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Aylsa is an integral part of the team at her current store on Carely Hill Road, taking part in events for Aldi's partner Teenage Cancer Trust, and providing support to her colleagues.

Aldi staff member in Sunderland celebrates 20 year milestone with the supermarket chain

Authors:
(1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA;
(2) Abhinav Tuli, Activeloop, Mountain View, CA, USA;
(3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA;
(4) Fariz Rahman, Activeloop, Mountain View, CA, USA;.
(5) Hrant Topchyan, Activeloop, Mountain View, CA, USA;
(6) David Isayan, Activeloop, Mountain View, CA, USA;
(7) Mark McQuade, Activeloop, Mountain View, CA, USA;
(8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA;
(9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA;
(10) Ivo Stranic, Activeloop, Mountain View, CA, USA;
(11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro
Current Challenges
Tensor Storage Format
Deep Lake System Overview
Machine Learning Use Cases
Performance Benchmarks
Discussion and Limitations
Related Work
Conclusions, Acknowledgement, and References 5. MACHINE LEARNING USE CASES In this section, we review the applications of Deep Lake. A typical scenario in a Deep Learning application starts with (1) A raw set of files that is collected on an object storage bucket. It might include images, videos, and other types of multimedia data in their native formats such as JPEG, PNG or MP4. (2) Any associated metadata and labels stored on a relational database. Optionally, they could be stored on the same bucket along with the raw data in a normalized tabular form such as CSV, JSON, or Parquet format. As shown in Fig. 4, an empty Deep Lake dataset is created. Then, empty tensors are defined for storing both raw data as well as metadata. The number of tensors could be arbitrary. A basic example of an image classification task would have two tensors, • images tensor with htype of 𝑖𝑚𝑎𝑔𝑒 and sample compression of JPEG • labels tensor with htype of 𝑐𝑙𝑎𝑠𝑠_𝑙𝑎𝑏𝑒𝑙 and chunk compression of LZ4. After declaring tensors, the data can be appended to the dataset. If a raw image compression matches the tensor sample compression, the binary is directly copied into a chunk without additional decoding. Label data is extracted from a SQL query or CSV table into a categorical integer and appended into labels tensor. labels tensor chunks are stored using LZ4 compression. All Deep Lake data is stored in the bucket and is self-contained. After storage, the data can be accessed in a NumPy interface or as a streamable deep learning dataloader. Then, the model running on a compute machine iterates over the stream of image tensors, and stores the output of the model in a new tensor called predictions. Furthermore, we discuss below how one can train, version control, query, and inspect the quality of a Deep Lake dataset. 5.1 Deep Learning Model Training Deep learning models are trained at multiple levels in an organization, ranging from exploratory training occurring on personal computers to large-scale training that occurs on distributed machines involving many GPUs. The time and effort required to bring the data from long-term storage to the training client are often comparable to the training itself. Deep Lake solves this problem by enabling rapid streaming of data without bottlenecking the downstream training process, thus avoiding the cost and time required to duplicate data on local storage. 5.2 Data Lineage and Version Control Deep learning data constantly evolve as new data is added and existing data is quality controlled. Analytical and training workloads occur in parallel while the data is changing. Hence, knowing which data version was used by a given workload is critical to understand the relationship between the data and model performance. Deep Lake enables deep learning practitioners to understand which version of their data was used in any analytical workload and to time travel across these versions if an audit is required. Since all data is mutable, it can be edited to meet compliance-related privacy requirements. Like Git for code, Deep Lake also introduces the concept of data branches, allowing experimentation and editing of data without affecting colleagues’ work. 5.3 Data Querying and Analytics Training of deep learning models rarely occurs on all data collected by an organization for a particular application. Training datasets are often constructed by filtering the raw data based on conditions increasing model performance, which often includes data balancing, eliminating redundant data, or selecting data that contains specific features. Deep Lake provides the tools to query and analyze data so that deep learning engineers can create datasets yielding the highest accuracy models. 5.4 Data Inspection and Quality Control Though unsupervised learning is becoming more applicable in realworld use cases, most deep learning applications still rely on supervised learning. Any supervised learning system is only as good as the quality of its data, often achieved by manual and exhaustive inspection of the data. Since this process is time-consuming, it is critical to provide the humans in the loop with tools to examine vast amounts of data very quickly. Deep Lake allows inspecting deep learning datasets of any size from the browser without any setup time or need to download data. Furthermore, the tools can be extended for comparing model results with ground truth. Combined with querying and version control, this can be applied to the iterative improvement of data to achieve the best possible model. This paper is available on arxiv under CC 4.0 license. Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Authors: Authors: (1) Sasun Hambardzumyan, Activeloop, Mountain View, CA, USA; (2) Abhinav Tuli, Activeloop, Mountain View, CA, USA; (3) Levon Ghukasyan, Activeloop, Mountain View, CA, USA; (4) Fariz Rahman, Activeloop, Mountain View, CA, USA;. (5) Hrant Topchyan, Activeloop, Mountain View, CA, USA; (6) David Isayan, Activeloop, Mountain View, CA, USA; (7) Mark McQuade, Activeloop, Mountain View, CA, USA; (8) Mikayel Harutyunyan, Activeloop, Mountain View, CA, USA; (9) Tatevik Hakobyan, Activeloop, Mountain View, CA, USA; (10) Ivo Stranic, Activeloop, Mountain View, CA, USA; (11) Davit Buniatyan, Activeloop, Mountain View, CA, USA. Table of Links Abstract and Intro Current Challenges Tensor Storage Format Deep Lake System Overview Machine Learning Use Cases Performance Benchmarks Discussion and Limitations Related Work Conclusions, Acknowledgement, and References Abstract and Intro Abstract and Intro Current Challenges Current Challenges Tensor Storage Format Tensor Storage Format Deep Lake System Overview Deep Lake System Overview Machine Learning Use Cases Machine Learning Use Cases Performance Benchmarks Performance Benchmarks Discussion and Limitations Discussion and Limitations Related Work Related Work Conclusions, Acknowledgement, and References Conclusions, Acknowledgement, and References 5. MACHINE LEARNING USE CASES In this section, we review the applications of Deep Lake. A typical scenario in a Deep Learning application starts with (1) A raw set of files that is collected on an object storage bucket. It might include images, videos, and other types of multimedia data in their native formats such as JPEG, PNG or MP4. (2) Any associated metadata and labels stored on a relational database. Optionally, they could be stored on the same bucket along with the raw data in a normalized tabular form such as CSV, JSON, or Parquet format. As shown in Fig. 4, an empty Deep Lake dataset is created. Then, empty tensors are defined for storing both raw data as well as metadata. The number of tensors could be arbitrary. A basic example of an image classification task would have two tensors, • images tensor with htype of 𝑖𝑚𝑎𝑔𝑒 and sample compression of JPEG • labels tensor with htype of 𝑐𝑙𝑎𝑠𝑠_𝑙𝑎𝑏𝑒𝑙 and chunk compression of LZ4. After declaring tensors, the data can be appended to the dataset. If a raw image compression matches the tensor sample compression, the binary is directly copied into a chunk without additional decoding. Label data is extracted from a SQL query or CSV table into a categorical integer and appended into labels tensor. labels tensor chunks are stored using LZ4 compression. All Deep Lake data is stored in the bucket and is self-contained. After storage, the data can be accessed in a NumPy interface or as a streamable deep learning dataloader. Then, the model running on a compute machine iterates over the stream of image tensors, and stores the output of the model in a new tensor called predictions. Furthermore, we discuss below how one can train, version control, query, and inspect the quality of a Deep Lake dataset. 5.1 Deep Learning Model Training Deep learning models are trained at multiple levels in an organization, ranging from exploratory training occurring on personal computers to large-scale training that occurs on distributed machines involving many GPUs. The time and effort required to bring the data from long-term storage to the training client are often comparable to the training itself. Deep Lake solves this problem by enabling rapid streaming of data without bottlenecking the downstream training process, thus avoiding the cost and time required to duplicate data on local storage. 5.2 Data Lineage and Version Control Deep learning data constantly evolve as new data is added and existing data is quality controlled. Analytical and training workloads occur in parallel while the data is changing. Hence, knowing which data version was used by a given workload is critical to understand the relationship between the data and model performance. Deep Lake enables deep learning practitioners to understand which version of their data was used in any analytical workload and to time travel across these versions if an audit is required. Since all data is mutable, it can be edited to meet compliance-related privacy requirements. Like Git for code, Deep Lake also introduces the concept of data branches, allowing experimentation and editing of data without affecting colleagues’ work. 5.3 Data Querying and Analytics Training of deep learning models rarely occurs on all data collected by an organization for a particular application. Training datasets are often constructed by filtering the raw data based on conditions increasing model performance, which often includes data balancing, eliminating redundant data, or selecting data that contains specific features. Deep Lake provides the tools to query and analyze data so that deep learning engineers can create datasets yielding the highest accuracy models. 5.4 Data Inspection and Quality Control Though unsupervised learning is becoming more applicable in realworld use cases, most deep learning applications still rely on supervised learning. Any supervised learning system is only as good as the quality of its data, often achieved by manual and exhaustive inspection of the data. Since this process is time-consuming, it is critical to provide the humans in the loop with tools to examine vast amounts of data very quickly. Deep Lake allows inspecting deep learning datasets of any size from the browser without any setup time or need to download data. Furthermore, the tools can be extended for comparing model results with ground truth. Combined with querying and version control, this can be applied to the iterative improvement of data to achieve the best possible model. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Deep Lake, a Lakehouse for Deep Learning: Machine Learning Use Cases

ডিপ লেক, গভীর শিক্ষার জন্য একটি লেকহাউস: মেশিন লার্নিং ব্যবহারের ক্ষেত্রে

Deep Lake, ein Lakehouse für Deep Learning: Anwendungsfälle für maschinelles Lernen

Deep Lake, una casa de lago para el aprendizaje profundo: casos de uso del aprendizaje automático

Deep Lake, un Lakehouse pour le Deep Learning : cas d'utilisation du Machine Learning

डीप लेक, डीप लर्निंग के लिए एक लेकहाउस: मशीन लर्निंग उपयोग के मामले

ディープラーニングのためのレイクハウス、Deep Lake: 機械学習のユースケース

딥 러닝을 위한 레이크하우스인 Deep Lake: 머신 러닝 사용 사례

Deep Lake, um lago para aprendizado profundo: casos de uso de aprendizado de máquina

