Seccuri

seccuri

The wait is over! - it’s time to celebrate the champion startups of the world. Shout out to all of the participants in our ! You did a wonderful job showcasing your incredible startups and powering through another year. 2023 Startups of the Year is a community-driven voting platform brought to you by HackerNoon that recognizes and celebrates tech startups changing technology and the world for the better. The award started on May 8th, 2023, where the internet nominated and voted for 30k startups around the world. The voting period remained open until December 31, 2023, and now we are here with the winner announcement after a comprehensive evaluation by the HackerNoon team. Startups of the Year 2023 To see the top 3 startups in each respective city, visit and pick a city. hackernoon.com/startups You’ll get a little surprise 😉 Here are the Startups of the Year Regional Announcements: North America Europe Asia Africa South America Oceania Misc/Remote From all of us at HackerNoon, thank YOU for your excellent campaign perseverance 🥺 This year has been awesome! Startups of the Year 2023 is concluded with 623k+ total votes, up from 215k+ votes in the inaugural startup competition. We look forward to seeing you again in the next campaign (stay tuned 😉)! Without further ado, let us kick off the announcement by celebrating the cities with top votes per region for 2023 🎉 North America (11,697 votes): Winner: Bloomfilter Cleveland, Ohio (8,835 votes) - Winner: SerpApi Austin, Texas , California (5,564 votes) - Winner: Finch San Francisco (4,258 votes) - Winner: RELAYTO SoMa, California - (3339 votes) Winner: Windscribe Toronto, Canada, North America (3304 votes) - Winner: Vouched Seattle (2716 votes) - Winner: Seccuri Palo Alto, CA, North America (2671 votes) - Winner: Salsa Valley Miami, FL, North America (2,613 votes) - Winner: Majhi Group Manhattan, New York (2423 votes) - Winner: Flieber, Inc. New York City, NY, North America See the complete announcement for the . Startups of the Year, North America, here Europe (45,612 votes) - Winner: Algomo London, UK (2,344votes) - Winner: Data Lake Warsaw, Poland (1,955 votes) - Winner: Hoken Tech Bari, Italy (1716 votes) - Winner: Not8 Amsterdam, Netherlands (1657 votes) - Winner: KYVE Frankfurt Am Main, Germany (1566 votes) - Winner: BazaIT Kyiv, Ukraine (1496 votes) - Winner:  Unity Network Tallinn, Estonia (1,478 votes) - Winner: Volcaino Ghent, Belgium (1,288 votes) - Winner: Owl Me Dublin, Ireland (1198 votes) - Winner: Groover Paris, France See the complete announcement for the . Startups of the Year, Europe, here Oceania (3,350 votes) - Winner: Sharepass Melbourne (2,107 votes) - Winner: The Catfisher Project Perth, Australia (220 votes) - Winner: Gemini AI Solutions Auckland, New Zealand, Oceania (213 votes) - Winner: Nexustorage Wellington, New Zealand, Oceania (138 votes) - Winner: Coviu Sydney (124 votes) - Winner: Bopple Brisbane, Australia, Oceania (124 votes) - Winner: Regional CFO New South Wales, Australia, Oceania (106 votes) - Winner: Edit on the Spot Gold Coast, Australia, Oceania (93 votes) - Winner: Knowledge Satnav Canbera, Australia, Oceania , Australia, Oceania (48 votes) - Winner: Peptalk360 North Melbourne, Melbourne See the complete announcement for the . Startups of the Year, Oceania, here Asia (16,345 votes) - Winner: Serenity Shield Singapore (13,588 votes) - Winners: QUIXY Telangana, India (10,168 votes) - Winner: HealthySure Mumbai, India (3,865 votes) - Winner: InterviewBuddy Visakhapatnam, India (3,733 votes) - Winner: Omniflo Bengaluru, India (4589 votes) - Winner: MeNow Tel Aviv (3877 votes) - Winner: ForestVPN Dubai (2478 votes) - Winner: TechGraph Powai, Mumbai, India (1004 votes) - Winner: Reelo Ahmedabad, GJ, India (875 votes) - Winner: Debt Hunter Hong Kong, China See the complete announcement for the . Startups of the Year, Asia, here Africa (8,179 votes) - Winner: GO PLATFORM Algiers, Algeria (767 votes) - Winner: Tudu Accra, Ghana (415 votes) - Winner: Treford Lagos, Nigeria (265 votes) - Winner: Phalconwise Ogbomosho, Nigeria (157 votes) - Winner: AbuErdan Cairo (151 votes) - Winner: Eazipay Lekki (144 votes) - Winner: KaziConnect Dar es Salaam, Tanzania (101 votes) - Winner: faceATT Gauteng, South Africa (79 votes) - Winner: Dimagi Cape Town (71 votes) - Winner: Sabi Addis Ababa See the complete announcement for the . Startups of the Year, Africa, here South America (376 votes ) - Winner: UpGirl Santiago, Chile (217 votes) - Winner: Klever Sao Paulo, Brazil (191 votes) - Winner: Tributo Simple Buenos Aires, Argentina (127 votes) - Winner: Light-it Montevideo, Uruguay (87 votes) -Winner: Thousand Antioquia, Colombia (108 votes) - Winner: GetLavado Lima (101 votes) - Winner: Cogniflow Medellin (73 votes) - Winner: TPAGA Bogota (73 votes) - Winner: Reliv Quito (47 votes) - WInner: Quantex Cordoba See the complete announcement for the . Startups of the Year, South America, here Misc/Remote: Featured Startups (1500 votes) - Winner: CIC Chain Remote, Earth (1193 votes) - Winner: Reship Colony US Misc (146 Votes) - Winner: Partful UK, Misc (116 votes) - Winner: GoMining British Virgin Islands Misc (283 Votes) - Winner: tomorrowSpace Germany Misc (225 votes) - Winner: Dtravel Cayman Islands See the complete announcement for Misc/Remote startups . here Once again, HUGE CONGRATULATIONS and BIG PROPS to all of the top Startups of each region this year! The whole team at HackerNoon would like to thank you for your perseverance in the campaign, and we look forward to seeing you in the next campaign. About HackerNoon’s Startups Of The Year Startups of The Year is HackerNoon’s Flagship community-driven event celebrating startups that survived and thrived in 2023. 30,000 startups across 4200+ cities and six continents participated this year to be crowned the best startup in their city. Visit our page to learn more. FAQ Companies that blog more get 67% more leads - Jumpstart your company’s blogging journey by purchasing our . Business Blogging Package The 2023 Startups Of The Year is sponsored by: . . The winners get a free .Tech Domain, a HackerNoon NFT, and a Tech Company News Page. TECH Domains

Stand Up for the Champion Startups of the Year! 

বছরের চ্যাম্পিয়ন স্টার্টআপের জন্য দাঁড়ান!

Setzen Sie sich für die Champion-Startups des Jahres ein!

¡Defiende a las startups campeonas del año!

Défendez les startups championnes de l’année !

वर्ष के चैंपियन स्टार्टअप के लिए खड़े रहें!

今年のチャンピオンスタートアップのために立ち上がろう!

올해의 챔피언 스타트업을 위해 일어서세요!

Defenda as startups campeãs do ano!

Встаньте на защиту стартапов-чемпионов года!

Yılın Şampiyon Startup'ları için Ayağa Kalkın!

Hãy ủng hộ các công ty khởi nghiệp vô địch của năm!

