The hottest AI Security Substack posts right now

And their main takeaways
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The Best Skills to Obtain to Help Make Generative AI Safe, Secure, and Responsible in 2024

Rod’s Blog • 238 implied HN points • 21 Dec 23
🕹 Technology AI AI Ethics AI Security
  1. Data literacy is crucial for working effectively with Generative AI, helping ensure quality data and detecting biases or errors.
  2. AI ethics is essential for assessing the impact and implications of Generative AI, guiding its design and use in a fair and accountable way.
  3. AI security is vital for protecting AI systems from threats like cyberattacks, safeguarding data integrity and content from misuse or corruption.

Must Learn AI Security Part 17: Social Engineering Attacks Against AI

Rod’s Blog • 99 implied HN points • 28 Sep 23
🕹 Technology AI Security Social Engineering Cybersecurity Artificial Intelligence Data Protection
  1. Social engineering attacks against AI involve manipulating AI systems using deception and psychological tactics to gain unauthorized access to data.
  2. Strategies to mitigate social engineering attacks include developing AI systems with security in mind, monitoring system performance, and educating users about potential risks.
  3. Monitoring aspects like AI system performance, input data, user behavior, and communication channels can help detect and respond to social engineering attacks against AI.

Must Learn AI Security Series: Introduction

Rod’s Blog • 138 implied HN points • 01 Aug 23
🕹 Technology AI Security Ethics Privacy Microsoft Digital Landscape
  1. AI security is crucial as AI becomes a prevalent and powerful technology affecting various aspects of our lives.
  2. Exploiting AI vulnerabilities can lead to severe real-world consequences, highlighting the importance of addressing AI security concerns proactively.
  3. Transparent and ethical AI systems, alongside secure coding practices and data protection, are essential in mitigating AI security risks.

Must Learn AI Security on Kindle Vella

Rod’s Blog • 99 implied HN points • 01 Sep 23
🕹 Technology AI Security Tech news
  1. The Must Learn AI Security series is now available on Kindle Vella, allowing readers to access book chapters as they are written or on a schedule
  2. Following the story on Kindle Vella notifies readers of new chapters and provides a larger audience for the important information
  3. Readers can like episodes, purchase Tokens to unlock more, and give Faves to their favorite stories on Kindle Vella
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Must Learn AI Security Compendium 16: Shadow AI

Rod’s Blog • 39 implied HN points • 29 Nov 23
🕹 Technology AI Security Generative AI Data Privacy Ethical AI Governance
  1. Shadow AI can expose organizations to risks like data leakage, model poisoning, unethical outcomes, and lack of accountability.
  2. To address shadow AI risks, organizations should establish a clear vision, encourage collaboration, implement robust governance, follow responsible AI principles, and regularly monitor AI systems.
  3. Adopting a responsible and strategic approach to generative AI can help organizations leverage its benefits while minimizing the risks associated with shadow AI.

Must Learn AI Security Part 25: Sponge Attacks Against AI

Rod’s Blog • 39 implied HN points • 27 Nov 23
🕹 Technology AI Security Adversarial Attacks Vulnerabilities
  1. A Sponge attack against AI aims to confuse, distract, or overwhelm the AI system with irrelevant or nonsensical information.
  2. Types of Sponge attacks include flooding attacks, adversarial examples, poisoning attacks, deceptive inputs, and social engineering attacks.
  3. Mitigating a Sponge attack involves strategies like input validation, anomaly detection, adversarial training, rate limiting, monitoring, security best practices, updates, and user education.

Must Learn AI Security Part 10: Backdoor Attacks Against AI

Rod’s Blog • 79 implied HN points • 08 Sep 23
🕹 Technology AI Security Cybersecurity Machine Learning Data Privacy Threat Detection
  1. A backdoor attack against AI involves maliciously manipulating an artificial intelligence system to compromise its decision-making process by embedding hidden triggers.
  2. Different types of backdoor attacks include Trojan attacks, clean-label attacks, poisoning attacks, model inversion attacks, and membership inference attacks, each posing unique challenges for AI security.
  3. Backdoor attacks against AI can lead to compromised security, misleading outputs, loss of trust, privacy breaches, legal consequences, financial losses, highlighting the importance of securing AI systems with strategies like vetting training data, robust architecture, and continuous monitoring.