Deep Lake, дом для глубокого обучения: примеры использования машинного обучения

Deep Lake, Derin Öğrenme için Bir Göl Evi: Makine Öğrenimi Kullanım Örnekleri

Deep Lake, Lakehouse cho Deep Learning: Các trường hợp sử dụng Machine Learning

Deep Lake，深度学习的 Lakehouse：机器学习用例

Meet the Winners of Startups in North and Central America

Businesses today face the challenges of external threats, but internal threats are not left out either. An organization can suffer from dangers within; it could be through devious goals or carelessness. Insider threats can cause noticeable damage to the operation, finances, and reputation of a company. A Cybersecurity Insiders report reveals that 74% of companies are at least moderately vulnerable to insider threats. This piece analyzes practical steps to protect your business against these inward threats safeguarding an organization's security position and strength. Internal Threats Awareness Businesses need to be highly concerned about insider threats, as they exploit the security from within. Understanding insider threats is important as there is no one-size-fits-all definition of "insider threat," but rather several different forms in which it can manifest. Types of Insider Threats - Malicious Insiders: This is a threat that is caused by dishonest insiders who try to harm the organization on purpose by leaking confidential information, stealing data, infiltrating systems for their benefit, or causing extensive damage to the company. - Negligent Insiders: Careless insiders inadvertently harm as a subset of this type causes damage due to being unaware and faulty. They might manage sensitive data poorly, become prey to phishing scams, or simply fail to follow security procedures leading to unintended data breaches. - Compromised Insiders: These are valuable (and exploitable) employees whose credentials have been disclosed by predators on the web. Since the threat actors have already got their access to do potential damage, there is no need for a direct connection! Understanding Motives and Triggers Behind Insider Threats - Financial Gain: Your employees may even be tempted to sell sensitive information out of financial gains and other similar fraudulent activities. - Retaliation: Disgruntled employees who are unhappy in their employment, or perceive themselves as being treated unfairly might seek to retaliate. - Ideological Beliefs: An actor launches an insider attack as a result of ideological beliefs (e.g., to affect company X in order that it endorse position Y). - Lack of Awareness: Insiders who just do not know are a different matter, needless breaches caused simply by lack of awareness and training. - Outside Threat: Cyber criminals can blackmail or force employees to collude with them in their plans. Employees can be blackmailed or forced to assist cyber attackers with their malicious activities. Identifying Potential Insider Threats You need to be able to identify what these insider threats look like; this way, you can take the necessary steps to prevent them within your business. Here is how to spot the signs and make it right before it's too late. Insider threat-susceptible employees have various behavioral changes that raise flags. Look out for recurring arguments with co-workers, increased stress, and immediate financial issues accompanied by mysterious loyalty shifts. These behaviors might point to someone likely to be a threat due to external influences or personal problems. There are indications that the work may be facing a new threat. If you detect anything which may include logging in at odd hours of the day or using authorized devices as needed to access critical data, such irregular trends can help locate trouble before it even becomes a hurdle. Observation is key in the identification of insider threats. Using watchdog tools monitors the movement of files, emails, and odd activities. Maintaining employee confidentiality is key, but as long as monitoring practices are fair and transparent they can end up serving both the employees' mutual interests as well as those of the company. Operational data analytics and machine learning are advanced technologies that can search through a mass depository of data, allowing for the identification of anomalous activities above what any number of human observers could ever locate. Regularly training the models on new data is likely to make them more effective at identifying potential risks. Implementing Effective Security Policies To ensure the protection of sensitive data and continuity in business, establishing an effective security policy is essential. You can implement these policies efficiently through: 1. Creating a Sophisticated Insider Threat Detection Program Develop a comprehensive insider threat detection program. This has to do with monitoring the activities of employees by using advanced analytics to identify irregularities and set up alarms for suspicious activities. Being proactive will recognize threats before they cause danger. 2. Establishing Clear Policies and Procedures for Handling Sensitive Data It is important to have clear policies and methods for managing sensitive information. Specify what makes a piece of information sensitive and make rules for its entry, storage, and transfer. Ensure all employees understand these rules and the importance of complying with them. 3. The Importance of Background Checks and Vetting Processes Carrying out thorough checks and examination processes is important when it comes to cybersecurity for small businesses and big organizations too. Before hiring, perform a thorough background check, and verify the integrity and reliability of potential employees. Review these checks regularly to ensure continuous loyalty. 4. Regularly Updating and Enforcing Security Protocols Safety measures should be flexible, not static. Update your safety measures often to cater to new threats and vulnerabilities. A periodic training session should be conducted to keep employees updated on the latest security practices and ensure compliance with standard procedures. Employee Education and Training Consistent cybersecurity awareness training sessions keep employees updated on new threats and best practices to avoid them. These sessions should be very engaging and updated frequently to address new security challenges. Employees must understand why cybersecurity and data protection are vital. By highlighting the potential risks and impacts of data breaches, you can foster a sense of responsibility and vigilance among your staff. Encourage a sense of responsibility and caution among your employees by making employees understand the importance of data protection and cyber security, and also enlighten them on the consequences that come with cyber threats. Establishing a culture of security involves incorporating cybersecurity practices into day-to-day routine. There should be open discussions with employees about security and good practices, ensuring that everyone from the top to entry-level employees stays committed to maintaining a secure workspace. Technological Solutions to Mitigate Insider Threats Leveraging these advanced cybersecurity tools and software can help protect your business from cyber threats. 1. Utilizing Advanced Cybersecurity Tools and Software For identifying and getting rid of insider threats, advanced cybersecurity tools are integral to providing comprehensive protection by observing, identifying, and responding to questionable activities within your network. 2. Intrusion Detection Systems (IDS) IDS monitors network traffic for irregular activities and keeps you alert to potential insider threats. By finding anomalies, IDS facilitates fast response to prevent damage. 3. Data Loss Prevention (DLP) Solutions DLP solutions protect sensitive information from being accessed or shared inappropriately. It monitors data transfers and ensures that critical information remains secure, reducing the risk of insider threats. 4. User and Entity Behavior Analytics (UEBA) User and Entity Behavior Analytics tools examine the behavior of users and entities within your network. By establishing baseline behavior patterns, UEBA can detect deviations from normal behaviors that may indicate insider threats; this proactive approach allows you to address issues before they escalate. Response and Recovery Strategies Here’s a simple guide to developing effective response and recovery strategies for insider threats. 1. Developing an Incident Response Plan Creating a customized incident response plan specific to insider threats is essential. This plan should outline clear steps and assign roles to team members to ensure a swift and organized response. 2. Immediate Steps After Identifying an Insider Threat After detecting an insider threat, act fast; isolate affected systems, change access credentials, and begin an internal investigation. Quick action can prevent further damage and loss. 3. Communicating With Affected Parties In this situation, effective communication is very important. Bring it to the attention of both customers and employees and be transparent about the incident immediately. Addressing the issue without avoidance can help manage public relations and maintain trust. 4. Post-Incident Analysis and Future Defense After finding a solution to the incident, carry out a post-incident analysis to identify the root cause and security vulnerabilities. Use these insights to strengthen your defenses and update your incident response plan to protect your business in the future. Safeguarding Your Business From Insider Threats To ensure the security of your business from insider threats you need a proactive approach that should also be an adequate mix of tough policies, advanced technologies, and constant employee treason. Insight into the various forms of insider threats, combined with robustly executed security and a vigilant culture can significantly mitigate those risks. Be vigilant, prioritize security, and continually update your approaches to ensure that your business is well-protected inside as well as outside. Feature image source Businesses today face the challenges of external threats, but internal threats are not left out either. An organization can suffer from dangers within; it could be through devious goals or carelessness. Insider threats can cause noticeable damage to the operation, finances, and reputation of a company. A Cybersecurity Insiders report reveals that 74% of companies are at least moderately vulnerable to insider threats. This piece analyzes practical steps to protect your business against these inward threats safeguarding an organization's security position and strength. 74% of companies Internal Threats Awareness Businesses need to be highly concerned about insider threats, as they exploit the security from within. Understanding insider threats is important as there is no one-size-fits-all definition of "insider threat," but rather several different forms in which it can manifest. Types of Insider Threats - Malicious Insiders : This is a threat that is caused by dishonest insiders who try to harm the organization on purpose by leaking confidential information, stealing data, infiltrating systems for their benefit, or causing extensive damage to the company. - Malicious Insiders - Negligent Insiders : Careless insiders inadvertently harm as a subset of this type causes damage due to being unaware and faulty. They might manage sensitive data poorly, become prey to phishing scams, or simply fail to follow security procedures leading to unintended data breaches. - Negligent Insiders - Compromised Insiders : These are valuable (and exploitable) employees whose credentials have been disclosed by predators on the web. Since the threat actors have already got their access to do potential damage, there is no need for a direct connection! - Compromised Insiders Understanding Motives and Triggers Behind Insider Threats - Financial Gain : Your employees may even be tempted to sell sensitive information out of financial gains and other similar fraudulent activities. - Financial Gain - Retaliation: Disgruntled employees who are unhappy in their employment, or perceive themselves as being treated unfairly might seek to retaliate. - Retaliation: - Ideological Beliefs : An actor launches an insider attack as a result of ideological beliefs (e.g., to affect company X in order that it endorse position Y). - Ideological Beliefs - Lack of Awareness: Insiders who just do not know are a different matter, needless breaches caused simply by lack of awareness and training. - Lack of Awareness: - Outside Threat : Cyber criminals can blackmail or force employees to collude with them in their plans. Employees can be blackmailed or forced to assist cyber attackers with their malicious activities. - Outside Threat Identifying Potential Insider Threats Identifying Potential Insider Threats You need to be able to identify what these insider threats look like; this way, you can take the necessary steps to prevent them within your business. Here is how to spot the signs and make it right before it's too late. Insider threat-susceptible employees have various behavioral changes that raise flags. Look out for recurring arguments with co-workers, increased stress, and immediate financial issues accompanied by mysterious loyalty shifts. These behaviors might point to someone likely to be a threat due to external influences or personal problems. There are indications that the work may be facing a new threat. If you detect anything which may include logging in at odd hours of the day or using authorized devices as needed to access critical data, such irregular trends can help locate trouble before it even becomes a hurdle. Observation is key in the identification of insider threats. Using watchdog tools monitors the movement of files, emails, and odd activities. Maintaining employee confidentiality is key, but as long as monitoring practices are fair and transparent they can end up serving both the employees' mutual interests as well as those of the company. Operational data analytics and machine learning are advanced technologies that can search through a mass depository of data, allowing for the identification of anomalous activities above what any number of human observers could ever locate. Regularly training the models on new data is likely to make them more effective at identifying potential risks. Implementing Effective Security Policies To ensure the protection of sensitive data and continuity in business, establishing an effective security policy is essential. You can implement these policies efficiently through: 1. Creating a Sophisticated Insider Threat Detection Program Develop a comprehensive insider threat detection program. This has to do with monitoring the activities of employees by using advanced analytics to identify irregularities and set up alarms for suspicious activities. Being proactive will recognize threats before they cause danger. 2. Establishing Clear Policies and Procedures for Handling Sensitive Data It is important to have clear policies and methods for managing sensitive information. Specify what makes a piece of information sensitive and make rules for its entry, storage, and transfer. Ensure all employees understand these rules and the importance of complying with them. 3. The Importance of Background Checks and Vetting Processes Carrying out thorough checks and examination processes is important when it comes to cybersecurity for small businesses and big organizations too. Before hiring, perform a thorough background check, and verify the integrity and reliability of potential employees. Review these checks regularly to ensure continuous loyalty. cybersecurity for small businesses 4. Regularly Updating and Enforcing Security Protocols Safety measures should be flexible, not static. Update your safety measures often to cater to new threats and vulnerabilities. A periodic training session should be conducted to keep employees updated on the latest security practices and ensure compliance with standard procedures. Employee Education and Training Consistent cybersecurity awareness training sessions keep employees updated on new threats and best practices to avoid them. These sessions should be very engaging and updated frequently to address new security challenges. Employees must understand why cybersecurity and data protection are vital. By highlighting the potential risks and impacts of data breaches, you can foster a sense of responsibility and vigilance among your staff. Encourage a sense of responsibility and caution among your employees by making employees understand the importance of data protection and cyber security, and also enlighten them on the consequences that come with cyber threats. Establishing a culture of security involves incorporating cybersecurity practices into day-to-day routine. There should be open discussions with employees about security and good practices, ensuring that everyone from the top to entry-level employees stays committed to maintaining a secure workspace. Technological Solutions to Mitigate Insider Threats Leveraging these advanced cybersecurity tools and software can help protect your business from cyber threats. 1. Utilizing Advanced Cybersecurity Tools and Software For identifying and getting rid of insider threats, advanced cybersecurity tools are integral to providing comprehensive protection by observing, identifying, and responding to questionable activities within your network. 2. Intrusion Detection Systems (IDS) IDS monitors network traffic for irregular activities and keeps you alert to potential insider threats. By finding anomalies, IDS facilitates fast response to prevent damage. 3. Data Loss Prevention (DLP) Solutions DLP solutions protect sensitive information from being accessed or shared inappropriately. It monitors data transfers and ensures that critical information remains secure, reducing the risk of insider threats. 4. User and Entity Behavior Analytics (UEBA) User and Entity Behavior Analytics (UEBA) User and Entity Behavior Analytics tools examine the behavior of users and entities within your network. By establishing baseline behavior patterns, UEBA can detect deviations from normal behaviors that may indicate insider threats; this proactive approach allows you to address issues before they escalate. Response and Recovery Strategies Here’s a simple guide to developing effective response and recovery strategies for insider threats. 1. Developing an Incident Response Plan Creating a customized incident response plan specific to insider threats is essential. This plan should outline clear steps and assign roles to team members to ensure a swift and organized response. 2. Immediate Steps After Identifying an Insider Threat After detecting an insider threat, act fast; isolate affected systems, change access credentials, and begin an internal investigation. Quick action can prevent further damage and loss. 3. Communicating With Affected Parties In this situation, effective communication is very important. Bring it to the attention of both customers and employees and be transparent about the incident immediately. Addressing the issue without avoidance can help manage public relations and maintain trust. 4. Post-Incident Analysis and Future Defense After finding a solution to the incident, carry out a post-incident analysis to identify the root cause and security vulnerabilities. Use these insights to strengthen your defenses and update your incident response plan to protect your business in the future. Safeguarding Your Business From Insider Threats To ensure the security of your business from insider threats you need a proactive approach that should also be an adequate mix of tough policies, advanced technologies, and constant employee treason. Insight into the various forms of insider threats, combined with robustly executed security and a vigilant culture can significantly mitigate those risks. Be vigilant, prioritize security, and continually update your approaches to ensure that your business is well-protected inside as well as outside. Feature image source Feature image source source