为年度冠军初创企业挺身而出！

Startups of The Year 2023 Winners: North America 

**MIAMI, Florida, November 21st, 2024/CyberNewsWire/--**Halo Security, a leader in external attack surface management and penetration testing, has announced the launch of its new Slack® app, empowering cybersecurity teams to receive real-time alerts on newly discovered assets, vulnerabilities, and other essential security updates directly within the Slack collaboration software. This new integration allows Halo Security’s customers to seamlessly incorporate important security notifications into their existing Slack workflows, improving response time and enhancing collaboration across security and IT teams. Previously, Halo Security offered Slack alerts through a Zapier integration, but this new, direct integration delivers a more streamlined experience, giving customers greater control with minimal configuration. Halo Security’s platform offers comprehensive external attack surface management, encompassing asset discovery, risk and vulnerability assessment, and penetration testing in a single, user-friendly dashboard. Built by a team of experienced penetration testers, security engineers, and reformed hackers, Halo Security provides organizations with an attacker’s perspective to help identify and address vulnerabilities. With the new Slack integration, customers are notified as soon as new issues and vulnerabilities are detected, unknown assets or open ports are discovered, and new technologies appear on their websites, helping teams stay ahead of emerging threats without being overwhelmed by irrelevant alerts. “Organizations need real-time, customized alerts for emerging security threats, but too many notifications can lead to alert fatigue,” said Ben Tyler, CTO of Halo Security. “Our direct Slack integration is easily configurable, giving customers precise control over which alerts they receive and allowing them to focus on the most critical issues directly within their existing workflows." How to Get Started with Halo Security’s Slack Integration Halo Security customers can activate the Slack app today directly from their accounts. New users interested in trying the Halo Security platform can sign up for a 7-day free trial at halosecurity.com. About Halo Security Halo Security is a comprehensive external attack surface management platform that provides asset discovery, risk assessment, and penetration testing in a single, easy-to-use dashboard. Founded by cybersecurity experts with backgrounds at McAfee, Intel, Kenna Security, OneLogin, and WhiteHat Security, Halo Security delivers a unique attacker-based approach to help organizations safeguard against potential threats. Users can learn more at halosecurity.com. Slack is a registered trademark and service mark of Slack Technologies, Inc. Contact VP of Marketing Nicholas Hemenway Halo Security nick@halosecurity.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here **MIAMI, Florida, November 21st, 2024/CyberNewsWire/--**Halo Security, a leader in external attack surface management and penetration testing, has announced the launch of its new Slack® app, empowering cybersecurity teams to receive real-time alerts on newly discovered assets, vulnerabilities, and other essential security updates directly within the Slack collaboration software. This new integration allows Halo Security’s customers to seamlessly incorporate important security notifications into their existing Slack workflows, improving response time and enhancing collaboration across security and IT teams. Previously, Halo Security offered Slack alerts through a Zapier integration, but this new, direct integration delivers a more streamlined experience, giving customers greater control with minimal configuration. Halo Security’s platform offers comprehensive external attack surface management, encompassing asset discovery, risk and vulnerability assessment, and penetration testing in a single, user-friendly dashboard. Built by a team of experienced penetration testers, security engineers, and reformed hackers, Halo Security provides organizations with an attacker’s perspective to help identify and address vulnerabilities. With the new Slack integration, customers are notified as soon as new issues and vulnerabilities are detected, unknown assets or open ports are discovered, and new technologies appear on their websites, helping teams stay ahead of emerging threats without being overwhelmed by irrelevant alerts. “Organizations need real-time, customized alerts for emerging security threats, but too many notifications can lead to alert fatigue,” said Ben Tyler, CTO of Halo Security. “Our direct Slack integration is easily configurable, giving customers precise control over which alerts they receive and allowing them to focus on the most critical issues directly within their existing workflows." “Organizations need real-time, customized alerts for emerging security threats, but too many notifications can lead to alert fatigue,” said Ben Tyler, CTO of Halo Security. “Our direct Slack integration is easily configurable, giving customers precise control over which alerts they receive and allowing them to focus on the most critical issues directly within their existing workflows." How to Get Started with Halo Security’s Slack Integration Halo Security customers can activate the Slack app today directly from their accounts. New users interested in trying the Halo Security platform can sign up for a 7-day free trial at halosecurity.com . halosecurity.com halosecurity.com About Halo Security Halo Security is a comprehensive external attack surface management platform that provides asset discovery, risk assessment, and penetration testing in a single, easy-to-use dashboard. Founded by cybersecurity experts with backgrounds at McAfee, Intel, Kenna Security, OneLogin, and WhiteHat Security, Halo Security delivers a unique attacker-based approach to help organizations safeguard against potential threats. Users can learn more at halosecurity.com . Slack is a registered trademark and service mark of Slack Technologies, Inc. Halo Security Halo Security halosecurity.com halosecurity.com Contact VP of Marketing Nicholas Hemenway Halo Security nick@halosecurity.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

Halo Security Launches Slack Integration For Real-Time Alerts On New Assets And Vulnerabilities

Halo Security, yeni aktivlər və zəifliklər haqqında real vaxt siqnalları üçün Slack inteqrasiyasını işə salır

Halo Security lanza la integración de Slack para alertas en tiempo real sobre nuevos activos y vulnerabilidades

Halo Security が Slack 統合を開始し、新たな資産や脆弱性に関するリアルタイムアラートを提供

Halo Security, 새로운 자산 및 취약점에 대한 실시간 알림을 위한 Slack 통합 출시

„Halo Security“ pradeda lėtą integraciją, kad realiuoju laiku būtų pranešama apie naujus išteklius ir pažeidžiamumą

Halo Security Melancarkan Integrasi Slack Untuk Makluman Masa Nyata Mengenai Aset dan Kerentanan Baharu

Halo Security lanceert Slack-integratie voor realtime waarschuwingen over nieuwe activa en kwetsbaarheden

Halo Security lansează integrarea Slack pentru alerte în timp real privind noile active și vulnerabilități

Halo Security запускает интеграцию со Slack для оповещений в реальном времени о новых активах и уязвимостях

Halo Security интегратсияи сустро барои огоҳиҳои вақти воқеӣ дар бораи дороиҳо ва осебпазириҳои нав оғоз мекунад

“Halo Security” täze aktiwlere we gowşak taraplara hakyky wagt duýduryşlary üçin haýal integrasiýa başlaýar

Ukhuseleko lweHalo luzisa uDityaniso lweSlack lwezilumkiso zeXesha lokwenyani kwii-asethi ezitsha kunye nokuba semngciphekweni