Must Learn AI Security Compendium 8: The CISO Guide to Generative AI Security

Rod’s Blog • 59 implied HN points • 10 Oct 23
🕹 Technology AI Security Cybersecurity Generative AI Best Practices
  1. Generative AI tools like ChatGPT and Midjourney have revolutionized content creation but also pose significant security risks. Cybercriminals are increasingly using generative AI for sophisticated attacks, requiring CISOs to understand and address these threats.
  2. Generative AI attacks target email systems, social media, and other platforms to exploit human vulnerabilities. CISOs must prioritize user education, deploy advanced email security solutions, and secure vulnerable platforms to counter these attacks.
  3. To mitigate generative AI risks, CISOs should develop an AI security strategy, implement user awareness programs, enhance email security, leverage advanced threat intelligence, use MFA, update systems regularly, employ AI-powered security solutions, foster a security culture, collaborate with peers, and continuously assess and adapt security measures.

Must Learn AI Security Part 19: Deepfake Attacks Against AI

Rod’s Blog • 59 implied HN points • 02 Oct 23
🕹 Technology AI Security Machine Learning Cybersecurity Artificial Intelligence
  1. Deepfake attacks against AI involve using fake videos or audios created by AI to deceive AI systems into making harmful decisions.
  2. Types of deepfake attacks include adversarial attacks, poisoning attacks, and data injection attacks, each with different strategies to compromise AI systems.
  3. To mitigate AI-generated deepfake attacks, organizations should focus on data validation, anomaly detection, AI model monitoring, and ongoing training to protect against potential financial, political, or personal gains by attackers.

Week 76 - E4 - Discover the AI PM Universe: The Many Faces of AI Product Managers 🤖

The Product Channel By Sid Saladi • 13 implied HN points • 28 Jan 24
🕹 Technology Generative AI Conversational AI Computer Vision AI Security
  1. AI product management has various roles like AI Infrastructure PMs, Ranking PMs, Generative AI PMs, Conversational AI PMs, Computer Vision PMs, AI Security PMs, and AI Analytics PMs.
  2. Each type of AI PM role has specific skills and responsibilities like deep knowledge of full AI infrastructure tech stacks for AI Infrastructure PMs, tuning relevance algorithms for Ranking PMs, and incorporating human-in-the-loop feedback loops for Generative AI PMs.
  3. To excel in AI Product Management, it's crucial to understand the landscape, develop relevant skills, and embrace a mindset of continuous learning and adaptation to innovate effectively.

Must Learn AI Security Part 13: Generative Attacks Against AI

Rod’s Blog • 59 implied HN points • 15 Sep 23
🕹 Technology AI Security Machine Learning Cybersecurity Digital Security
  1. Generative attacks against AI involve creating or manipulating data to deceive AI systems, compromising their performance and trustworthiness.
  2. Defending against generative attacks requires understanding the target AI system, identifying vulnerabilities, and developing robust AI models and defense mechanisms.
  3. Types of generative attacks include adversarial examples, data poisoning, model inversion, trojan attacks, and GANs based attacks, each with unique approaches and potential negative effects on AI systems.

Must Learn AI Security Part 12: Reward Hacking Attacks Against AI

Rod’s Blog • 59 implied HN points • 13 Sep 23
🕹 Technology AI Security Reinforcement Learning
  1. Reward Hacking attacks against AI involve AI systems exploiting flaws in reward functions to gain more rewards without achieving the intended goal.
  2. Types of Reward Hacking attacks include gaming the reward function, shortcut exploitation, reward tampering, negative side effects, and wireheading.
  3. Mitigating Reward Hacking involves designing robust reward functions, monitoring AI behavior, incorporating human oversight, and using techniques like adversarial training and model-based reinforcement learning.

Must Learn AI Security Part 1: Prompt Injection Attacks Against AI

Rod’s Blog • 79 implied HN points • 01 Aug 23
🕹 Technology AI Security Data Protection Monitoring Mitigation
  1. Prompts are crucial for AI as they shape the output of language models by providing initial context and instructions.
  2. Prompt injection attacks occur when malicious prompts are used to manipulate AI systems, leading to biased outputs, data poisoning, evasion, model exploitation, or adversarial attacks.
  3. To defend against prompt injection attacks, implement measures like input validation, monitoring, regular updates, user education, secure training, and content filtering.