How to Protect Your Business From Insider Threats

The Snowflake Hack and Its Domino Effect

স্নোফ্লেক হ্যাক এবং এর ডমিনো প্রভাব

Der Snowflake-Hack und sein Dominoeffekt

El truco del copo de nieve y su efecto dominó

Le Snowflake Hack et son effet domino

स्नोफ्लेक हैक और इसका डोमिनो प्रभाव

スノーフレークハッキングとそのドミノ効果

눈송이 해킹과 도미노 효과

O truque do floco de neve e seu efeito dominó

Взлом Snowflake и его эффект домино

Kar Tanesi Hilesi ve Domino Etkisi

Hack bông tuyết và hiệu ứng Domino của nó

雪花黑客及其多米诺骨牌效应

Ask These 10 Questions to Transform Your Leadership Approach

Investigating Ad Targeting on YouTube: Implications and Legal Analysis

The need for cybersecurity data loss prevention goes beyond a simple compliance checkbox in the constantly changing world of threats. It is now your first line of defense against potentially disastrous violations and the threat of impending fines from the authorities. The cybersecurity market is expected to grow to $300 billion in 2024. Protecting sensitive data and strengthening an organization's ability to withstand a successful cyberattack are the main goals of cyber hygiene. This idea is similar to personal hygiene, in which people do routinely advised behaviors like washing their hands to prevent infections and flossing to prevent cavities. Similarly, companies may maintain their digital health by continuously implementing preventative cyber hygiene practices designed to lessen the likelihood of security incidents and data breaches. Prioritizing cyber resilience and hygiene is becoming increasingly important as the digital era develops. It may have been enough in the past to use caution when sharing information and to be aware of how you use passwords. But with the increasing complexity of assaults, even seemingly insignificant weaknesses can put people, businesses, and organizations in danger. An organization's ability to withstand, respond to, and recover from cyberattacks is known as cyber resilience, and it takes center stage. Guaranteeing a strong defense against changing threats entails putting proactive defensive measures into place, raising staff knowledge, keeping patches up-to-date, encrypting data, and encouraging teamwork. 1. Recognizing Scam Patterns Understanding threat trends is essential to creating a future of cybersecurity that is more secure. Companies and organizations must understand the scope and origins of risks and their types and sources. It is critical to understand the significance of data loss and its effects on business operations, regardless of the source—unauthorized access, unintentional deletion, change, or disclosure of sensitive information in various ways, whether purposeful or unintended. Businesses need to understand tips to protect themselves from sudden attacks and recognize typical scam behaviors that endanger their data so they can take precautions against possible threats. For example, Juniper Research’s study on combating online payment fraud forecasts global losses exceeding at least $343 billion until 2027. A cohesive and proactive strategy is required to minimize cyber risks and avoid data loss. Business Email Compromise (BEC) To access a business network, many proficient cybercriminals do not rely on vulnerabilities. Phishing scams are a common way for users to be tricked into voluntarily giving up their login credentials in Business Email Compromise (BEC) attacks. Studies show that Business Email Compromise (BEC) assaults have increased significantly over the previous two years, rising by over 175% and more than 81% in the last year. Significantly, there has been a 145% increase in malicious emails targeting Small and Medium-sized Businesses (SMBs). To trick the firm, malevolent actors assume the identities of senior executives, often the CEO or CFO, in a Business Email Compromise (BEC) assault. The typical goals of these impostors are to start fraudulent financial transactions, get private information, or provide orders that appear genuine at first. The risk associated with business email compromise (BEC) attacks stems from the unusual degree of legitimacy that the perpetrators project through their communications, many of which involve the use of phony email accounts. Spear Phishing While spear phishing is a more advanced version of the classic phishing approach, phishing is still a popular tactic used by malware developers. Spear phishing is a highly targeted technique as opposed to the indiscriminate nature of generic phishing, vishing, and smishing, in which malicious actors mimic official communications from trusted entities to trick a random recipient into clicking a link, downloading an attachment, or divulging sensitive information. These communications frequently convey urgent justifications and pressure on victims to provide private information. To reach a fake website and be asked to input passwords, account numbers, PINs, and access codes, targets are encouraged to open a malicious file or click on a link. Under the pretense of wanting to access uploaded images, an attacker may ask for usernames and passwords for several services, including Facebook, while assuming the identity of a friend. Malvertising Malvertising is a tactic used by hackers to disseminate harmful advertisements by taking advantage of internet advertising networks. When users click on these dangerous advertisements, their devices may unintentionally acquire malware or ransomware. Malvertising is a tactic used by malicious actors to add a false aspect to their assaults by preying on people's trust in well-known websites. The majority of malvertising campaigns purchase ad space from reliable ad networks, guaranteeing that their malicious advertisements appear on trustworthy websites. Even though these advertisements seem innocent, they include malicious code that attacks victims as soon as the website loads their assaults. Tech Support Scam Even though tech support scams appear to be out of date, they nevertheless exist and fool many people. Cybercriminals contact people under the guise of tech support professionals and claim to have problems with their machines in this scam. They demand action quickly and frequently demand payment or remote access to the victim's device to infect it with malware or steal data. Users are especially vulnerable to this kind of danger because of the psychological manipulation and exploitation of their anxiety and lack of technical understanding. 2. Cyber Hygiene’s Best Practices To Prevent Data Loss Implement a Strong Automated Backup System To prevent major data loss or business interruption even in the event of a cyber disaster, IT managers must take a proactive approach to cyber hygiene. This entails backing up data on a regular and consistent basis to aid in recovery in the case of an attack. To protect against any data loss, it is essential to keep these backups offline and in several places. Furthermore, to patch any vulnerabilities and remain ahead of emerging threats, it is crucial to regularly upgrade all systems, software, and applications. Constant Learning and Instruction Humans continue to be the most important aspect of cybersecurity because of their mental processes, perception, hesitation, ignorance, and mood swings. This is true regardless of how strong your technology protections are. Human mistakes have long been recognized as a major cybersecurity risk and vulnerability. It is essential to prioritize ongoing security awareness education as a result. To minimize the danger of careless insider threats and to keep your staff up to date on the most recent risks, hold frequent training sessions. Use realistic phishing simulations to assess readiness and improve abilities. Multi-Factor Authentication (MFA) By requesting two or more verification methods from users to get access, MFA improves security. Preventing unwanted access is extremely successful with this method. Without the additional authentication element, fraudsters are unable to further their attempts, even if they are successful in obtaining login credentials. Implement Endpoint Security Invest in endpoint security solutions that manage and keep an eye on every endpoint to strengthen your network's defenses. Fortify the network against possible breaches by deploying powerful endpoint protection technologies that integrate machine learning and behavioral analysis to detect and stop malicious activity in real-time. Conclusion Keeping up strong cybersecurity procedures is critical in the current digital environment. Adding layers of protection against changing cyber threats requires the adoption of proactive solutions like multi-factor authentication, constant education, and strong endpoint security. To protect sensitive data and guarantee a robust and secure digital environment, people and organizations need to maintain awareness, give priority to cyber hygiene, and put in place efficient security measures. The need for cybersecurity data loss prevention goes beyond a simple compliance checkbox in the constantly changing world of threats. It is now your first line of defense against potentially disastrous violations and the threat of impending fines from the authorities. The cybersecurity market is expected to grow to $300 billion in 2024. Protecting sensitive data and strengthening an organization's ability to withstand a successful cyberattack are the main goals of cyber hygiene. This idea is similar to personal hygiene, in which people do routinely advised behaviors like washing their hands to prevent infections and flossing to prevent cavities. Similarly, companies may maintain their digital health by continuously implementing preventative cyber hygiene practices designed to lessen the likelihood of security incidents and data breaches. Prioritizing cyber resilience and hygiene is becoming increasingly important as the digital era develops. It may have been enough in the past to use caution when sharing information and to be aware of how you use passwords. But with the increasing complexity of assaults, even seemingly insignificant weaknesses can put people, businesses, and organizations in danger. An organization's ability to withstand, respond to, and recover from cyberattacks is known as cyber resilience, and it takes center stage. Guaranteeing a strong defense against changing threats entails putting proactive defensive measures into place, raising staff knowledge, keeping patches up-to-date, encrypting data, and encouraging teamwork. 1. Recognizing Scam Patterns 1. Recognizing Scam Patterns Understanding threat trends is essential to creating a future of cybersecurity that is more secure. Companies and organizations must understand the scope and origins of risks and their types and sources. It is critical to understand the significance of data loss and its effects on business operations, regardless of the source—unauthorized access, unintentional deletion, change, or disclosure of sensitive information in various ways, whether purposeful or unintended. Businesses need to understand tips to protect themselves from sudden attacks and recognize typical scam behaviors that endanger their data so they can take precautions against possible threats. to protect themselves For example, Juniper Research’s study on combating online payment fraud forecasts global losses exceeding at least $343 billion until 2027 . A cohesive and proactive strategy is required to minimize cyber risks and avoid data loss. $343 billion until 2027 Business Email Compromise (BEC) Business Email Compromise (BEC) To access a business network, many proficient cybercriminals do not rely on vulnerabilities. Phishing scams are a common way for users to be tricked into voluntarily giving up their login credentials in Business Email Compromise (BEC) attacks. Studies show that Business Email Compromise (BEC) assaults have increased significantly over the previous two years, rising by over 175% and more than 81% in the last year. Significantly, there has been a 145% increase in malicious emails targeting Small and Medium-sized Businesses (SMBs). Studies show To trick the firm, malevolent actors assume the identities of senior executives, often the CEO or CFO, in a Business Email Compromise (BEC) assault. The typical goals of these impostors are to start fraudulent financial transactions, get private information, or provide orders that appear genuine at first. The risk associated with business email compromise (BEC) attacks stems from the unusual degree of legitimacy that the perpetrators project through their communications, many of which involve the use of phony email accounts. Spear Phishing Spear Phishing While spear phishing is a more advanced version of the classic phishing approach, phishing is still a popular tactic used by malware developers. Spear phishing is a highly targeted technique as opposed to the indiscriminate nature of generic phishing, vishing, and smishing, in which malicious actors mimic official communications from trusted entities to trick a random recipient into clicking a link, downloading an attachment, or divulging sensitive information. These communications frequently convey urgent justifications and pressure on victims to provide private information. To reach a fake website and be asked to input passwords, account numbers, PINs, and access codes, targets are encouraged to open a malicious file or click on a link. Under the pretense of wanting to access uploaded images, an attacker may ask for usernames and passwords for several services, including Facebook, while assuming the identity of a friend. Malvertising Malvertising Malvertising is a tactic used by hackers to disseminate harmful advertisements by taking advantage of internet advertising networks. When users click on these dangerous advertisements, their devices may unintentionally acquire malware or ransomware. Malvertising is a tactic used by malicious actors to add a false aspect to their assaults by preying on people's trust in well-known websites. The majority of malvertising campaigns purchase ad space from reliable ad networks, guaranteeing that their malicious advertisements appear on trustworthy websites. Even though these advertisements seem innocent, they include malicious code that attacks victims as soon as the website loads their assaults. Tech Support Scam Tech Support Scam Even though tech support scams appear to be out of date, they nevertheless exist and fool many people. Cybercriminals contact people under the guise of tech support professionals and claim to have problems with their machines in this scam. They demand action quickly and frequently demand payment or remote access to the victim's device to infect it with malware or steal data. Users are especially vulnerable to this kind of danger because of the psychological manipulation and exploitation of their anxiety and lack of technical understanding. 2. Cyber Hygiene’s Best Practices To Prevent Data Loss 2. Cyber Hygiene’s Best Practices To Prevent Data Loss Implement a Strong Automated Backup System Implement a Strong Automated Backup System To prevent major data loss or business interruption even in the event of a cyber disaster, IT managers must take a proactive approach to cyber hygiene. This entails backing up data on a regular and consistent basis to aid in recovery in the case of an attack. To protect against any data loss, it is essential to keep these backups offline and in several places. Furthermore, to patch any vulnerabilities and remain ahead of emerging threats, it is crucial to regularly upgrade all systems, software, and applications. Constant Learning and Instruction Constant Learning and Instruction Humans continue to be the most important aspect of cybersecurity because of their mental processes, perception, hesitation, ignorance, and mood swings. This is true regardless of how strong your technology protections are. Human mistakes have long been recognized as a major cybersecurity risk and vulnerability. It is essential to prioritize ongoing security awareness education as a result. To minimize the danger of careless insider threats and to keep your staff up to date on the most recent risks, hold frequent training sessions. Use realistic phishing simulations to assess readiness and improve abilities. Multi-Factor Authentication (MFA) Multi-Factor Authentication (MFA) By requesting two or more verification methods from users to get access, MFA improves security. Preventing unwanted access is extremely successful with this method. Without the additional authentication element, fraudsters are unable to further their attempts, even if they are successful in obtaining login credentials. Implement Endpoint Security Invest in endpoint security solutions that manage and keep an eye on every endpoint to strengthen your network's defenses. Fortify the network against possible breaches by deploying powerful endpoint protection technologies that integrate machine learning and behavioral analysis to detect and stop malicious activity in real-time. Conclusion Conclusion Keeping up strong cybersecurity procedures is critical in the current digital environment. Adding layers of protection against changing cyber threats requires the adoption of proactive solutions like multi-factor authentication, constant education, and strong endpoint security. To protect sensitive data and guarantee a robust and secure digital environment, people and organizations need to maintain awareness, give priority to cyber hygiene, and put in place efficient security measures.

Cyber Hygiene: Recognizing Scam Trends and Protecting Against Data Breaches

How Leaders Can Avoid the Trap of False Urgency and Boost Team Productivity

কীভাবে নেতারা মিথ্যা জরুরীতার ফাঁদ এড়াতে পারে এবং দলের উত্পাদনশীলতা বাড়াতে পারে

Wie Führungskräfte die Falle falscher Dringlichkeit vermeiden und die Teamproduktivität steigern können

Cómo los líderes pueden evitar la trampa de la falsa urgencia e impulsar la productividad del equipo

Comment les dirigeants peuvent éviter le piège de la fausse urgence et augmenter la productivité de l'équipe

नेता झूठी तात्कालिकता के जाल से कैसे बच सकते हैं और टीम की उत्पादकता कैसे बढ़ा सकते हैं

リーダーが偽りの緊急性の罠を避け、チームの生産性を高める方法

리더가 잘못된 긴급함의 함정을 피하고 팀 생산성을 높일 수 있는 방법

Como os líderes podem evitar a armadilha da falsa urgência e aumentar a produtividade da equipe

Как лидеры могут избежать ловушки ложной срочности и повысить продуктивность команды

Liderler Yanlış Aciliyet Tuzağından Nasıl Kurtulabilir ve Ekip Verimliliğini Nasıl Artırabilir?

