aren't novices ” adds Alexander "SARD isn't just our latest project; it's the culmination of our team’s battle scars and victories in cybersecurity." shares , the Co-Founder and CEO of SARD “Our team has been in the cybersecurity game for over 15 years, and now, we've packed all our finest, time-honed strategies and tricks into SARD. And what sets SARD apart is its “superhero duo” at the core Alexander Harutyunyan “We've merged the unshakable might of kernel-level security with smart AI. It's like teaming up a cyber detective with a digital superhero, both committed to guarding the fairness and safety of the gaming world." To fully illustrate what SARD brings to the table, it's crucial to delve into the existing anti-cheat landscape within the gaming industry. Broadly, the landscape is dominated by two widely used technologies: kernel-based technology and AI-powered solutions. Kernel-based gaming anti-cheats work at the operating system's core, providing deep and direct access to monitor the system functions, including memory management and hardware interactions. Such access is essential for detecting and preventing cheating activities that might attempt to manipulate or interfere with the game's processes at a fundamental level. This approach is effective but not without its challenges, including complex development requirements and privacy concerns. On the other hand, utilize sophisticated machine learning modules to analyze and identify patterns or behaviors indicative of cheating. These advanced algorithms continuously learn from a vast array of in-game data, enabling the system to effectively differentiate between legitimate player actions and potential cheating strategies. The core strength of AI-powered anti-cheat systems lies in their ability to learn and adapt. Unlike traditional systems that operate on predefined algorithms, these AI anti-cheat solutions employ machine learning techniques to analyze vast amounts of game data. By continuously learning from player behavior, game mechanics, and the ever-changing tactics of cheaters, these systems become more effective over time. They are designed to identify anomalies in gameplay that could indicate cheating, such as impossibly fast reaction times or unnatural accuracy. AI-powered anti-cheat systems SARD is not just one or the other; it's both - kernel strength combined with AI smarts. This synergy offers an unprecedented level of game security and adaptability. SARD's innovation isn't just about technology; it's about rethinking the approach. , COO and Co-Founder, explains their philosophy: " Tigran Gyurjyan SARD was born to challenge the established norms within the anti-cheat market. Our goal was to provide a more accessible, cost-effective, and meanwhile customizable solution. We decided to , making it more transparent for our clients. open the black box of anti-cheat technology .” Recognizing the uniqueness of each game, we prioritize customization, allowing our clients to fine-tune the solution to meet their game's specific needs This emphasis on customization empowers game developers to adjust SARD's settings, ensuring top-tier protection without compromising the player experience. Such flexibility is a game-changer in an industry where often fall short. one-size-fits-all solutions But SARD's commitment goes beyond just providing flexible tools. It also offers to ensure a , which is super important in the fast-paced world of gaming. premium customer support swift and effective issue resolution Restating the pressing importance of smart gaming anti-cheats, Tigran recalls his own experience “ ” This incident resonates with the collective mission of SARD – to develop a comprehensive and robust solution to protect online gaming integrity. The first case I could recall facing the real threats of cheating in games was back in the 2000s. Our team of enthusiasts had created a game that was a mixture of city quests. We hoped that we could generate impressive revenues with the game... But the truth was 2 days later the game was hacked, and we couldn't protect it... About 25 years have passed since then, and now I can proudly state that we have developed and refined a solution that will save the revenues of those who have invested their time and efforts to craft new games… I often recall the story, and it serves as a constant reminder of the aim we've embraced—to safeguard the gaming community from the challenge of cheating and uphold fair play. Looking to the future, SARD's ambitions go beyond just tackling cheats in PC games. Alexander shares SARD’s broader vision: " " We're expanding our horizons to cover more areas in gaming security. Currently, we are in the process of developing anti-tamper and anti-DDoS solutions, and we are also stepping into mobile gaming. Meanwhile, we are looking into the web3 space, where the stakes are higher and the need for solid security is paramount. In a gaming world where cheats are constantly evolving, SARD's proactive, innovative approach is exactly what's needed. states “Yes, we know the problem, and ,” then adds  “As a gamer myself, I believe that the gaming community deserves better cybersecurity solutions, and that's what we've built at SARD.” Alexander we have the solution SARD Anti-Cheat software represents a significant advancement in the ongoing battle against cheating in online PC games.  Its combination of game security on kernel-level with AI anti-cheat modules provides a sophisticated, adaptable approach to a complex problem.  As the gaming industry continues to evolve, the effectiveness of tools like SARD will be crucial in maintaining a fair and enjoyable gaming experience.

Introducing SARD: The New AI-Empowered Anti-Cheat Solution for Fair Gaming

SARD উপস্থাপন করা হচ্ছে: ন্যায্য গেমিংয়ের জন্য নতুন এআই-এমপাওয়ারড অ্যান্টি-চিট সমাধান

Wir stellen vor: SARD: Die neue KI-gestützte Anti-Cheat-Lösung für faires Spielen

Presentamos SARD: la nueva solución antitrampas impulsada por IA para juegos justos

Présentation de SARD : la nouvelle solution anti-triche basée sur l'IA pour un jeu équitable

SARD का परिचय: फेयर गेमिंग के लिए नया AI-सशक्त एंटी-चीट समाधान

SARD のご紹介: 公正なゲームのための AI を活用した新しいアンチチート ソリューション

SARD 소개: 공정한 게임을 위한 새로운 AI 기반 부정행위 방지 솔루션

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