Best Practices for API Security: JavaScript and Python Examples 

The Generative AI industry has exploded, thanks to no small part to the release of ChatGPT in the fall of 2022, which took the world by storm. There has been a lot of hand-wrangling on the importance of model safety and data security, with the possibility of further AI regulations. As per IBM, around 70% of their survey respondents say that innovation is more important than security, which contrasts with 82% of the respondents accepting that security in AI is essential to their business. Unsurprisingly, only 24% of the projects polled in the IBM survey have components meant to secure the initiative. Security is not an add-on to thoroughly flushed-out Generative AI infrastructure. It should be weaved in and through the entire infrastructure rather than slapped on top of it to pass regulations and compliance. The immense power of Generative AI brings with it vast responsibilities. This article aims to convince readers of the importance of security in AI and provide them with the knowledge and talking points to become security champions in their organizations. Objective The objective of GenAI security should be to secure the entire infrastructure from end to end. As security champions, we must secure data ingestion infrastructure, ensuring data legitimacy, data manipulation operations, model deployment, and user interactions. For the last year, people have been complaining about the degrading quality of answers provided by ChatGPT, whereas OpenAI has refuted such claims. While it is true that the quality of responses can deteriorate over time, it is entirely possible that an internally deployed model can be modified in an unauthorized manner by bad actors. Such responses can be indistinguishable from the typical quality degradation we expect. It goes without saying that customer data, which consists of their prompt history and responses, should be considered critical data, and we need to examine any risks that might seriously compromise users' trust. While the conversations are between a human and a machine, the personal nature of such discussions should be treated just like human-to-human conversations and secured as such. Risks There is no limit to the kind of risks one has to deal with when working on GenAI infrastructure. While we are looking into the typical security best practices from traditional security paradigms, there are AI-specific steps we need to take to ensure that we achieve a reasonable security posture. Data Poisoning: We have heard the old adage of garbage-in-garbage-out. If malicious actors manage to introduce malicious data into the corpus, the responses might not be what we expect. Imagine your company analyzes data and signals intelligence for your customers. If the data was poisoned by modification, deletion, or addition, then the results are going to be far from accurate. In fact, properly crafted training data can lead to wrong responses from the AI model. Model Manipulation: While this sounds far-fetched, the National Institute of Standards and Technology has outlined how different types of cyberattacks manipulate the behavior of AI systems, focusing on evasion and privacy attacks. Model manipulation is closely tied to data poisoning; in practice, it’s hard to differentiate one from the other. Prompt Injection Attacks: This attack vector is unique to Generation AI and is not in traditional cybersecurity practices. While a lot of research has gone into understanding the reasoning pathway of the models, for all practical purposes, they do remain a black box. Attackers can bypass safety controls by specially crafting input prompts, which lead to unauthorized responses. These responses can contain confidential information or reveal some inner workings in the worst case. These attacks work because they exploit the very nature of Generative AI models, which deal with understanding natural languages. Supply Chain Attacks: Generative AI is nascent and involves integrating various software components, sometimes with little regard to security, to beat the competitors in the field of innovation. Each third-party library or component introduces another point of attack. Even malicious or modified data can fit under the umbrella of supply chain attacks. Refer to Open Worldwide Application Security Project (OWASP) LLM05: Supply Chain Vulnerabilities for more information. Approaches There isn’t a single way to handle security in the Generative AI space. Any attempts at creating a formal process run afoul of the dreaded checklist-based security compliance processes. At its core, security is not about processes and technology but a mindset and the culture that promotes it. Fundamentally, to secure Generative AI infrastructure, one needs a secure-by-design architecture. This might include, but is not limited to, access control, the least privilege model, encryption at rest and transit, and threat/anomaly detection systems. The architecture keeps evolving based on new research and changing business needs. Changes to architecture can increase the attack surface. To avoid this, there should be some form of operational review and annual security overview. One needs to have a culture where people are proactive about reviewing the changes and bold enough to discuss the possible threats. Supply chain threats are more complex to prevent, but digitally signing all components and pieces and validation before deployment can be a good start. A little more involved process may be using Software Bill of Materials tracking, which is recommended by the Cybersecurity and Infrastructure Security Agency (CISA) Conclusion The job of a CISO is becoming increasingly complex and involved. Apart from the increased DDoS attacks as reported by Cloudflare, which is 20% year-over-year, the addition of Generative AI to the broader organization infrastructure adds more moving parts to an already complex setup. While moving ahead with innovation and sidelining security might look tempting at first, it is equivalent to playing with fire. One wrong move can result in disastrous results, whether lost customer trust, personal data leak, or government-imposed penalties. An individual must be someone other than a CISO to push for a culture shift. Often, executives rely on people reporting to them to give them advice, which in turn is asked down the reporting chain. If Engineers or program Managers can make themselves security champions, they have a high chance of shining and access to opportunities for bigger responsibilities and a career boost. The future of Generative AI hinges on our ability and willingness to secure it. Organizations that recognize this fact and act proactively will be best positioned to thrive and overcome dangers and regulatory hurdles. The Generative AI industry has exploded, thanks to no small part to the release of ChatGPT in the fall of 2022, which took the world by storm. There has been a lot of hand-wrangling on the importance of model safety and data security, with the possibility of further AI regulations . further AI regulations further AI regulations As per IBM, around 70% of their survey respondents say that innovation is more important than security, which contrasts with 82% of the respondents accepting that security in AI is essential to their business. Unsurprisingly, only 24% of the projects polled in the IBM survey have components meant to secure the initiative. Security is not an add-on to thoroughly flushed-out Generative AI infrastructure. It should be weaved in and through the entire infrastructure rather than slapped on top of it to pass regulations and compliance. The immense power of Generative AI brings with it vast responsibilities. This article aims to convince readers of the importance of security in AI and provide them with the knowledge and talking points to become security champions in their organizations. Objective The objective of GenAI security should be to secure the entire infrastructure from end to end. As security champions, we must secure data ingestion infrastructure, ensuring data legitimacy, data manipulation operations, model deployment, and user interactions. For the last year, people have been complaining about the degrading quality of answers provided by ChatGPT , whereas OpenAI has refuted such claims. While it is true that the quality of responses can deteriorate over time , it is entirely possible that an internally deployed model can be modified in an unauthorized manner by bad actors. Such responses can be indistinguishable from the typical quality degradation we expect. degrading quality of answers provided by ChatGPT degrading quality of answers provided by ChatGPT quality of responses can deteriorate over time quality of responses can deteriorate over time It goes without saying that customer data, which consists of their prompt history and responses, should be considered critical data, and we need to examine any risks that might seriously compromise users' trust. While the conversations are between a human and a machine, the personal nature of such discussions should be treated just like human-to-human conversations and secured as such. Risks There is no limit to the kind of risks one has to deal with when working on GenAI infrastructure. While we are looking into the typical security best practices from traditional security paradigms, there are AI-specific steps we need to take to ensure that we achieve a reasonable security posture. Data Poisoning : We have heard the old adage of garbage-in-garbage-out. If malicious actors manage to introduce malicious data into the corpus, the responses might not be what we expect. Imagine your company analyzes data and signals intelligence for your customers. If the data was poisoned by modification, deletion, or addition, then the results are going to be far from accurate. In fact, properly crafted training data can lead to wrong responses from the AI model. Data Poisoning Model Manipulation : While this sounds far-fetched, the National Institute of Standards and Technology has outlined how different types of cyberattacks manipulate the behavior of AI systems , focusing on evasion and privacy attacks. Model manipulation is closely tied to data poisoning; in practice, it’s hard to differentiate one from the other. Model Manipulation different types of cyberattacks manipulate the behavior of AI systems different types of cyberattacks manipulate the behavior of AI systems Prompt Injection Attacks : This attack vector is unique to Generation AI and is not in traditional cybersecurity practices. While a lot of research has gone into understanding the reasoning pathway of the models, for all practical purposes, they do remain a black box. Attackers can bypass safety controls by specially crafting input prompts , which lead to unauthorized responses. Prompt Injection Attacks specially crafting input prompts specially crafting input prompts These responses can contain confidential information or reveal some inner workings in the worst case. These attacks work because they exploit the very nature of Generative AI models, which deal with understanding natural languages. Supply Chain Attacks : Generative AI is nascent and involves integrating various software components, sometimes with little regard to security, to beat the competitors in the field of innovation. Each third-party library or component introduces another point of attack. Supply Chain Attacks Even malicious or modified data can fit under the umbrella of supply chain attacks. Refer to Open Worldwide Application Security Project (OWASP) LLM05: Supply Chain Vulnerabilities for more information. Open Worldwide Application Security Project (OWASP) LLM05: Supply Chain Vulnerabilities Open Worldwide Application Security Project (OWASP) LLM05: Supply Chain Vulnerabilities Approaches There isn’t a single way to handle security in the Generative AI space. Any attempts at creating a formal process run afoul of the dreaded checklist-based security compliance processes. At its core, security is not about processes and technology but a mindset and the culture that promotes it. Fundamentally, to secure Generative AI infrastructure, one needs a secure-by-design architecture. This might include, but is not limited to, access control, the least privilege model, encryption at rest and transit, and threat/anomaly detection systems. The architecture keeps evolving based on new research and changing business needs. Changes to architecture can increase the attack surface. To avoid this, there should be some form of operational review and annual security overview. One needs to have a culture where people are proactive about reviewing the changes and bold enough to discuss the possible threats. Supply chain threats are more complex to prevent, but digitally signing all components and pieces and validation before deployment can be a good start. A little more involved process may be using Software Bill of Materials tracking, which is recommended by the Cybersecurity and Infrastructure Security Agency (CISA) recommended by the Cybersecurity and Infrastructure Security Agency (CISA) recommended by the Cybersecurity and Infrastructure Security Agency (CISA) Conclusion The job of a CISO is becoming increasingly complex and involved. Apart from the increased DDoS attacks as reported by Cloudflare , which is 20% year-over-year, the addition of Generative AI to the broader organization infrastructure adds more moving parts to an already complex setup. While moving ahead with innovation and sidelining security might look tempting at first, it is equivalent to playing with fire. One wrong move can result in disastrous results, whether lost customer trust, personal data leak, or government-imposed penalties. reported by Cloudflare reported by Cloudflare An individual must be someone other than a CISO to push for a culture shift. Often, executives rely on people reporting to them to give them advice, which in turn is asked down the reporting chain. If Engineers or program Managers can make themselves security champions, they have a high chance of shining and access to opportunities for bigger responsibilities and a career boost. The future of Generative AI hinges on our ability and willingness to secure it. Organizations that recognize this fact and act proactively will be best positioned to thrive and overcome dangers and regulatory hurdles.