Làm thế nào các nhà lãnh đạo có thể tránh được cái bẫy khẩn cấp sai lầm và tăng năng suất của nhóm

领导者如何避免虚假紧迫感的陷阱并提高团队生产力

Sailing the Waters: Developing Production-Grade RAG Applications with Data Lakes

সেলিং দ্য ওয়াটারস: ডেটা লেক সহ প্রোডাকশন-গ্রেড RAG অ্যাপ্লিকেশন তৈরি করা

Auf See unterwegs: Entwicklung produktionsreifer RAG-Anwendungen mit Data Lakes

Navegando por las aguas: desarrollo de aplicaciones RAG de nivel de producción con lagos de datos

Naviguer sur les eaux : développer des applications RAG de qualité production avec des lacs de données

जलयात्रा: डेटा झीलों के साथ उत्पादन-ग्रेड आरएजी अनुप्रयोगों का विकास

海を航海する: データレイクを使用した本番環境レベルの RAG アプリケーションの開発

바다 항해: 데이터 레이크를 사용하여 프로덕션 등급 RAG 애플리케이션 개발

Navegando pelas águas: desenvolvendo aplicações RAG de nível de produção com data lakes

Плавание по водам: разработка RAG-приложений промышленного уровня с использованием озер данных

Sulara Yelken Açmak: Data Lakes ile Üretim Sınıfında RAG Uygulamaları Geliştirme

Đi thuyền trên biển: Phát triển ứng dụng RAG cấp sản xuất với Hồ dữ liệu

扬帆起航：利用数据湖开发生产级 RAG 应用程序

I grew interested in how the 2FA usually works for quite some time, and of course, in a data-driven society, security is key to everything, right? So I thought of maybe developing one of my own and named it ProAuth. Well, it’s not as ideal as someone would hope it to be(I mean feature-like), but for security, it’s…. what could I say SUPERLATIVE! I’m sure you’ll love it. Here’s a recap of how it works: You log in with your Google account, and you’ll be prompted to a dynamic code generator page where unique codes will be generated every 60 seconds. Amazing! I know right? I’m afraid I didn’t have anyone to join hands with in the build-up process(I know it would’ve been great). Purely, I was targeting organizations that still don’t have their own internal 2FA verification system. If this is what your organization lacks then Viola! Welcome. You’d be surprised how easy it is to navigate through my app. First, the user is prompted to sign in with Google, and by the way, that’s just another secure user authentication. Once allowed in, the backend is just like I mentioned: time-sensitive code is generated(and an API for validating generated codes). The code is then stored in a global variable (memory), and thereafter, the API endpoint is used for code verification to ensure it matches. You must have noticed that I used Python (flask) for the entire backend process, with a bit of JS script spiced inside HTML and CSS for the frontend processing and code display. Well, I don’t have to mention the authentication again, right? (it’s Google). For serving this application, I chose WSGI HTTP, which we both know is well-suited and relied on by most, if not all, flask apps. Nginx was just for taking care of reverse proxy and handling incoming HTTP requests, serving static contents, and managing SSL/TLS. Oooh! I used Cerbot to obtain the SSL certificate so that all connections would appear secure and trustworthy on the internet. I encountered a significant technical challenge that revolved around deploying the app to the server and integrating Google OAuth for user authentication. The challenge became apparent when I repeatedly faced "Resource temporarily unavailable" errors while attempting to SSH into the remote server. Furthermore, attempts to set up OAuth resulted in Google's OAuth service blocking requests, citing security concerns. After many attempts and consultations with the mentors, it was discovered that the problem stemmed from a network misconfiguration at the server level. The server was incorrectly routed, causing intermittent connectivity issues. Once the server's network settings were corrected, SSH access was restored. From this project, what I learned as an engineer is the importance of resilience and a methodical approach to solving problems, especially those that involve network settings that can be daunting. It has informed my engineering path to adopt an iterative development approach with continuous testing in environments that closely mimic production, which will be crucial to identifying and resolving issues at an early stage. Generally about me, I am a dedicated engineer with a passion for creating secure, user-focused web applications.  My background focuses  on both front and backend development. I thrive in solving complex problems and continuously seek  to enhance my technical skills through hands-on projects and collaboration. Beyond coding, I enjoy learning about new technologies and sharing knowledge with my peers, and oh! I love music; perhaps someday I’ll develop an app for this. GitHub repo: https://github.com/Carey99/full_proauth
Deployed Project Page: https://proauth.awiki.org
LinkedIn Profile: https://www.linkedin.com/in/cery-akida-a619911a8/ Feel free to explore the project and connect with me for further discussions or collaborations! I grew interested in how the 2FA usually works for quite some time, and of course, in a data-driven society, security is key to everything, right? So I thought of maybe developing one of my own and named it ProAuth. Well, it’s not as ideal as someone would hope it to be(I mean feature-like), but for security, it’s…. what could I say SUPERLATIVE! I’m sure you’ll love it. Here’s a recap of how it works: You log in with your Google account, and you’ll be prompted to a dynamic code generator page where unique codes will be generated every 60 seconds. Amazing! I know right? I’m afraid I didn’t have anyone to join hands with in the build-up process(I know it would’ve been great). Purely, I was targeting organizations that still don’t have their own internal 2FA verification system. If this is what your organization lacks then Viola! Welcome. You’d be surprised how easy it is to navigate through my app. First, the user is prompted to sign in with Google, and by the way, that’s just another secure user authentication. Once allowed in, the backend is just like I mentioned: time-sensitive code is generated(and an API for validating generated codes). The code is then stored in a global variable (memory), and thereafter, the API endpoint is used for code verification to ensure it matches. You must have noticed that I used Python (flask) for the entire backend process, with a bit of JS script spiced inside HTML and CSS for the frontend processing and code display. Well, I don’t have to mention the authentication again, right? ( it’s Google ). For serving this application, I chose WSGI HTTP, which we both know is well-suited and relied on by most, if not all, flask apps. Nginx was just for taking care of reverse proxy and handling incoming HTTP requests, serving static contents, and managing SSL/TLS. Oooh! I used Cerbot to obtain the SSL certificate so that all connections would appear secure and trustworthy on the internet. it’s Google I encountered a significant technical challenge that revolved around deploying the app to the server and integrating Google OAuth for user authentication. The challenge became apparent when I repeatedly faced " Resource temporarily unavailable " errors while attempting to SSH into the remote server. Furthermore, attempts to set up OAuth resulted in Google's OAuth service blocking requests, citing security concerns. Resource temporarily unavailable After many attempts and consultations with the mentors, it was discovered that the problem stemmed from a network misconfiguration at the server level. The server was incorrectly routed, causing intermittent connectivity issues. Once the server's network settings were corrected, SSH access was restored. From this project, what I learned as an engineer is the importance of resilience and a methodical approach to solving problems, especially those that involve network settings that can be daunting. It has informed my engineering path to adopt an iterative development approach with continuous testing in environments that closely mimic production, which will be crucial to identifying and resolving issues at an early stage. Generally about me, I am a dedicated engineer with a passion for creating secure, user-focused web applications.  My background focuses  on both front and backend development. I thrive in solving complex problems and continuously seek  to enhance my technical skills through hands-on projects and collaboration. Beyond coding, I enjoy learning about new technologies and sharing knowledge with my peers, and oh! I love music; perhaps someday I’ll develop an app for this. GitHub repo: https://github.com/Carey99/full_proauth Deployed Project Page: https://proauth.awiki.org LinkedIn Profile: https://www.linkedin.com/in/cery-akida-a619911a8/ GitHub repo: https://github.com/Carey99/full_proauth https://github.com/Carey99/full_proauth Deployed Project Page: https://proauth.awiki.org https://proauth.awiki.org LinkedIn Profile: https://www.linkedin.com/in/cery-akida-a619911a8/ https://www.linkedin.com/in/cery-akida-a619911a8/ Feel free to explore the project and connect with me for further discussions or collaborations!

ProAuth: How I Used Flask and OAuth to Develop a Dynamic Code Authentication App

The F*ck It Effect

F*ck এটি প্রভাব

Der F*ck-It-Effekt

El efecto F*ck It

L'effet F*ck It

एफ*क इट प्रभाव

ファック・イット効果

빌어먹을 효과

O efeito foda-se

Эффект «к черту это»