Security in Generative AI Infrastructure Is of Critical Importance

The world is a much more connected place than it was 20 years ago. With the change came new expectations. Downtimes can cripple businesses. There is an expectation of privacy and security. If we post on Facebook and the post gets lost due to any kind of issue, people will not tolerate it. Data integrity and durability are more critical than ever. As an organization, we must consider multiple technological tenets—security, availability, latency, durability, integrity, reliability, and privacy. We don’t get to choose from this list; we just sort it by importance based on business needs. Business continuity Disaster can come in many flavors, ranging from natural disasters to hardware failures, misconfiguration, and cyberattacks. The problem with cyberattacks is that we risk having our data stolen and wonder if the rest of the data is trustworthy. Maybe the attack was a sleight of hand, and the primary objective was to introduce malicious data that would haunt the organization forever. After the security incident, we need to recover the data and restore the service to the level our customers expect. One of the issues we face is reconstructing the original picture and configuration. We need to understand how all the pieces work together and their interdependency. Data restoration is more than just restoring a highly scalable web service snapshot. Objective At this point, our primary objective should be to ensure that our services are available and reliable by whatever means necessary. In the context of dependency management, we need to have a comprehensive data and dependency catalog, including the relationships between the entities. This provides us with the necessary insight into what exists. We also need regular operations testing and robust disaster recovery through automation and manual runbooks. We need to cultivate a culture of disaster recovery fire drills and identify fallbacks. Maturity Model Like an individual who enters college goes through freshman, sophomore, junior, and senior years before graduating, businesses need to follow a slow, progressive approach. It’s impractical for a company or organization to go from no disaster recovery to a fully-fledged, perfectly fleshed-out plan. The road to success is incremental, and each step builds on the previous step. Maturity models examine an organization’s current state from any specific perspective, such as disaster recovery, and help the organization reach the desired state or capability. As the organization progresses toward the end goal, it goes through various checkpoints, usually called Levels. Most Maturity Models have four levels, starting from Level 1 up to Level 4. Why are we talking about maturity models? One step in such a disaster recovery maturity model requires that the organization understand dependencies among various entities and data objects. Recovery usually requires restoring the data and bringing back critical systems in a specific incremental manner, Implementation Based on the business needs, understanding dependency and its relationship can be somewhere in the middle levels of the maturity models. In a 4-level system, it is most likely in Level 2 or Level 3. The other approach is to split the Dependency Management into the same number of milestones as the number of levels. Suppose we are using a 4-level maturity model. In that case, we can split up dependency management to span all 4 Levels, starting with the most straightforward steps and going all the way to the most complicated, expensive, and involved steps. Going forward, we will only discuss the dependency management aspect of this maturity model. Level 1 - Discovering and Understanding Dependencies For many organizations, this can be the most daunting task. If dependency management was never considered necessary, there might be no institutional knowledge. We will start by discussing technical dependencies and then discuss process dependencies. Regarding technical dependencies, we need to identify the direct dependencies first. If the code is written in JavaScript and runs on a cloud service, then the direct dependencies might be JavaScript, NodeJS, AWS Lambda, etc. If we use npm for package management, the packages listed in the dependency file are direct dependencies. Next, we have transitive dependencies, which are pulled automatically from direct dependencies. In practice, this forms a directed acyclic graph. Another aspect of technical dependencies is the cloud architecture. Let’s take an example of AWS—we might use EC2, S3, API Gateway, CloudFront, ELB, etc. If the cloud resources were deployed using tools like CloudFormation or CDK, then it would be trivial to determine the dependencies. If the resources were created manually, the dependency diagram must be made manually by reading the source code and drawing conclusions. Process dependency falls under the non-technical category and defines how different stakeholders work together to move forward. It can also outline the release process or runbooks to invoke during an incident. Level 2 - Setup Continuous Dependencies Validation Dependencies can change since there are too many cooks in the kitchen. The organization's continuous integration tool should enumerate and track the direct code and library dependencies. Every time the code is pushed, a snapshot of current pinned dependencies should be saved to recreate the specific circumstances or restore to a known good state. Like the Continuous integration concept, the non-technical aspects should have some automation. Alerts should be created on metric, and using tools like PagerDuty, appropriate people should be notified of issues, and proper escalation channels should be created. It should be tested occasionally to avoid the known problem called Dogs Not Barking - a situation when you wonder if you are not getting any alerts because nothing is wrong or the alerting logic is broken. Some dependencies can be validated by using the Scream test. Under proper notification and preparation, turn off specific dependencies and see what fails. You may find a Load Bearing Mac Mini. Collect all of this configuration and manage it through some kind of provisioning tool like Chef and Terraform Level 3 - Fixing dependencies by adding, updating, or removing Once we have all the dependencies figured out, take some time and pat yourself on the back. Now, back on the issue, the work isn’t done. During the quest, you realize that some dependencies are causing more problems than they solve. One approach would be to update the dependencies. For example, if we are using old versions of OpenSSL, updating them to newer versions can solve many of the warnings raised by the build system and even help us pass any security compliance. Sometimes, the specific versions of two separate packages are incompatible. The solution is to find versions that can work together. This involves changing the version and might involve downgrading one or more packages. Sometimes, just removing a package or dependency solves the problem. It could have been a dead dependency with no further need but causing warnings and spamming the application logs. Under some circumstances, we might have to add a dependency to bring harmony. Suppose a transitive dependency is causing the problem, but we cannot update the direct dependency. In that case, explicitly adding the fixed version of the transitive dependency as a direct dependency can be an acceptable solution. Level 4 - Manual and Automated Disaster Recovery Drills This is the highest form of maturity in disaster recovery. We need runbooks that outline the roles and responsibilities of individuals involved. The runbook should also define the interdependencies and criticality of each step. The disaster recovery process should be practiced regularly by simulating a disaster and identifying possible sources of friction that get introduced when the plan is set in motion. The gold standard should be automated disaster recovery under human supervision. Automating the disaster recovery workflow ensures that humans do not make mistakes that go unnoticed. Automating has an additional advantage in that we can now run stress tests and simulation disasters by turning off certain network parts in a controlled manner to validate whether our automated disaster recovery is performing as expected. Conclusion Dependency management plays a critical role in disaster recovery, which is essential after a security incident. By following a structured and methodological approach, organizations can build their understanding progressively and control the dependencies. Each maturity model level builds on the previous one and strengthens the organization's security posture. While it can be easy to write off dependency management as a superfluous technical exercise, it is primarily about creating a comprehensive framework that brings together computer systems, people, and processes to work harmoniously. The systematic approach ensures that when disaster strikes, even in the form of a security breach, we can safely recover the data and be confident in the integrity of the said data. The