S*ktir Et Etkisi

Hiệu ứng F*ck It

F*ck It 效应

Concluding Our Characterizing Biases in Cable News Study

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) D.Sinclair, Imense Ltd, and email: david@imense.com;
(2) W.T.Pye, Warwick University, and email:  willempye@gmail.com. Table of Links Abstract and Introduction
Interrogating the LLM with an Emotion Eliciting Tail Prompt
PCA analysis of the Emotion of Amazon reviews
Future Work
Conclusions
Acknowledgments and References 2. Interrogating the LLM with an Emotion Eliciting Tail Prompt In this work we used Facebook’s open source LlaMa2 7 billion weight LLM as the core engine [2]. It was necessary to use a LLM that allowed access to raw token probabilities after a prompt. The model ran on a Mac Studio with 32 gigabytes of RAM. With this combination of hardware and model it took 2 minutes to compute the probabilities of the emotion descriptors in the emotion dictionary given below. 2.1. The Emotion Dictionary The English language is blessed with many words and an extensive literature providing examples of the usage of these words in appropriate contexts. For the purposes of LLMs, it is the context of a word that conveys it’s meaning. A reader will infer the meaning of an unfamiliar word through the context they find the word used in, provided they understand the context. As an example, ‘The shotgun discombobulated the rabbit.’ shows how meaning can be moderated by context. The context created by a tail prompt will favour an associated class of words. For the experiment detailed in this paper the following tail prompt was used to elicit emotion descriptors, ’Reading this makes me feel’. It is likely that specific emotion eliciting tail prompts will favour specific sub classes of descriptor but studying this is beyond the scope of this paper The following words were chosen to provide a broad sample of emotion descriptors. acceptance, admiration, adoration, affection, afraid, agitation, agony, aggressive, alarm, alarmed, alienation, amazement, ambivalence, amusement, anger, anguish, annoyed anticipating, anxious, apathy, apprehension, arrogant, assertive, astonished, attentiveness, attraction, aversion, awe, baffled, bewildered, bitter, bitter sweetness, bliss, bored, brazen, brooding, calm, carefree, careless, caring, charity, cheeky, cheerfulness, claustrophobic, coercive, comfortable, confident, confusion, contempt, content, courage, cowardly, cruelty, curiosity, cynicism, dazed, dejection, delighted, demoralized, depressed, desire, despair, determined, disappointment, disbelief, discombobulated, discomfort, discontentment, disgruntled, disgust, disheartened, dislike, dismay, disoriented, dispirited, displeasure, distraction, distress, disturbed, dominant, doubt, dread, driven, dumbstruck, eagerness, ecstasy, elation, embarrassment, empathy, enchanted, enjoyment, enlightened, ennui, enthusiasm, envy, epiphany, euphoria, exasperated, excitement, expectancy, fascination, fear, flakey, focused, fondness, friendliness, fright, frustrated, fury, glee, gloomy, glumness, gratitude, greed, grief, grouchiness, grumpiness, guilt, happiness, hate, hatred, helpless, homesickness, hope, hopeless, horrified, hospitable, humiliation, humility, hurt, hysteria, idleness, impatient, indifference, indignant, infatuation, infuriated, insecurity, insightful, insulted, interest, intrigued, irritated, isolated, jealousy, joviality, joy, jubilation, kind, lazy, liking, loathing, lonely, longing, loopy, love, lust, mad, melancholy, miserable, miserliness, mixed up, modesty, moody, mortified, mystified, nasty, nauseated, negative, neglect, nervous, nostalgic, numb, obstinate, offended, optimistic, outrage, overwhelmed, panicked, paranoid, passion, patience, pensiveness, perplexed, persevering, pessimism, pity, pleased, pleasure, politeness, positive, possessive, powerless, pride, puzzled, rage, rash, rattled, regret, rejected, relaxed, relieved, reluctant, remorse, resentment, resignation, restlessness, revulsion, ruthless, sadness, satisfaction, scared, schadenfreude, scorn, self-caring, self-compassionate, self-confident, self-conscious, self-critical, self-loathing, self-motivated, self-pity, self-respecting, self-understanding, sentimentality, serenity, shame, shameless, shocked, smug, sorrow, spite, stressed, strong, stubborn, stuck, submissive, suffering, sullenness, surprise, suspense, suspicious, sympathy, tenderness, tension, terror, thankfulness, thrilled, tired, tolerance, torment, triumphant, troubled, trust, uncertainty, undermined, uneasiness, unhappy, unnerved, unsettled, unsure, upset, vengeful, vicious, vigilance, vulnerable, weak, woe, worried, worthy, wrath. This set of words is not intended to be complete or definitive in any way. Using the tail prompt without restricting the return to emotion descriptors elicits general waffle responses that are not straightforward to extract sentiment form. 2.1.1. Estimating the emotion probability vector LlaMa2 [2] has been released in such a way as to allow developers to access estimated token weights returned in response to a prompt. LlaMa2 has an internal vocabulary size of roughly 30,000 tokens. This means that when LlaMa 2 estimates the probability of the next token in a sequence the probability vector will have 30,000 elements. Some of the words in the emotion descriptor list are made up of more than one token in which case forward conditional probabilities are used. Figure 2.1.1 shows the scaled probability distribution over words from the emotion dictionary elicited by the tail prompt for the Amazon review text, ‘I read a lot of negative reviews about the Fitbit inspire 2, I took a chance and hoped the one I ordered would be one of the great ones that worked. Unfortunately that was not the case. I unpacked it, charged it, downloaded the app. I took a walk with it on before the sun went down. I have the Google Fit app on my phone that tracks my steps also. The phone was in my jeans pocket. When I got home I compared the two, Google Fit said 4,458 steps, Fitbit said 1,168. Apparently Fitbit works with wrist motion which I don’t have while pushing a walker around the neighborhood. I downloaded the manual and noticed you can put it on a clip (that wasn’t included). That would work for me. So I started to scroll through the different features except I couldn’t scroll through all of them. While scrolling I must have turned on the stopwatch. I couldn’t turn it off. Then I couldn’t scroll through anything except water lock feature. I had to turn on the water lock to get back to the stopwatch. Then the side buttons stopped working. I had it a total of 5 hours. I packed it up and started the Amazon return. I did get a full refund. Very disappointing.’ The text from 50 Amazon reviews of a book was borrowed from https://www.amazon.com/dp/B000WM9UK2. The reviews were for the most part favourable. Example review texts include: ‘The Children of H´urin is a great tragedy mixed with grace. The genealogical records at the beginning may be difficult to get through, but the story very quickly gains speed. I only gave the story four stars because of the difficulty of the first few chapters, much like Matthew’s genealogy of Christ at the beginning of his gospel. Although such records are important in both they are nonetheless difficult to get through. I still definitely would recommend this book, though, as it depicts the horrible effects of evil powers on good men and women, and yet, we must continue to resist evil no matter the tragic end. It is very telling that in Tolkien’s world, at the end of days when Morgoth returns, that it is T´urin, a man, who ends him once and for all. Those whom Satan most destroys in this life are they who will ultimately deal his death blow, as the Revelation says, “They overcame him by the blood of the Lamb and by the word of their ‘martyria,’ [witness, testimony],” those like T´urin, or in Scripture, those like Job.’ Figure 2.1.2 shows the emotion vectors for all 50 processed reviews. The 10 most probable emotions experienced from purchasing the product were: depressed, kind, nostalgic, tired, hopeless, lonely, hope, calm, lazy, confident. This paper is available on arxiv under CC 4.0 license. Authors: (1) D.Sinclair, Imense Ltd, and email: david@imense.com; (2) W.T.Pye, Warwick University, and email:  willempye@gmail.com. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) D.Sinclair, Imense Ltd, and email: david@imense.com; (2) W.T.Pye, Warwick University, and email:  willempye@gmail.com. Table of Links Abstract and Introduction Interrogating the LLM with an Emotion Eliciting Tail Prompt PCA analysis of the Emotion of Amazon reviews Future Work Conclusions Acknowledgments and References Abstract and Introduction Abstract and Introduction Interrogating the LLM with an Emotion Eliciting Tail Prompt Interrogating the LLM with an Emotion Eliciting Tail Prompt PCA analysis of the Emotion of Amazon reviews PCA analysis of the Emotion of Amazon reviews Future Work Future Work Conclusions Conclusions Acknowledgments and References Acknowledgments and References 2. Interrogating the LLM with an Emotion Eliciting Tail Prompt In this work we used Facebook’s open source LlaMa2 7 billion weight LLM as the core engine [2]. It was necessary to use a LLM that allowed access to raw token probabilities after a prompt. The model ran on a Mac Studio with 32 gigabytes of RAM. With this combination of hardware and model it took 2 minutes to compute the probabilities of the emotion descriptors in the emotion dictionary given below. 2.1. The Emotion Dictionary The English language is blessed with many words and an extensive literature providing examples of the usage of these words in appropriate contexts. For the purposes of LLMs, it is the context of a word that conveys it’s meaning. A reader will infer the meaning of an unfamiliar word through the context they find the word used in, provided they understand the context. As an example, ‘The shotgun discombobulated the rabbit.’ shows how meaning can be moderated by context. The context created by a tail prompt will favour an associated class of words. For the experiment detailed in this paper the following tail prompt was used to elicit emotion descriptors, ’Reading this makes me feel’. It is likely that specific emotion eliciting tail prompts will favour specific sub classes of descriptor but studying this is beyond the scope of this paper The following words were chosen to provide a broad sample of emotion descriptors. acceptance, admiration, adoration, affection, afraid, agitation, agony, aggressive, alarm, alarmed, alienation, amazement, ambivalence, amusement, anger, anguish, annoyed anticipating, anxious, apathy, apprehension, arrogant, assertive, astonished, attentiveness, attraction, aversion, awe, baffled, bewildered, bitter, bitter sweetness, bliss, bored, brazen, brooding, calm, carefree, careless, caring, charity, cheeky, cheerfulness, claustrophobic, coercive, comfortable, confident, confusion, contempt, content, courage, cowardly, cruelty, curiosity, cynicism, dazed, dejection, delighted, demoralized, depressed, desire, despair, determined, disappointment, disbelief, discombobulated, discomfort, discontentment, disgruntled, disgust, disheartened, dislike, dismay, disoriented, dispirited, displeasure, distraction, distress, disturbed, dominant, doubt, dread, driven, dumbstruck, eagerness, ecstasy, elation, embarrassment, empathy, enchanted, enjoyment, enlightened, ennui, enthusiasm, envy, epiphany, euphoria, exasperated, excitement, expectancy, fascination, fear, flakey, focused, fondness, friendliness, fright, frustrated, fury, glee, gloomy, glumness, gratitude, greed, grief, grouchiness, grumpiness, guilt, happiness, hate, hatred, helpless, homesickness, hope, hopeless, horrified, hospitable, humiliation, humility, hurt, hysteria, idleness, impatient, indifference, indignant, infatuation, infuriated, insecurity, insightful, insulted, interest, intrigued, irritated, isolated, jealousy, joviality, joy, jubilation, kind, lazy, liking, loathing, lonely, longing, loopy, love, lust, mad, melancholy, miserable, miserliness, mixed up, modesty, moody, mortified, mystified, nasty, nauseated, negative, neglect, nervous, nostalgic, numb, obstinate, offended, optimistic, outrage, overwhelmed, panicked, paranoid, passion, patience, pensiveness, perplexed, persevering, pessimism, pity, pleased, pleasure, politeness, positive, possessive, powerless, pride, puzzled, rage, rash, rattled, regret, rejected, relaxed, relieved, reluctant, remorse, resentment, resignation, restlessness, revulsion, ruthless, sadness, satisfaction, scared, schadenfreude, scorn, self-caring, self-compassionate, self-confident, self-conscious, self-critical, self-loathing, self-motivated, self-pity, self-respecting, self-understanding, sentimentality, serenity, shame, shameless, shocked, smug, sorrow, spite, stressed, strong, stubborn, stuck, submissive, suffering, sullenness, surprise, suspense, suspicious, sympathy, tenderness, tension, terror, thankfulness, thrilled, tired, tolerance, torment, triumphant, troubled, trust, uncertainty, undermined, uneasiness, unhappy, unnerved, unsettled, unsure, upset, vengeful, vicious, vigilance, vulnerable, weak, woe, worried, worthy, wrath. This set of words is not intended to be complete or definitive in any way. Using the tail prompt without restricting the return to emotion descriptors elicits general waffle responses that are not straightforward to extract sentiment form. 2.1.1. Estimating the emotion probability vector LlaMa2 [2] has been released in such a way as to allow developers to access estimated token weights returned in response to a prompt. LlaMa2 has an internal vocabulary size of roughly 30,000 tokens. This means that when LlaMa 2 estimates the probability of the next token in a sequence the probability vector will have 30,000 elements. Some of the words in the emotion descriptor list are made up of more than one token in which case forward conditional probabilities are used. Figure 2.1.1 shows the scaled probability distribution over words from the emotion dictionary elicited by the tail prompt for the Amazon review text, ‘I read a lot of negative reviews about the Fitbit inspire 2, I took a chance and hoped the one I ordered would be one of the great ones that worked. Unfortunately that was not the case. I unpacked it, charged it, downloaded the app. I took a walk with it on before the sun went down. I have the Google Fit app on my phone that tracks my steps also. The phone was in my jeans pocket. When I got home I compared the two, Google Fit said 4,458 steps, Fitbit said 1,168. Apparently Fitbit works with wrist motion which I don’t have while pushing a walker around the neighborhood. I downloaded the manual and noticed you can put it on a clip (that wasn’t included). That would work for me. So I started to scroll through the different features except I couldn’t scroll through all of them. While scrolling I must have turned on the stopwatch. I couldn’t turn it off. Then I couldn’t scroll through anything except water lock feature. I had to turn on the water lock to get back to the stopwatch. Then the side buttons stopped working. I had it a total of 5 hours. I packed it up and started the Amazon return. I did get a full refund. Very disappointing.’ ‘I read a lot of negative reviews about the Fitbit inspire 2, I took a chance and hoped the one I ordered would be one of the great ones that worked. Unfortunately that was not the case. I unpacked it, charged it, downloaded the app. I took a walk with it on before the sun went down. I have the Google Fit app on my phone that tracks my steps also. The phone was in my jeans pocket. When I got home I compared the two, Google Fit said 4,458 steps, Fitbit said 1,168. Apparently Fitbit works with wrist motion which I don’t have while pushing a walker around the neighborhood. I downloaded the manual and noticed you can put it on a clip (that wasn’t included). That would work for me. So I started to scroll through the different features except I couldn’t scroll through all of them. While scrolling I must have turned on the stopwatch. I couldn’t turn it off. Then I couldn’t scroll through anything except water lock feature. I had to turn on the water lock to get back to the stopwatch. Then the side buttons stopped working. I had it a total of 5 hours. I packed it up and started the Amazon return. I did get a full refund. Very disappointing.’ The text from 50 Amazon reviews of a book was borrowed from https://www.amazon.com/dp/B000WM9UK2. The reviews were for the most part favourable. Example review texts include: ‘The Children of H´urin is a great tragedy mixed with grace. The genealogical records at the beginning may be difficult to get through, but the story very quickly gains speed. I only gave the story four stars because of the difficulty of the first few chapters, much like Matthew’s genealogy of Christ at the beginning of his gospel. Although such records are important in both they are nonetheless difficult to get through. I still definitely would recommend this book, though, as it depicts the horrible effects of evil powers on good men and women, and yet, we must continue to resist evil no matter the tragic end. It is very telling that in Tolkien’s world, at the end of days when Morgoth returns, that it is T´urin, a man, who ends him once and for all. Those whom Satan most destroys in this life are they who will ultimately deal his death blow, as the Revelation says, “They overcame him by the blood of the Lamb and by the word of their ‘martyria,’ [witness, testimony],” those like T´urin, or in Scripture, those like Job.’ ‘The Children of H´urin is a great tragedy mixed with grace. The genealogical records at the beginning may be difficult to get through, but the story very quickly gains speed. I only gave the story four stars because of the difficulty of the first few chapters, much like Matthew’s genealogy of Christ at the beginning of his gospel. Although such records are important in both they are nonetheless difficult to get through. I still definitely would recommend this book, though, as it depicts the horrible effects of evil powers on good men and women, and yet, we must continue to resist evil no matter the tragic end. It is very telling that in Tolkien’s world, at the end of days when Morgoth returns, that it is T´urin, a man, who ends him once and for all. Those whom Satan most destroys in this life are they who will ultimately deal his death blow, as the Revelation says, “They overcame him by the blood of the Lamb and by the word of their ‘martyria,’ [witness, testimony],” those like T´urin, or in Scripture, those like Job.’ Figure 2.1.2 shows the emotion vectors for all 50 processed reviews. The 10 most probable emotions experienced from purchasing the product were: depressed, kind, nostalgic, tired, hopeless, lonely, hope, calm, lazy, confident.

Estimate Emotion Probability Vectors: Interrogating the LLM with an Emotion Eliciting Tail Prompt

আবেগ সম্ভাবনা ভেক্টর অনুমান করুন: একটি আবেগ এলিসিটিং টেইল প্রম্পটের সাথে এলএলএমকে জিজ্ঞাসাবাদ করা

Schätzen Sie Wahrscheinlichkeitsvektoren für Emotionen: Abfragen des LLM mit einem Emotionen hervorrufenden Tail Prompt

Estimar vectores de probabilidad de emociones: interrogar al LLM con un mensaje de cola que provoca emociones

Estimer les vecteurs de probabilité d'émotion : interroger le LLM avec une invite de queue suscitant des émotions

भावना संभाव्यता सदिशों का अनुमान लगाना: भावना उद्घाटित करने वाले टेल प्रॉम्प्ट के साथ LLM से पूछताछ करना

感情確率ベクトルの推定: 感情を喚起するテールプロンプトによる LLM の調査

감정 확률 벡터 추정: 감정 유도 꼬리 프롬프트로 LLM 조사

Estimar vetores de probabilidade de emoção: interrogando o LLM com um prompt de cauda que provoca emoção

Оценка векторов вероятности эмоций: опрос LLM с помощью хвостовой подсказки, вызывающей эмоции

Duygu Olasılığı Vektörlerini Tahmin Edin: Yüksek Lisans'ı Duygu Ortaya Çıkaran Kuyruk İstemi ile Sorgulamak

Ước tính các vectơ xác suất cảm xúc: Thẩm vấn LLM bằng lời nhắc gợi ý cảm xúc

估计情绪概率向量：用引发情绪的尾部提示询问法学硕士

Invisible Online: A Family Guide to Private and Secure Online Living

We see that multiple industries are working hard to accelerate the adoption of Artificial Intelligence, without having the proper security measures in place. It is important to know that AI is not some invincible new technology, but rather, a technology extremely vulnerable to cyber threats just like many others that came before it. The motivations for attacking AI are what you would expect. They range from financial gain to manipulating public opinion to gaining competitive advantage. While industries are gaining the benefit of increased efficiency and innovation thanks to AI, there is still the concerning reality that expanding the use of AI causes a significant increase in security risks. Risks Related to the Use of AI Like with any other life-changing technology, artificial intelligence is a double-edged sword. Although it’s already starting to have a massively positive impact on our lives and workflows, it also has tremendous potential to cause serious harm, especially if used carelessly or with overt malicious intent. There are plenty of ways in which adversaries - such as criminals, terrorists, cyber threat actors, foul-playing competitors, and repressive nation-states can utilize AI to their advantage. There are also numerous obscure risks related to the legitimate use of this technology. Privacy is also an issue when it comes to the information we share with AI-based tools. Data leakage can cause significant legal issues for businesses and institutions. In addition, because of code generation tools, vulnerabilities could be introduced into the software - intentionally, by poisoning the datasets, or unintentionally, by training the models on already vulnerable code. All this is on top of copyright violations and various ethical and societal concerns. Generative AI is especially vulnerable to abuse. It can be: a) manipulated to give biased, inaccurate, or harmful information b) used to create harmful content, such as malware, phishing, and propaganda c) used to develop deepfake images, audio and video d) leveraged by any malicious activity to provide access to dangerous or illegal information. There’s a lot of conversation about the safe and ethical use of AI-powered tools, however the security and safety of AI systems themselves are still often overlooked. It’s vital to remember that, like with any other ubiquitous technology, AI-based solutions can be abused by attackers, resulting in disruption, financial loss, reputational harm, or even risk to human health and life. There are, broadly, three types of attacks targeting AI: Adversarial machine learning attacks - Attacks against AI algorithms, aimed to alter AI’s behavior, evade AI-based detection, or steal the underlying technology.
Generative AI system attacks - Attacks against AI’s filters and restrictions, intended to generate content deemed harmful or illegal.
Supply chain attacks - Attacks against ML artifacts and platforms, with the intention of arbitrary code execution and delivery of traditional malware. Securing Your AI for Better Data Security Understanding and implementing extensive security measures for AI is no longer a choice. It’s a necessity. Too much is at risk for organizations, government, and society at large. Security must maintain pace with AI to allow innovation to flourish. That is why it is imperative to safeguard your most valuable assets, from development to operation and everything in between. Discovery and Asset Management First, identify where AI is already used in your organization. What applications has your organization already purchased that use AI or have AI-enabled features? Second, evaluate what AI may be under development by your organization. Third, understand what pretrained models from public repositories may already be in use. Risk Assessment and Threat Modelling First, conduct a benefit assessment to identify the potential negative consequences associated with the AI system if those models were to be compromised in any way. Second, perform threat modeling to understand the potential vulnerabilities and attack vectors that could be exploited by malicious actors to complete your understanding of your organization’s AI risk exposure. Data Security and Privacy Go beyond the typical implementation of encryption, access controls, and secure data storage practices to protect your AI model data. Evaluate and implement security solutions that are purpose-built to provide runtime protection for AI models. Model Robustness and Validation Regularly assess the robustness of AI models against adversarial attacks. This involves pen-testing the models’ response to various attacks such as intentionally manipulated inputs. Next, implement model validation techniques to ensure the AI system behaves predictably and reliably in real-world scenarios. This will help minimize the risk of unintended consequences. Continuous Monitoring and Incident Response Implement continuous monitoring mechanisms to detect anomalies and potential security incidents in real time for your AI. Require your vendors to utilize AI in their solutions to alert you to attacks that could compromise your data or business processes. Develop a robust AI incident response plan to quickly and effectively address security breaches or anomalies. Regularly test and update the incident response plan to adapt to evolving AI threats. Conclusion The security landscape as well as AI technology are dynamically and rapidly changing. It’s crucial to stay informed about emerging threats and best practices. Regularly update and refine your AI-specific security program to address new challenges and vulnerabilities. Responsible and ethical AI frameworks in many cases fall short of ensuring models are secure before they go into production, as well as after an AI system is in use. Always ask yourself the following questions 1) What am I doing to secure my organization’s use of AI? 2) Is it enough? 3) How do I know? Only by answering these questions with data-driven, intellectual honesty, can you maintain the integrity of your security role and keep your organization secure. We see that multiple industries are working hard to accelerate the adoption of Artificial Intelligence , without having the proper security measures in place. It is important to know that AI is not some invincible new technology, but rather, a technology extremely vulnerable to cyber threats just like many others that came before it. The motivations for attacking AI are what you would expect. They range from financial gain to manipulating public opinion to gaining competitive advantage. While industries are gaining the benefit of increased efficiency and innovation thanks to AI, there is still the concerning reality that expanding the use of AI causes a significant increase in security risks. Artificial Intelligence Risks Related to the Use of AI Like with any other life-changing technology, artificial intelligence is a double-edged sword. Although it’s already starting to have a massively positive impact on our lives and workflows, it also has tremendous potential to cause serious harm, especially if used carelessly or with overt malicious intent. There are plenty of ways in which adversaries - such as criminals, terrorists, cyber threat actors, foul-playing competitors, and repressive nation-states can utilize AI to their advantage. There are also numerous obscure risks related to the legitimate use of this technology. Privacy is also an issue when it comes to the information we share with AI-based tools. Data leakage can cause significant legal issues for businesses and institutions. In addition, because of code generation tools, vulnerabilities could be introduced into the software - intentionally, by poisoning the datasets, or unintentionally, by training the models on already vulnerable code. All this is on top of copyright violations and various ethical and societal concerns. Generative AI is especially vulnerable to abuse. Generative AI It can be: It can be: a) manipulated to give biased, inaccurate, or harmful information b) used to create harmful content, such as malware, phishing, and propaganda c) used to develop deepfake images, audio and video d) leveraged by any malicious activity to provide access to dangerous or illegal information. There’s a lot of conversation about the safe and ethical use of AI-powered tools, however the security and safety of AI systems themselves are still often overlooked. It’s vital to remember that, like with any other ubiquitous technology, AI-based solutions can be abused by attackers, resulting in disruption, financial loss, reputational harm, or even risk to human health and life. There are, broadly, three types of attacks targeting AI: There are, broadly, three types of attacks targeting AI: Adversarial machine learning attacks - Attacks against AI algorithms, aimed to alter AI’s behavior, evade AI-based detection, or steal the underlying technology. Generative AI system attacks - Attacks against AI’s filters and restrictions, intended to generate content deemed harmful or illegal. Supply chain attacks - Attacks against ML artifacts and platforms, with the intention of arbitrary code execution and delivery of traditional malware. Adversarial machine learning attacks - Attacks against AI algorithms, aimed to alter AI’s behavior, evade AI-based detection, or steal the underlying technology. Generative AI system attacks - Attacks against AI’s filters and restrictions, intended to generate content deemed harmful or illegal. Supply chain attacks - Attacks against ML artifacts and platforms, with the intention of arbitrary code execution and delivery of traditional malware. Securing Your AI for Better Data Security Understanding and implementing extensive security measures for AI is no longer a choice. It’s a necessity. Too much is at risk for organizations, government, and society at large. Security must maintain pace with AI to allow innovation to flourish. That is why it is imperative to safeguard your most valuable assets, from development to operation and everything in between. Discovery and Asset Management First, identify where AI is already used in your organization. What applications has your organization already purchased that use AI or have AI-enabled features? Second, evaluate what AI may be under development by your organization. Third, understand what pretrained models from public repositories may already be in use. Risk Assessment and Threat Modelling First, conduct a benefit assessment to identify the potential negative consequences associated with the AI system if those models were to be compromised in any way. Second, perform threat modeling to understand the potential vulnerabilities and attack vectors that could be exploited by malicious actors to complete your understanding of your organization’s AI risk exposure. Data Security and Privacy Go beyond the typical implementation of encryption, access controls, and secure data storage practices to protect your AI model data. Evaluate and implement security solutions that are purpose-built to provide runtime protection for AI models. Model Robustness and Validation Regularly assess the robustness of AI models against adversarial attacks. This involves pen-testing the models’ response to various attacks such as intentionally manipulated inputs. Next, implement model validation techniques to ensure the AI system behaves predictably and reliably in real-world scenarios. This will help minimize the risk of unintended consequences. Continuous Monitoring and Incident Response Implement continuous monitoring mechanisms to detect anomalies and potential security incidents in real time for your AI. Require your vendors to utilize AI in their solutions to alert you to attacks that could compromise your data or business processes. Develop a robust AI incident response plan to quickly and effectively address security breaches or anomalies. Regularly test and update the incident response plan to adapt to evolving AI threats. Conclusion The security landscape as well as AI technology are dynamically and rapidly changing. It’s crucial to stay informed about emerging threats and best practices. Regularly update and refine your AI-specific security program to address new challenges and vulnerabilities. Responsible and ethical AI frameworks in many cases fall short of ensuring models are secure before they go into production, as well as after an AI system is in use. Always ask yourself the following questions 1) What am I doing to secure my organization’s use of AI? 2) Is it enough? 3) How do I know? Only by answering these questions with data-driven, intellectual honesty, can you maintain the integrity of your security role and keep your organization secure.

Navigating Data Security Risks in the Age of Artificial Intelligence

This paper is under CC 4.0 license. available on arxiv Authors: (1) Zhe Liu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (2) Chunyang Chen, Monash University, Melbourne, Australia; (3) Junjie Wang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China & Corresponding author; (4) Mengzhuo Chen, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (5) Boyu Wu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (6) Zhilin Tian, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (7) Yuekai Huang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (8) Jun Hu, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; (9) Qing Wang, State Key Laboratory of Intelligent Game, Beijing, China Institute of Software Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China & Corresponding author. Table of Links Abstract and Introduction Motivational Study and Background Approach Experiment Design Results and Analysis Discussion and Threats to Validity Related Work Conclusion and References 8 CONCLUSION Automated testing is crucial for helping improve app quality. Despite the dozens of mobile app GUI testing techniques, how to automatically generate the diversified unusual text inputs for fully testing mobile apps remains a challenge. This paper proposes InputBlaster which leverages the LLM to produce the unusual inputs together with the mutation rules which serve as the reasoning chains. It formulates the unusual inputs generation problem as a task of producing a set of test generators, each of which can yield a batch of unusual text inputs under the same mutation rule. The evaluation is conducted for both effectiveness and usefulness, with 136% higher bug detection rate than the best baselines, and uncovering 37 new crashes. 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Leveraging LLMs for Generation of Unusual Text Inputs in Mobile App Tests: Conclusion and References