world is a much more connected place than it was 20 years ago. With the change came new expectations. Downtimes can cripple businesses. There is an expectation of privacy and security. If we post on Facebook and the post gets lost due to any kind of issue, people will not tolerate it. Data integrity and durability are more critical than ever. As an organization, we must consider multiple technological tenets—security, availability, latency, durability, integrity, reliability, and privacy. We don’t get to choose from this list; we just sort it by importance based on business needs. Business continuity Disaster can come in many flavors, ranging from natural disasters to hardware failures, misconfiguration, and cyberattacks. The problem with cyberattacks is that we risk having our data stolen and wonder if the rest of the data is trustworthy. Maybe the attack was a sleight of hand, and the primary objective was to introduce malicious data that would haunt the organization forever. After the security incident, we need to recover the data and restore the service to the level our customers expect. One of the issues we face is reconstructing the original picture and configuration. We need to understand how all the pieces work together and their interdependency. Data restoration is more than just restoring a highly scalable web service snapshot. Objective At this point, our primary objective should be to ensure that our services are available and reliable by whatever means necessary. In the context of dependency management, we need to have a comprehensive data and dependency catalog, including the relationships between the entities. This provides us with the necessary insight into what exists. We also need regular operations testing and robust disaster recovery through automation and manual runbooks. We need to cultivate a culture of disaster recovery fire drills and identify fallbacks. Maturity Model Like an individual who enters college goes through freshman, sophomore, junior, and senior years before graduating, businesses need to follow a slow, progressive approach. It’s impractical for a company or organization to go from no disaster recovery to a fully-fledged, perfectly fleshed-out plan. The road to success is incremental, and each step builds on the previous step. Maturity models examine an organization’s current state from any specific perspective, such as disaster recovery, and help the organization reach the desired state or capability. As the organization progresses toward the end goal, it goes through various checkpoints, usually called Levels. Most Maturity Models have four levels, starting from Level 1 up to Level 4. Why are we talking about maturity models? One step in such a disaster recovery maturity model requires that the organization understand dependencies among various entities and data objects. Recovery usually requires restoring the data and bringing back critical systems in a specific incremental manner, Implementation Based on the business needs, understanding dependency and its relationship can be somewhere in the middle levels of the maturity models. In a 4-level system, it is most likely in Level 2 or Level 3. The other approach is to split the Dependency Management into the same number of milestones as the number of levels. Suppose we are using a 4-level maturity model. In that case, we can split up dependency management to span all 4 Levels, starting with the most straightforward steps and going all the way to the most complicated, expensive, and involved steps. Going forward, we will only discuss the dependency management aspect of this maturity model. Level 1 - Discovering and Understanding Dependencies For many organizations, this can be the most daunting task. If dependency management was never considered necessary, there might be no institutional knowledge. We will start by discussing technical dependencies and then discuss process dependencies. Regarding technical dependencies, we need to identify the direct dependencies first. If the code is written in JavaScript and runs on a cloud service, then the direct dependencies might be JavaScript, NodeJS, AWS Lambda, etc. If we use npm for package management, the packages listed in the dependency file are direct dependencies. Next, we have transitive dependencies, which are pulled automatically from direct dependencies. In practice, this forms a directed acyclic graph. Another aspect of technical dependencies is the cloud architecture. Let’s take an example of AWS—we might use EC2, S3, API Gateway, CloudFront, ELB, etc. If the cloud resources were deployed using tools like CloudFormation or CDK, then it would be trivial to determine the dependencies. If the resources were created manually, the dependency diagram must be made manually by reading the source code and drawing conclusions. Process dependency falls under the non-technical category and defines how different stakeholders work together to move forward. It can also outline the release process or runbooks to invoke during an incident. Level 2 - Setup Continuous Dependencies Validation Dependencies can change since there are too many cooks in the kitchen. The organization's continuous integration tool should enumerate and track the direct code and library dependencies. Every time the code is pushed, a snapshot of current pinned dependencies should be saved to recreate the specific circumstances or restore to a known good state. Like the Continuous integration concept, the non-technical aspects should have some automation. Alerts should be created on metric, and using tools like PagerDuty, appropriate people should be notified of issues, and proper escalation channels should be created. It should be tested occasionally to avoid the known problem called Dogs Not Barking - a situation when you wonder if you are not getting any alerts because nothing is wrong or the alerting logic is broken. Some dependencies can be validated by using the Scream test . Under proper notification and preparation, turn off specific dependencies and see what fails. You may find a Load Bearing Mac Mini . Collect all of this configuration and manage it through some kind of provisioning tool like Chef and Terraform Scream test Scream test Load Bearing Mac Mini Load Bearing Mac Mini Level 3 - Fixing dependencies by adding, updating, or removing Once we have all the dependencies figured out, take some time and pat yourself on the back. Now, back on the issue, the work isn’t done. During the quest, you realize that some dependencies are causing more problems than they solve. One approach would be to update the dependencies. For example, if we are using old versions of OpenSSL, updating them to newer versions can solve many of the warnings raised by the build system and even help us pass any security compliance. Sometimes, the specific versions of two separate packages are incompatible. The solution is to find versions that can work together. This involves changing the version and might involve downgrading one or more packages. Sometimes, just removing a package or dependency solves the problem. It could have been a dead dependency with no further need but causing warnings and spamming the application logs. Under some circumstances, we might have to add a dependency to bring harmony. Suppose a transitive dependency is causing the problem, but we cannot update the direct dependency. In that case, explicitly adding the fixed version of the transitive dependency as a direct dependency can be an acceptable solution. Level 4 - Manual and Automated Disaster Recovery Drills This is the highest form of maturity in disaster recovery. We need runbooks that outline the roles and responsibilities of individuals involved. The runbook should also define the interdependencies and criticality of each step. The disaster recovery process should be practiced regularly by simulating a disaster and identifying possible sources of friction that get introduced when the plan is set in motion. The gold standard should be automated disaster recovery under human supervision. Automating the disaster recovery workflow ensures that humans do not make mistakes that go unnoticed. Automating has an additional advantage in that we can now run stress tests and simulation disasters by turning off certain network parts in a controlled manner to validate whether our automated disaster recovery is performing as expected. Conclusion Dependency management plays a critical role in disaster recovery, which is essential after a security incident. By following a structured and methodological approach, organizations can build their understanding progressively and control the dependencies. Each maturity model level builds on the previous one and strengthens the organization's security posture. While it can be easy to write off dependency management as a superfluous technical exercise, it is primarily about creating a comprehensive framework that brings together computer systems, people, and processes to work harmoniously. The systematic approach ensures that when disaster strikes, even in the form of a security breach, we can safely recover the data and be confident in the integrity of the said data.