At some point, too many people will have lost too much Bitcoin that they will meet to talk about it. Later, they will opt to do something about it. There can only be a (decreasing number) of bitcoins Lost cryptocurrency is a unique beast, quite different from lost information for 2 reasons: 1. We can continue maintaining its value. Right? Is it really still valuable? 2. Other people know it is lost. They simply check the blockchain. Whereas with lost information, nobody else can tell except those who have lost that intel. Take Bitcoin as an example. We still say 'There can only be 21 million'. But if this includes even lost bitcoins, then we are counting our losses as assets! That is very strange. Imagine if you were an accountant for a major company. The company deals in all sorts of assets, but sometimes writes some physical assets off as lost either due to theft, fire outbreak, or irreparable damage. But to your shock, the manager insists that not only do you keep counting those assets, but also, count them as fully functional assets whose value is going up every year. "But they are damaged", you might say. "Yeah, but they are still our assets" "What, how?" "There is hope we can recover them exactly as they were before they were lost. And then some". It’s hard to fathom. It indeed feels like Alice in Wonderland, where cards talk, a piece of cake can make one bigger, or smaller, and a cat can disappear leaving only its grin behind. Bitcoin and most crypto are the same. The question is thus; Is real value riding on all coins? Even when some are lost? Certainly, supply and demand economics might make all the coins feel equally valuable hence we would say that if Bitcoin is at $70,000 then the market cap is $1.47 trillion. But taking the figures as per this reddit post on lost bitcoins, if 6 million are lost then only 15 million coins are existing which are backed by a market cap of $1.05 trillion. Less by $420 billion. This does deflate our dreams and begs the question --> are we kidding ourselves? The road to Zero bitcoins By some estimates, 4% of 21,000,000 bitcoins continue to be lost every year. How many shall be lost, going forward? Let's assume this 4% holds true. 4% × 21,000,000 = 840,000 bitcoins. This is very steep. Well then, in 15 years, we shall lose 840,000 * 15 = 12,600,000 Add 6 million to this batch and circa 2039, 'There can only be 2,400,000 bitcoins' When will they be finished? Now, a little hope is in order. The 4% cannot hold steady forever, so it would be wise to say it too will be a declining figure as we progress. Because as Bitcoins get more valuable, people will be more careful with their keys. It is inevitable. Taking it at face value though, we shall run out of bitcoins to trade within the next 18 years. So Circa 2042. I hope the naysayers will be back to tell us they told us so, or to plead guilty of doomism and explain why there are still many millions of Bitcoins being traded daily. However, it certainly looks like way before the year 2137 when the last satoshi will be mined, we shall hit a big wall as per the coins lost vs the coins in the available supply. When enough is enough, for lost Bitcoins Assume at the time this shocking reality hits the Bitcoin community, we have only 4 million coins tradable around the world. Assume even Michael Saylor shall have lost some of his keys/coins. What then? Well, the price of a single coin, feeling so much demand, will spike to about $10 million, and then it will dawn on everybody. Bitcoin has a $40 trillion market capitalization and not a $210 trillion market capitalization. Right now, this is an elephant 🐘 in the Bitcoin room, but it is still a baby and we are all happy to pet it and feed it with careless abandon. Soon, however, it will become a giant. A big fat frustration. At $10 trillion per coin, the fees to move even a few sats will likely be thousands of dollars because; a) the Bitcoin reward is smaller. b) miners have also lost their coins. c) losing coins to hackers will be most likely (hackers will be sniffing all over every wallet, waiting for the SSL to get some slight holes) Everybody will be losing coins. It will be wild. But before I proceed? Why do people lose keys hence coins? Well, if you are using the key every time, sometimes changing it this way and that way to maintain your security (because AI powered cameras may be watching), it so happens that you are bound to make a mistake and lose the key. Or else, be hacked (since you cannot fathom changing the key all the time). Like smart people must have said, meat brains are not good at holding random numbers or random sequences of words. Luke Dash Jnr, a seasoned cypherpunk, was hacked last year. He lost 200 bitcoins. Elon Musk may have lost his Bitcon keys back in '20. So, shit happens. To hard fork those lost coins back, or not. That is the question So here's the thing. We wake up and Bitcoin is seizing up. Crashing into its mass and threatening to take our financial dreams with it when it turns into a black hole of lost keys / burned coins. We also wake up and realize that the debt spiral is still sucking us down with it, even as we hodl Bitcoin. Climate is now a real issue and oh, AI is giving us headaches. Too many addictive cat videos. Too many coins are lost, the blocks are always full so confirmations take decades, the core software is too large, the fees are too high. So we sit down, like AA group members, and decide that continuing to preach financial freedom yet the currency is fast tending towards technological obsolescence, is like being high on alcohol and thinking that one is blissful for other reasons. We need to quit the 'lost coins are good for all' alcohol. In short, it will be time for a hard fork. The Hard Fork --> Bitcoin ver. 2.0 Innovation comes full circle. After factoring in the reality of available supply and not merely supply, we could let Satoshi Nakamoto's 2 million or so coins be our target practice and our saving grace. One shot and bull’s eye. Trillions of dollars pour down from the Bitcoin piñata. Like manna from the heavens. Right? Alas, There shall only be 4 million bitcoins and the value of lost coins, if "re-supplied" to the blockchain, will quickly deflate the value of the 4 million coins existing. Overall, lost coins would have helped the remaining coins get valuable to that point. Say $10 million per BTC. If you un-lose them by the most brute force of them all -- a hard fork -- you shock the demand economics and viola, have worth-less bitcoins than you started with. Now whoever is holding their previously un-lost bitcoins cannot be happy about that. It begs the question, what would they do? Refuse the hard fork outright. Obviously. Indeed, even if the value of the bitcoins would overall increase the market cap of the available supply, we can be damn sure it would reduce the value of each individual coin. Supply and Demand. Economics 101. The Big Question: Is this a financial revolution for the better, or for the worse? For example, let's say we had 4 million coins left. We then redeem the coins in the wallets of those proven deceased without any heir. Say Satoshi Finney is that first choice. At $10 million per coin, 4 million coins give up a market cap of 40 trillion USD. Add 2 million coins to the available supply but have the price shocked from 10 million to only 7 million. Total availability supply = 6 million. 6 million x 7 million USD = 42 trillion dollars. It is a good financial explosion. But to whom? Who gets the money?? Who makes the ultimate sacrifice of accepting to end up with less money than before? If rational and educated human beings cannot agree on simple (obvious) things like "Are fossil fuels necessary for economic progress or not?", how can they agree on how to share this pie? It is a financial explosion, except the majority will not want it. Satoshi may have solved the Byzantine General's problem. Somebody needs to solve the "Goodness of the community over the Goodness of the individual" problem. Of course, with my limited brains, I know most people are wary of a Marxist dystopia sliding in during this very crucial process. E.g. The coins could instead be coded to go to a handful of powerful individuals, whereas before it seemed like they would go to people who deserved a financial turnaround. Especially in the wake of what we see today with government debt ballooning, government operatives hoping to snatch some of this reclaimed BTC so as to help the government pay its gigantic debt is not an impossibility. Talk about being stuck between a rock and a hard place. Not the best kind of sandwich. At some point, too many people will have lost too much Bitcoin that they will meet to talk about it. Bitcoin Later, they will opt to do something about it. There can only be a (decreasing number) of bitcoins L ost cryptocurrency is a unique beast, quite different from lost information for 2 reasons: ost cryptocurrency is a unique beast, quite different from lost information for 2 reasons: 1. We can continue maintaining its value. Right? Is it really still valuable? Is it really still valuable? really 2. Other people know it is lost. They simply check the blockchain. Whereas with lost information, nobody else can tell except those who have lost that intel. Take Bitcoin as an example. We still say 'There can only be 21 million'. But if this includes even lost bitcoins, then we are counting our losses as assets! That is very strange. Imagine if you were an accountant for a major company. The company deals in all sorts of assets, but sometimes writes some physical assets off as lost either due to theft, fire outbreak, or irreparable damage. But to your shock, the manager insists that not only do you keep counting those assets, but also, count them as fully functional assets whose value is going up every year. " But they are damaged ", you might say. But they are damaged " Yeah, but they are still our assets " Yeah, but they are still our assets " What, how? " What, how? " There is hope we can recover them exactly as they were before they were lost. There is hope we can recover them exactly as they were before they were lost. And then some ". And then some It’s hard to fathom. It indeed feels like Alice in Wonderland, where cards talk, a piece of cake can make one bigger, or smaller, and a cat can disappear leaving only its grin behind. Bitcoin and most crypto are the same. The question is thus; Is real value riding on all coins? Even when some are lost? Certainly, supply and demand economics might make all the coins feel equally valuable hence we would say that if Bitcoin is at $70,000 then the market cap is $1.47 trillion. But taking the figures as per this reddit post on lost bitcoins , if 6 million are lost then only 15 million coins are existing which are backed by a market cap of $1.05 trillion. reddit post on lost bitcoins Less by $420 billion. This does deflate our dreams and begs the question --> are we kidding ourselves? The road to Zero bitcoins By some estimates , 4% of 21,000,000 bitcoins continue to be lost every year. some estimates How many shall be lost, going forward? Let's assume this 4% holds true. 4% × 21,000,000 = 840,000 bitcoins. This is very steep. Well then, in 15 years, we shall lose 840,000 * 15 = 12,600,000 Add 6 million to this batch and circa 2039, 'There can only be 2,400,000 bitcoins' When will they be finished? Now, a little hope is in order. The 4% cannot hold steady forever, so it would be wise to say it too will be a declining figure as we progress. Because as Bitcoins get more valuable, people will be more careful with their keys. It is inevitable. Taking it at face value though, we shall run out of bitcoins to trade within the next 18 years. So Circa 2042. I hope the naysayers will be back to tell us they told us so, or to plead guilty of doomism and explain why there are still many millions of Bitcoins being traded daily. However, it certainly looks like way before the year 2137 when the last satoshi will be mined, we shall hit a big wall as per the coins lost vs the coins in the available supply. When enough is enough, for lost Bitcoins Assume at the time this shocking reality hits the Bitcoin community, we have only 4 million coins tradable around the world. Assume even Michael Saylor shall have lost some of his keys/coins. What then? Well, the price of a single coin, feeling so much demand, will spike to about $10 million, and then it will dawn on everybody. Bitcoin has a $40 trillion market capitalization and not a $210 trillion market capitalization. Right now, this is an elephant 🐘 in the Bitcoin room, but it is still a baby and we are all happy to pet it and feed it with careless abandon. Soon, however, it will become a giant. A big fat frustration. At $10 trillion per coin, the fees to move even a few sats will likely be thousands of dollars because; a) the Bitcoin reward is smaller. b) miners have also lost their coins. c) losing coins to hackers will be most likely (hackers will be sniffing all over every wallet, waiting for the SSL to get some slight holes) Everybody will be losing coins. It will be wild. But before I proceed? Why do people lose keys hence coins? Well, if you are using the key every time, sometimes changing it this way and that way to maintain your security (because AI powered cameras may be watching), it so happens that you are bound to make a mistake and lose the key. Or else, be hacked (since you cannot fathom changing the key all the time). Like smart people must have said, meat brains are not good at holding random numbers or random sequences of words. Luke Dash Jnr, a seasoned cypherpunk, was hacked last year. He lost 200 bitcoins. Elon Musk may have lost his Bitcon keys back in '20. So, shit happens. To hard fork those lost coins back, or not. That is the question So here's the thing. We wake up and Bitcoin is seizing up. Crashing into its mass and threatening to take our financial dreams with it when it turns into a black hole of lost keys / burned coins. We also wake up and realize that the debt spiral is still sucking us down with it, even as we hodl Bitcoin. Climate is now a real issue and oh, AI is giving us headaches. Too many addictive cat videos. Too many coins are lost, the blocks are always full so confirmations take decades, the core software is too large, the fees are too high. So we sit down, like AA group members, and decide that continuing to preach financial freedom yet the currency is fast tending towards technological obsolescence, is like being high on alcohol and thinking that one is blissful for other reasons. We need to quit the 'lost coins are good for all' alcohol. In short, it will be time for a hard fork. The Hard Fork --> Bitcoin ver. 2.0 Innovation comes full circle. Innovation comes full circle. After factoring in the reality of available supply and not merely supply, we could let Satoshi Nakamoto's 2 million or so coins be our target practice and our saving grace. One shot and bull’s eye. Trillions of dollars pour down from the Bitcoin piñata. Like manna from the heavens. Right? Alas, There shall only be 4 million bitcoins and the value of lost coins, if "re-supplied" to the blockchain, will quickly deflate the value of the 4 million coins existing. Overall, lost coins would have helped the remaining coins get valuable to that point. Say $10 million per BTC. If you un-lose them by the most brute force of them all -- a hard fork -- you shock the demand economics and viola, have worth-less bitcoins than you started with. Now whoever is holding their previously un-lost bitcoins cannot be happy about that. It begs the question, what would they do? Refuse the hard fork outright. Obviously. Indeed, even if the value of the bitcoins would overall increase the market cap of the available supply, we can be damn sure it would reduce the value of each individual coin. Supply and Demand. Economics 101. The Big Question: Is this a financial revolution for the better, or for the worse? For example, let's say we had 4 million coins left. We then redeem the coins in the wallets of those proven deceased without any heir. Say Satoshi Finney is that first choice. At $10 million per coin, 4 million coins give up a market cap of 40 trillion USD. Add 2 million coins to the available supply but have the price shocked from 10 million to only 7 million. Total availability supply = 6 million. 6 million x 7 million USD = 42 trillion dollars. It is a good financial explosion. But to whom? Who gets the money?? Who makes the ultimate sacrifice of accepting to end up with less money than before? If rational and educated human beings cannot agree on simple (obvious) things like "Are fossil fuels necessary for economic progress or not?" , how can they agree on how to share this pie? "Are fossil fuels necessary for economic progress or not?" It is a financial explosion, except the majority will not want it. Satoshi may have solved the Byzantine General's problem. Somebody needs to solve the "Goodness of the community over the Goodness of the individual" problem. Of course, with my limited brains, I know most people are wary of a Marxist dystopia sliding in during this very crucial process. E.g. The coins could instead be coded to go to a handful of powerful individuals, whereas before it seemed like they would go to people who deserved a financial turnaround. Especially in the wake of what we see today with government debt ballooning, government operatives hoping to snatch some of this reclaimed BTC so as to help the government pay its gigantic debt is not an impossibility. Talk about being stuck between a rock and a hard place. Not the best kind of sandwich.