Dependency Management is Critical for Disaster Recovery After a Security Incident

Best Practices for Securing Cloud Environments Against Cyber Threats

As AI continues to integrate into cybersecurity frameworks, business processes and critical infrastructure, understanding and addressing its unique security risks is crucial for effective risk management. Just as the Pyramid of Pain has provided a structured way to measure the impact and difficulty of disrupting cyberattacks, the Security Pyramid of AI offers a similar framework for evaluating and mitigating the increasing challenges posed by AI-driven systems. Each layer of this pyramid represents a different set of vulnerabilities, escalating in complexity and severity, which security teams must prioritize to protect the integrity, confidentiality, and availability of AI assets. By understanding these layers, CISOs and security leaders can better align their risk mitigation strategies with the evolving AI threat landscape. Let’s explore this concept, layer by layer. AI Model Output Manipulation (Low Pain) At the base of the pyramid, we encounter the easiest-to-address attacks: manipulating AI model outputs. These involve tricking AI models into making incorrect predictions or classifications by making subtle changes to inputs. For example, an adversarial image may fool a visual recognition system by introducing barely perceptible distortions, or misleading text queries may deceive a natural language model into outputting incorrect or harmful responses. While these attacks can affect the performance of AI systems, they are often easier to identify and mitigate. Defenders can detect output anomalies and retrain models with augmented data to improve resilience. Compared to more complex threats, the response is relatively straightforward. Data Poisoning (Moderate Pain) Data poisoning represents a more significant threat: corrupting the data on which the AI model is trained. This can involve introducing incorrect, mislabeled, or malicious data into the training set, causing the model to make biased or harmful predictions. A successful data poisoning attack can degrade the model’s performance in specific areas, such as reducing its ability to detect spam or identify malware. While it’s more challenging than output manipulation, defending against data poisoning is still manageable. By implementing strict data validation processes and continuously monitoring for inconsistencies, defenders can reduce the risk. However, cleaning and retraining the model requires more effort than addressing output manipulation. Model Evasion/Bypass (Moderate to High Pain) Model evasion or bypass occurs when attackers craft inputs that escape detection by AI-powered security systems. This is particularly concerning in areas like AI-driven malware detection or intrusion detection systems. Attackers may make small tweaks to evade detection, leaving defenders scrambling to keep up. The challenge here is that adversaries need only minimal modifications to bypass AI models, while defenders must continuously evolve their detection algorithms. Each bypass increases the difficulty, as detection thresholds need refinement without introducing false positives. Model inversion attacks are more sophisticated. They involve extracting sensitive information from AI models, such as identifying whether a specific data point was part of the training set. This could allow attackers to reverse-engineer private or proprietary data, violating privacy and confidentiality. Model inversion is difficult to detect and mitigate. It often requires implementing privacy-preserving techniques like differential privacy or training models with limited data exposure. These approaches add complexity to AI development and reduce performance, making this a high-impact challenge for defenders. Model Theft/Reverse Engineering (High to Severe Pain) The next layer, model theft or reverse engineering, is a critical issue for AI-driven companies. Here, adversaries steal the AI model’s architecture or weights through techniques like API probing. Once stolen, they can replicate the model’s functionality, undermining the value of intellectual property and giving attackers insight into potential vulnerabilities. Preventing model theft requires robust API security and obfuscation techniques to hide the internal workings of the model. However, the difficulty is immense, as even with protections in place, attackers may still find ways to replicate proprietary systems, leading to severe consequences for businesses that rely on AI innovation. AI Supply Chain Attack (Severe Pain) At the top of the AI Security Pyramid of Pain, we have the most challenging and damaging threat: AI supply chain attacks. These involve compromising the development, deployment, or update process of AI systems, such as introducing vulnerabilities into pre-trained models or injecting malicious code into AI frameworks. AI supply chain attacks have the potential to compromise entire AI ecosystems. Mitigating these risks requires securing the entire AI development pipeline, from sourcing third-party tools to auditing open-source components. This is a monumental task, and a successful supply chain attack can render even the most secure AI systems vulnerable, leading to catastrophic consequences. Why This Pyramid Matters: Just as in traditional cybersecurity, understanding the layers of difficulty in securing AI systems helps us prioritize defenses. At the lower levels, threats are easier to detect and mitigate, but as we move up the pyramid, the complexity, and impact of attacks grow exponentially. Building resilience against AI security risks requires a comprehensive approach that spans from defending against adversarial examples to securing the entire AI supply chain. As AI systems become more prevalent, understanding the spectrum of risks associated with their use is critical. The “Security Pyramid of AI” provides a proposed framework for thinking about these risks, helping defenders focus their efforts where they are needed most. By recognizing and addressing each layer of this pyramid, we can work towards a more secure and resilient AI future. As AI continues to integrate into cybersecurity frameworks, business processes and critical infrastructure, understanding and addressing its unique security risks is crucial for effective risk management. Just as the Pyramid of Pain has provided a structured way to measure the impact and difficulty of disrupting cyberattacks, the Security Pyramid of AI offers a similar framework for evaluating and mitigating the increasing challenges posed by AI-driven systems. Each layer of this pyramid represents a different set of vulnerabilities, escalating in complexity and severity, which security teams must prioritize to protect the integrity, confidentiality, and availability of AI assets. By understanding these layers, CISOs and security leaders can better align their risk mitigation strategies with the evolving AI threat landscape. Security Pyramid of AI Security Pyramid of AI Let’s explore this concept, layer by layer. AI Model Output Manipulation (Low Pain) AI Model Output Manipulation (Low Pain) At the base of the pyramid, we encounter the easiest-to-address attacks: manipulating AI model outputs. These involve tricking AI models into making incorrect predictions or classifications by making subtle changes to inputs. For example, an adversarial image may fool a visual recognition system by introducing barely perceptible distortions, or misleading text queries may deceive a natural language model into outputting incorrect or harmful responses. While these attacks can affect the performance of AI systems, they are often easier to identify and mitigate. Defenders can detect output anomalies and retrain models with augmented data to improve resilience. Compared to more complex threats, the response is relatively straightforward. Data Poisoning (Moderate Pain) Data Poisoning (Moderate Pain) Data poisoning represents a more significant threat: corrupting the data on which the AI model is trained. This can involve introducing incorrect, mislabeled, or malicious data into the training set, causing the model to make biased or harmful predictions. A successful data poisoning attack can degrade the model’s performance in specific areas, such as reducing its ability to detect spam or identify malware. While it’s more challenging than output manipulation, defending against data poisoning is still manageable. By implementing strict data validation processes and continuously monitoring for inconsistencies, defenders can reduce the risk. However, cleaning and retraining the model requires more effort than addressing output manipulation. Model Evasion/Bypass (Moderate to High Pain) Model Evasion/Bypass (Moderate to High Pain) Model evasion or bypass occurs when attackers craft inputs that escape detection by AI-powered security systems. This is particularly concerning in areas like AI-driven malware detection or intrusion detection systems. Attackers may make small tweaks to evade detection, leaving defenders scrambling to keep up. The challenge here is that adversaries need only minimal modifications to bypass AI models, while defenders must continuously evolve their detection algorithms. Each bypass increases the difficulty, as detection thresholds need refinement without introducing false positives. Model inversion attacks are more sophisticated. They involve extracting sensitive information from AI models, such as identifying whether a specific data point was part of the training set. This could allow attackers to reverse-engineer private or proprietary data, violating privacy and confidentiality. Model inversion is difficult to detect and mitigate. It often requires implementing privacy-preserving techniques like differential privacy or training models with limited data exposure. These approaches add complexity to AI development and reduce performance, making this a high-impact challenge for defenders. Model Theft/Reverse Engineering (High to Severe Pain) Model Theft/Reverse Engineering (High to Severe Pain) The next layer, model theft or reverse engineering, is a critical issue for AI-driven companies. Here, adversaries steal the AI model’s architecture or weights through techniques like API probing. Once stolen, they can replicate the model’s functionality, undermining the value of intellectual property and giving attackers insight into potential vulnerabilities. Preventing model theft requires robust API security and obfuscation techniques to hide the internal workings of the model. However, the difficulty is immense, as even with protections in place, attackers may still find ways to replicate proprietary systems, leading to severe consequences for businesses that rely on AI innovation. AI Supply Chain Attack (Severe Pain) AI Supply Chain Attack (Severe Pain) At the top of the AI Security Pyramid of Pain, we have the most challenging and damaging threat: AI supply chain attacks. These involve compromising the development, deployment, or update process of AI systems, such as introducing vulnerabilities into pre-trained models or injecting malicious code into AI frameworks. AI supply chain attacks have the potential to compromise entire AI ecosystems. Mitigating these risks requires securing the entire AI development pipeline, from sourcing third-party tools to auditing open-source components. This is a monumental task, and a successful supply chain attack can render even the most secure AI systems vulnerable, leading to catastrophic consequences. Why This Pyramid Matters: Why This Pyramid Matters: Just as in traditional cybersecurity, understanding the layers of difficulty in securing AI systems helps us prioritize defenses. At the lower levels, threats are easier to detect and mitigate, but as we move up the pyramid, the complexity, and impact of attacks grow exponentially. Building resilience against AI security risks requires a comprehensive approach that spans from defending against adversarial examples to securing the entire AI supply chain. As AI systems become more prevalent, understanding the spectrum of risks associated with their use is critical. The “Security Pyramid of AI” provides a proposed framework for thinking about these risks, helping defenders focus their efforts where they are needed most. By recognizing and addressing each layer of this pyramid, we can work towards a more secure and resilient AI future. “Security Pyramid of AI” “Security Pyramid of AI”

 The Security Pyramid of AI

**TEL AVIV, Israel, November 11th, 2024/CyberNewsWire/--**Sweet Security today announced the availability of its cloud-native detection and response platform on the Amazon Web Services (AWS) marketplace. Sweet’s solution unifies threat detection across cloud infrastructure, network, workloads, and applications. It provides deep runtime context that enables security teams to quickly extract actual attack narratives from a sea of isolated incidents. Using Sweet, AWS Marketplace customers can detect active threats in real time and respond to them within minutes, enabling them to resolve threats with unprecedented speed - 2-5 minute MTTR - and maintain an agile and resilient environment. AWS customers visiting AWS re:Invent 2024 in Las Vegas can book a meeting to learn more here. “Cloud environments are noisy and complex, making them fertile grounds for attackers -- deterring them requires detection and response capabilities that, to date, have been aspirational, but we’ve made them table stakes,” said Eyal Fisher, Co-Founder and Chief Product Officer of Sweet Security and former head of the Cyber Operation Center, Unit 8200 (Col., retired). “We’re delighted that our solution is now available to AWS Marketplace. customers and look forward to helping them simplify the burden of cloud security and do their jobs faster and better.” What Makes Sweet So Sweet? Sweet Security offers detection and response for cloud native environments. Its approach is unique in how it unifies detection across cloud infrastructure, network, workloads, and applications, providing deep runtime context that cuts through the noise and delivers actual attack narratives. Key Features include: ●     Advanced threat detection and incident response (IR) across infrastructure, network, application, and workload levels. ●     Vulnerability management enriched with runtime insights, reducing CVEs by 99% and putting only the critical risks in front of security personnel. ●     Lean sensor technology that requires minimal resources (50 MB RAM, 0.20% CPU per node) and take only minutes to deploy ●     30+ out-of-the-box integrations with SIEM, SOAR, notification and ticketing systems, and mor Sweet empowers security teams to achieve a Mean Time to Detect of 30 seconds (MTTD) and a 2-5 minute Mean Time to Resolve (MTTR), transforming cloud security into a more proactive and effective discipline. For more information, users can visit Sweet Security on the AWS Marketplace. About Sweet Security Specializing in Cloud Native Detection & Response (D&R), Sweet Security protects cloud environments in real time. Founded by the IDF's former CISO, Sweet's solution focuses on the relationships between cloud infrastructure, workloads and applications , as well as network, and identity components. Leveraging a lean, eBPF-based sensor and deep behavioral analysis, Sweet analyzes anomalies, generating vital insights on incidents, vulnerabilities, and non-human identities. Its GenAI-infused technology cuts through the noise and delivers actionable recommendations on critical, real-time cloud risks. Privately funded, Sweet is backed by Evolution Equity Partners, Munich Re Ventures, Glilot Capital Partners, CyberArk Ventures and an elite group of angel investors. For more information, please visit http://sweet.security. Contact Account Director Chloe Amante Montner Tech PR camante@montner.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here. **TEL AVIV, Israel, November 11th, 2024/CyberNewsWire/--**Sweet Security today announced the availability of its cloud-native detection and response platform on the Amazon Web Services (AWS) marketplace. Sweet’s solution unifies threat detection across cloud infrastructure, network, workloads, and applications. It provides deep runtime context that enables security teams to quickly extract actual attack narratives from a sea of isolated incidents. Using Sweet, AWS Marketplace customers can detect active threats in real time and respond to them within minutes, enabling them to resolve threats with unprecedented speed - 2-5 minute MTTR - and maintain an agile and resilient environment. AWS customers visiting AWS re:Invent 2024 in Las Vegas can book a meeting to learn more here . here here “Cloud environments are noisy and complex, making them fertile grounds for attackers -- deterring them requires detection and response capabilities that, to date, have been aspirational, but we’ve made them table stakes,” said Eyal Fisher, Co-Founder and Chief Product Officer of Sweet Security and former head of the Cyber Operation Center, Unit 8200 (Col., retired). “We’re delighted that our solution is now available to AWS Marketplace. customers and look forward to helping them simplify the burden of cloud security and do their jobs faster and better.” “Cloud environments are noisy and complex, making them fertile grounds for attackers -- deterring them requires detection and response capabilities that, to date, have been aspirational, but we’ve made them table stakes,” said Eyal Fisher, Co-Founder and Chief Product Officer of Sweet Security and former head of the Cyber Operation Center, Unit 8200 (Col., retired). “We’re delighted that our solution is now available to AWS Marketplace. customers and look forward to helping them simplify the burden of cloud security and do their jobs faster and better.” What Makes Sweet So Sweet? Sweet Security offers detection and response for cloud native environments. Its approach is unique in how it unifies detection across cloud infrastructure, network, workloads, and applications, providing deep runtime context that cuts through the noise and delivers actual attack narratives. Key Features include: Key Features include: ●     Advanced threat detection and incident response (IR) across infrastructure, network, application, and workload levels. ●     Vulnerability management enriched with runtime insights, reducing CVEs by 99% and putting only the critical risks in front of security personnel. ●     Lean sensor technology that requires minimal resources (50 MB RAM, 0.20% CPU per node) and take only minutes to deploy ●     30+ out-of-the-box integrations with SIEM, SOAR, notification and ticketing systems, and mor Sweet empowers security teams to achieve a Mean Time to Detect of 30 seconds (MTTD) and a 2-5 minute Mean Time to Resolve (MTTR), transforming cloud security into a more proactive and effective discipline. For more information, users can visit Sweet Security on the AWS Marketplace. Sweet Security on the AWS Marketplace. Sweet Security on the AWS Marketplace. About Sweet Security Specializing in Cloud Native Detection & Response (D&R), Sweet Security protects cloud environments in real time. Founded by the IDF's former CISO, Sweet's solution focuses on the relationships between cloud infrastructure, workloads and applications , as well as network, and identity components. Sweet Security Sweet Security Leveraging a lean, eBPF-based sensor and deep behavioral analysis, Sweet analyzes anomalies, generating vital insights on incidents, vulnerabilities, and non-human identities. Its GenAI-infused technology cuts through the noise and delivers actionable recommendations on critical, real-time cloud risks. Privately funded, Sweet is backed by Evolution Equity Partners, Munich Re Ventures, Glilot Capital Partners, CyberArk Ventures and an elite group of angel investors. For more information, please visit http://sweet.security . http://sweet.security http://sweet.security Contact Account Director Chloe Amante Montner Tech PR camante@montner.com This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here. This story was distributed as a release by Cybernewswire under HackerNoon’s Business Blogging Program. Learn more about the program here. here . here