Is Lost Cryptocurrency Still Valuable, and Can We Reclaim It? 

The Single Responsibility Principle (SRP) is one of the five SOLID principles of code design. It states that a unit of code should have only one reason to change. However, this principle is often misunderstood and misapplied, as we can see in too numerous 'big (or distributed) balls of mud' and 'spaghetti' code bases. In this article, we will explore what the SRP is and how to make following it practical and actually beneficial. First things first, why the SRP? Why should we care about it? The SRP helps write code that is easier to understand, maintain, and extend. Essentially, being able to follow the SRP is one of the key factors in writing clean code. The next thing is that the SRP can be applied not only to classes but also to methods and contracts in general. This means that the SRP is not just about classes but about any unit of code that can change. And by a 'contact' I mean a set of inputs, outputs, and behaviors that a unit of code is expected to provide. The main challenge in the SRP is exactly in defining the 'reason' to change, to outline the responsibilities - this is what the article will revolve around. The irony is that there is probably not a single developer who would not know about the SRP or would say that it is not important. However, in practice, we see high coupling, overcomplicated dependency graphs, the costs of changes and maintenance keep growing way too fast, and the code becomes a nightmare to work with - and all the while everyone knows about the SRP and even tries to follow it. How the SRP Is Often Introduced and Why It Does Not Really Help Normally, the description of the SRP starts with an example where a few different responsibilities are combined together, and then it goes by separating those responsibilities into different classes, and stating that this is it, this is the SRP. Yes, separating concerns may lead to a good and cleaner design, and it would be an example of the SRP, but it is just a limited application of it. Essentially, such examples show how to separate concerns, but not how to really apply the SRP. Once the concerns are separated, we still need to implement the functionality that uses them together in a single flow. So, the SRP is not about separating different concerns, it is about how to properly outline the responsibilities in the first place. And the definition of 'proper' comes from the definition of the SRP itself - 'a unit of code should have only one reason to change.' Let's take the of a class that compiles and prints a report, even though it is rather limited, but we can start with it. Clearly, building or compiling a report and printing or saving it into a representation are two different functionalities that are quite independent of each other. However, in the end, they form a single responsibility. Wikipedia example may Let's ask ourselves a question: is this software ever going to need a different way to compile or build a report? Or a different way to print it or save it into another representation form? Does it actually have such reasons to change? The answer to these questions comes from the business requirements - it is not about the code itself or not about the code itself. only So, the typical view on the Single Responsibility Principle is fine we balance it with proper bounds that will not lead us into a trap of over-engineering the code. It is very much like premature optimizations - just not in terms of performance or memory consumption but in terms of design and architecture. "If it is not broken, do not fix it. If it is not needed, do not add it." Do not write code or create designs that are not needed now and may never be needed. if It would be great to augment the canonical definition of the SRP - 'a unit of code should have only one reason to change' - with an understanding that the 'reasons to change' should be limited by the existing and potential, though still justified, requirements. What the piece of software that this unit of code is a part of is supposed to do, and what it is not supposed to do. Ok, all this sounds good but there may be a question: why not just separate concerns and be done with it? Even if this separation will never be required by our software, even if we will have only a single implementation of each of those concerns and probably just a single use for each of them - why not separate them? We can have a cleaner implementation, more in line with the SRP, 'clean code', etc. Again, if our software is small and simple, this separation will not actually hurt. The answer to this question is that such separation of concerns creates new abstractions, new interfaces, new classes, and new dependencies. More abstractions potentially mean higher coupling. And since we are normally dealing with large and complex software, this separation may lead to a combinatorial explosion of abstractions, and thus - higher coupling. So, it will help with making our code cleaner and easier to understand and maintain. What is often omitted when describing the SOLID principles, and the SRP in particular, is that everything comes at a cost, and we need to balance these costs in order to keep our code maintainable and understandable. not To make this statement clearer, just count how many new abstractions we have after we carelessly separated concerns, and how many new dependencies we would need to inject into our classes to make them work - the higher the numbers, the higher the coupling. Well, the next arising and rather justified question can be about the testability of such code, if we do not separate concerns. And yes, it can be very justified to separate concerns in order to make the code more testable, as this is often a part of our NFRs (non-functional requirements). But again, should we really introduce new abstractions and dependencies just to make our code more testable? Can it be tested just fine without introducing new abstractions? And the answer, surprisingly, can often be 'yes.' Following the Wikipedia example, we can still apply the SRP on the level of methods, without introducing additional abstractions, and such methods can be tested just fine. The SRP From the Point of View of Contracts The Single Responsibility Principle is a design principle, and the design often starts with contracts. We can describe the contracts in a rather wide form - as a set of inputs, outputs, and behaviors that a unit of code is expected to use and provide. This description should be good enough for the purpose of this article. Let's see how the SRP can be applied to contracts, and let's repeat its definition - 'a unit of code should have only one reason to change.' The contracts often have the most direct influence on when and why the code should change; if a contract changes, the code that implements or uses it should change as well. And while we strive to maintain the contracts stable, they connect the pieces of code together, and thus, are quite likely to change when the co-related requirements change. In other words, we do not control some of the 'reasons to change' even if the requirements for our own unit of code are stable. A contract can be described in terms of a 'dependency-consumer' relationship: another piece of code provides some behaviors used by our unit of code and those behaviors may create some data required by our unit of code. In turn, our unit of code gets consumed by other units of code. A method with its list of parameters and return values is an example of such a contract. In the same way, a class or interface provides a contract with its methods, and we can keep climbing the abstraction ladder, speaking about the contracts on the level of modules, services, APIs, and so on. When designing contracts for our code, we normally take into account this co-relation between the units of code, as it is quite clear that a contract we are designing should serve specific 'consumers' and will depend on other contracts. However, when looking at a contract from the point of view of the SRP, we should also consider that the 'reasons to change' for our contract are also influenced by those 'consumers' and 'dependencies', or if we look 'from inside out' - the behavior provided by our contract, and thus its output, influence the 'reasons to change' for the 'consumers' of our contract. If we deal with quite a stable code base where the requirements are not changing often, and our contract most likely is going to be stable, we just need to design it well with sufficient thoughtfulness regarding its consumers and dependencies. However, if we deal with a code base that is more fluid, where the requirements for our consumers and dependencies may change, we may want or will have to, either accept that our contract will keep changing as well, or design additional abstractions that will protect our contract from those changes. One such abstraction is a mapping layer that will convert the data from the representation produced by the dependencies into the representation that our contract expects, and the same way around for our consumers. In fact, this is a common practice in the design of APIs, bounded contexts, and other similar units of code. The main question is the cost - what is going to be less expensive, changing our contract and implementation when the dependencies change, thus invoking in turn potential change in our consumers, or introducing additional abstractions that will isolate our contract from those changes? In real-world scenarios, we often begin with what is faster to implement, and then, we see how it evolves. Ideally, our working process has enough space to address and solve the arising technical debt, and we can refactor our code when needed with the goal of keeping it maintainable at the lowest cost. And by the 'cost' I mean not only the time and effort needed to implement a particular change but also how costly it will be to maintain and extend the code in the future. Many of us know all too well that what costs very little to do right away may and will cost a lot in the future. So, a bit unexpectedly, the SRP got connected not only with immediate design decisions and long-term maintainability but also with the costs of maintaining the code and addressing the technical debt. Of course, the developers are not seers, but by applying the SRP properly for code design at the contract level, we can achieve better maintainability and lower future costs - both for the business and for the developers. The SRP and the Leaky Abstractions A leaky abstraction is an abstraction that leaks details of what it is supposed to abstract away. Wikipedia On the practical level, a leaky abstraction can be anything that exposes, directly or indirectly, how a unit of code works - something that the consuming code is not expected to know about or use. For example, the response object contains a public property that logically should not be there, as it exposes internal state or implementation detail that the consuming code should not be aware of. Another example is when a class method output should be additionally processed by another method of the same class in certain scenarios, which the consuming code does not control but is forced to check for. One more example is submitting a parameter that controls the input data processing while the parameter value could be deducted from the input itself or should come from another source that the consuming code does not own or use for any other purpose. Leaky abstractions are related to the Single Responsibility Principle because they introduce additional 'reasons to change' for a unit of code, even if only potentially. Leaky abstractions add to contract instability, both in terms of consumption and exposure. Let's look at leaky abstractions from the point of view of contract design, which we discussed in the previous part. A few good checkup questions to ask when designing a contract are: Does the contract really need a particular input parameter? Is the exposed behavior atomic and self-sufficient, or will the consumers have to invoke additional behaviors of our contract on a condition not determined by the consumer? Does the contract expose any internal, or even public, state that is not required by the consumers? Let's go over these checkpoints in more detail to see why they may be related to leaky abstractions and the SRP. Does the contract really need a particular input parameter? Why should we be concerned about an input parameter of a contract? Besides the obvious reason of not being used by the code, we should also consider if we can deduce this parameter from the input data itself without incurring additional costs or if the parameter is a part of the global application state, and thus, could be injected into the contract implementation at the dependency resolution stage. Well, normally we do need our parameters at the calling site, but we should be sure that it is within the responsibility of our consumers to provide them, and it is not the responsibility of our contract to obtain them. If these responsibilities are mixed up, we may have a leaky abstraction and we create an additional 'reason to change' either for our unit code or for its consumers. Is the exposed behavior atomic and self-sufficient? This may not affect our own unit of code 'reasons to change' directly, but it may affect the consumers of our contract, as this influences the contract stability. If the exposed contract behavior is not atomic, changes in its internal logic are more likely to affect the contract itself which will be a breaking change for the consumers. This would also be a violation of the SRP on the contract level as the contract should not change when only the internal behavior changes. While the contract still provides the same final result, it has changed because of internal reasons that should not be exposed to the consumers, and this is another example of leaky abstractions. Does the contract expose any internal or even public state that is not required by the consumers? As well as introducing the risk of potentially breaking changes for the consumers, exposing internal or not needed state may also lead to unexpected coupling or 'reasons to change'. Let's say the consumers of our contract came to rely on an unintentionally exposed state, and for some reason, it is impossible or too expensive now to update the consumers; while this state cannot or should not be maintained by our code because the requirements have changed, and the code should be updated. So, our code will be forced to maintain this state and implement an additional workaround to keep the consumers working - we now have an extra 'reason to change' for our code. Consuming Leaky Abstractions Of course, we should also be watchful when consuming leaky abstractions. Let's again take the Wikipedia example for the SRP regarding a class implementing a report persistence functionality. A report may be persisted into a local file, a database, or any other type of storage. Each of these methods of persistence has its own internal specifics that may leak out, and our code may mistakenly rely on them. For example, the local or remote file persistence may expose the file location that our code may want to use for some reason. When the application is expanded with a new persistence method, say, a database, our code will have to change - simply because it was built around a leaky abstraction. In a sense, leaky abstractions provide 'false contracts' that are not stable and may change at any time or simply be absent or different in other contract implementations, thus introducing additional 'reasons to change' for a unit of code and violating the SRP. Let's Summarize... Of course, all code is written in a context, and the context is the king, as it is defined by business requirements, NFRs, the current state of the code base, the current level of expertise of developers, the way the costs are balanced, and by many other factors. Nonetheless, it is possible to outline some points for additional considerations when creating designs or implementing code that would help to stay more in line with the Single Responsibility Principle within the context present at the moment. Outline the responsibilities within the units of code based on valid 'reasons to change' that are defined by the business and non-functional requirements, and not by the code itself. "If it is not broken, do not fix it. If it is not needed, do not add it." Before extracting or creating a new abstraction, consider if it is really needed - check the 'reasons to change' for already existing code and for the new abstraction. If they are not different, it may most likely be better to keep the code together. When driven by the code testability requirements, consider applying the SRP on the level of methods, not only on the level of abstractions. If the only reason for a new abstraction is to make the code more testable, try adjusting your testing approach first, and introduce new abstractions only when there is no other way. When designing contracts in a complex system, consider if the potentially required dependencies are stable enough to be used directly, or if it is better to introduce additional abstractions that will protect your code from the changes in those dependencies. The cost of adding and maintaining extra abstractions should be balanced with the cost of potentially breaking changes when the dependencies change. Check for leaky abstraction in your code and in the dependencies it needs. If you see that the abstractions are leaking, consider replacing them with more stable abstractions. The cost of relying on leaky abstractions, or better to say the risk, should be balanced with the cost of replacing them. As you see, sometimes, we should not add a new abstraction and sometimes we should - the context is the king, we just need to make sure it can rule happily for as long as possible and define the 'reasons to change' and the cost balancing are the keys in applying the SRP.

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