Sweet Security Launches Its Cloud Native Detection And Response Platform On The AWS Marketplace

Sweet Security lancerer sin Cloud Native Detection and Response Platform på AWS Marketplace

Sweet Security lanza su plataforma de detección y respuesta nativa de la nube en el mercado de AWS

Sweet Security lanseeraa pilvipohjaisen tunnistus- ja vastausalustan AWS-markkinapaikalla

Sweet Security が AWS Marketplace でクラウドネイティブ検出および対応プラットフォームを開始

Sweet Security AWS tirgū izlaiž savu mākoņa sākotnējās noteikšanas un reaģēšanas platformu

Sweet Security lanceert zijn cloud-native detectie- en responsplatform op de AWS Marketplace

Sweet Security e Thakgola Sefala sa Yona sa Leru la Setlogo sa go Lemoga le go Karabelo Mmarakeng wa AWS

Sweet Security wprowadza na rynek AWS swoją platformę wykrywania i reagowania w chmurze

Sweet Security Puyu Nativo Detección chaymanta Kutichiy Plataforma AWS qhatupi qallarichin

Sweet Security spúšťa svoju cloudovú natívnu platformu detekcie a odozvy na trhu AWS

Sweet Security Inotangisa Yayo Cloud Native Detection uye Mhinduro Platform PaIyo AWS Musika.

Sweet Security, Bulut Yerel Algılama ve Yanıt Platformunu AWS Pazarında Başlattı

As I sit here reflecting on my recent deep dive into blockchain security, I can't help but feel a mix of excitement and humility. The world of smart contract auditing has opened up before me in ways I never expected, and my journey with Cyfrin Updraft has already taught me invaluable lessons about what it truly means to be a security researcher in the Web3 space. When I first started, I thought auditing was simply about diving into code and hunting for bugs. I couldn't have been more wrong. One of my earliest and most important lessons was about the art of protocol onboarding. Before even touching a single line of code, I learned to ask the right questions: What's the project trying to achieve? What chains will it deploy to? Who are the different actors in the system? These questions aren't just formalities – they're the foundation of a thorough security review. The tools I've discovered along the way have become trusted companions in my daily work. Solidity Metrics helps me understand the complexity of what I'm dealing with, while CLOC gives me a clear picture of the codebase's scope. It's fascinating how these tools can transform an overwhelming mass of code into manageable, quantifiable work. My first real security review of the PasswordStore protocol was an eye-opening experience. Armed with my new knowledge and tools, I uncovered three distinct vulnerabilities – from critical access control issues to privacy concerns with on-chain data storage. What made this experience particularly valuable wasn't just finding the issues, but learning how to think about their impact. Is user funds at direct risk? Could this disrupt the entire protocol? These questions now guide my evaluation of every potential vulnerability I encounter. One of the most transformative aspects of my training has been learning to communicate findings effectively. It's not enough to spot a vulnerability – you need to explain it clearly, demonstrate it conclusively, and suggest practical solutions. I've learned to back every finding with solid proof of concept and clear, actionable recommendations for mitigation. The journey hasn't always been smooth. I've spent hours poring over code, sometimes feeling stuck or uncertain. But each challenge has taught me something new about the intricate world of blockchain security. I've learned that being a good security researcher isn't just about technical knowledge – it's about patience, attention to detail, and the ability to think like both a builder and a breaker. Looking ahead, I'm excited to tackle more complex protocols and continue building my skills. The blockchain space moves incredibly fast, and there's always something new to learn. But with the foundation I've built through Cyfrin Updraft, I feel well-equipped to face these challenges. This field has shown me that smart contract security is an art as much as it's a science. It's about understanding systems, thinking creatively about potential vulnerabilities, and working collaboratively with development teams to build more secure protocols. Every day brings new challenges and opportunities to learn, and I couldn't be more excited about what lies ahead. I'm grateful to be part of this journey, contributing in my own way to making Web3 more secure. As I continue to grow in this space, I look forward to sharing more insights and experiences. After all, in the world of blockchain security, we're all learning and growing together. If you're on a similar path or just getting started, I'd love to hear about your experiences. What challenges have you faced? What victories have you celebrated? Let's continue pushing the boundaries of what's possible in the world of smart contract security. You can find out more on my Codehawks Security Portfolio and my first Official Initial Audit Report. As I sit here reflecting on my recent deep dive into blockchain security, I can't help but feel a mix of excitement and humility. The world of smart contract auditing has opened up before me in ways I never expected, and my journey with Cyfrin Updraft has already taught me invaluable lessons about what it truly means to be a security researcher in the Web3 space. When I first started, I thought auditing was simply about diving into code and hunting for bugs. I couldn't have been more wrong. One of my earliest and most important lessons was about the art of protocol onboarding. Before even touching a single line of code, I learned to ask the right questions: What's the project trying to achieve? What chains will it deploy to? Who are the different actors in the system? These questions aren't just formalities – they're the foundation of a thorough security review. The tools I've discovered along the way have become trusted companions in my daily work. Solidity Metrics helps me understand the complexity of what I'm dealing with, while CLOC gives me a clear picture of the codebase's scope. It's fascinating how these tools can transform an overwhelming mass of code into manageable, quantifiable work. My first real security review of the PasswordStore protocol was an eye-opening experience. Armed with my new knowledge and tools, I uncovered three distinct vulnerabilities – from critical access control issues to privacy concerns with on-chain data storage. What made this experience particularly valuable wasn't just finding the issues, but learning how to think about their impact. Is user funds at direct risk? Could this disrupt the entire protocol? These questions now guide my evaluation of every potential vulnerability I encounter. One of the most transformative aspects of my training has been learning to communicate findings effectively. It's not enough to spot a vulnerability – you need to explain it clearly, demonstrate it conclusively, and suggest practical solutions. I've learned to back every finding with solid proof of concept and clear, actionable recommendations for mitigation. The journey hasn't always been smooth. I've spent hours poring over code, sometimes feeling stuck or uncertain. But each challenge has taught me something new about the intricate world of blockchain security. I've learned that being a good security researcher isn't just about technical knowledge – it's about patience, attention to detail, and the ability to think like both a builder and a breaker. Looking ahead, I'm excited to tackle more complex protocols and continue building my skills. The blockchain space moves incredibly fast, and there's always something new to learn. But with the foundation I've built through Cyfrin Updraft, I feel well-equipped to face these challenges. This field has shown me that smart contract security is an art as much as it's a science. It's about understanding systems, thinking creatively about potential vulnerabilities, and working collaboratively with development teams to build more secure protocols. Every day brings new challenges and opportunities to learn, and I couldn't be more excited about what lies ahead. I'm grateful to be part of this journey, contributing in my own way to making Web3 more secure. As I continue to grow in this space, I look forward to sharing more insights and experiences. After all, in the world of blockchain security, we're all learning and growing together. If you're on a similar path or just getting started, I'd love to hear about your experiences. What challenges have you faced? What victories have you celebrated? Let's continue pushing the boundaries of what's possible in the world of smart contract security. You can find out more on my Codehawks Security Portfolio and my first Official Initial Audit Report . Codehawks Security Portfolio Audit Report

My Journey as a Smart Contract Security Researcher: First Steps with Cyfrin Updraft

Stories, News & Mentions about SECCURI

Seccuri’s Stories on HackerNoon