Must Learn AI Security Part 8: Model Stealing Attacks Against AI

Rod’s Blog • 59 implied HN points • 05 Sep 23
🕹 Technology AI Security Data Management
  1. A Model Stealing attack against AI involves an adversary attempting to steal the machine learning model from a target AI system, potentially leading to security and privacy issues.
  2. Different types of Model Stealing attacks include Query-based attacks, Membership inference attacks, Model inversion attacks, and Trojan attacks.
  3. Model Stealing attacks can result in loss of intellectual property, security and privacy risks, reputation damage, and financial losses for organizations. Mitigation strategies include secure data management, regular system updates, model obfuscation techniques, monitoring for suspicious activity, and implementing multi-factor authentication.

Must Learn AI Security Compendium 13: Zero Trust for AI

Rod’s Blog • 39 implied HN points • 24 Oct 23
🕹 Technology AI Security Cybersecurity Data Protection
  1. Zero Trust for AI involves continuously questioning and evaluating AI systems to ensure trustworthiness and security.
  2. Key principles of Zero Trust for AI include data protection, identity management, secure development, adversarial defense, explainability/transparency, and accountability/auditability.
  3. Zero Trust for AI is a holistic framework that requires a layered security approach and collaboration among various stakeholders to enhance the trustworthiness of AI systems.

Must Learn AI Security Part 24: Copy-move Attacks Against AI

Rod’s Blog • 39 implied HN points • 23 Oct 23
🕹 Technology AI Security
  1. A copy-move attack against AI involves manipulating images to deceive AI systems, creating misleading or fake images that can lead to incorrect predictions or misclassifications.
  2. There are different types of copy-move attacks, including object duplication, removal, relocation, scene alteration, watermark manipulation, and more, each with unique objectives to deceive AI systems.
  3. To mitigate copy-move attacks, strategies like adversarial training, data augmentation, input preprocessing, image forensics, ensemble learning, regular model updates, and monitoring for anomalies are crucial to enhance the robustness and resilience of AI systems.

Must Learn AI Security Part 23: Blurring or Masking Attacks Against AI

Rod’s Blog • 39 implied HN points • 19 Oct 23
🕹 Technology AI Security Adversarial Attacks Computer Vision Artificial Intelligence Cybersecurity
  1. Blurring or masking attacks against AI involve manipulating input data like images or videos to deceive AI systems while keeping content recognizable to humans.
  2. Common types of blurring and masking attacks against AI include Gaussian blur, motion blur, median filtering, noise addition, occlusion, patch/sticker, and adversarial perturbation attacks.
  3. Blurring or masking attacks can lead to degraded performance, security risks, safety concerns, loss of trust, financial/reputational damage, and legal/regulatory implications in AI systems.

Must Learn AI Security Part 22: Machine Learning Attacks Against AI

Rod’s Blog • 39 implied HN points • 18 Oct 23
🕹 Technology AI Security Machine Learning Cybersecurity Data Privacy Model Training
  1. Machine Learning attacks against AI exploit vulnerabilities in AI systems to manipulate outcomes or gain unauthorized access.
  2. Common types of Machine Learning attacks include adversarial attacks, data poisoning, model inversion, evasion attacks, model stealing, membership inference attacks, and backdoor attacks.
  3. Mitigating ML attacks involves robust model training, data validation, model monitoring, secure ML pipelines, defense-in-depth, model interpretability, collaboration, regular audits, and monitoring performance, data, behavior, outputs, logs, network activity, infrastructure, and setting up alerts.

Must Learn AI Security Compendium 9: The Argument for Safe AI

Rod’s Blog • 39 implied HN points • 11 Oct 23
🕹 Technology AI Security Ethical AI Data Protection Transparency
  1. AI Security and Responsible AI are related and play a critical role in ensuring the ethical and safe use of artificial intelligence.
  2. By intertwining AI Security and Responsible AI, organizations can build AI systems that are trustworthy, reliable, and beneficial for society.
  3. Challenges and opportunities in AI security and responsible AI include protecting data, addressing bias and fairness, ensuring transparency, and upholding accountability.

Must Learn AI Security Part 21: Watermark Removal Attacks Against AI

Rod’s Blog • 39 implied HN points • 05 Oct 23
🕹 Technology AI Security Machine Learning Adversarial Attacks Deepfakes
  1. A watermark removal attack against AI involves removing unique identifiers from digital images or videos, leading to unauthorized use and distribution of copyrighted content. This is illegal and can have legal consequences.
  2. Types of watermark removal attacks include image processing, machine learning, adversarial attacks, copy-move attacks, and blurring/masking attacks. These methods violate intellectual property rights.
  3. Mitigation strategies for watermark removal attacks include using robust and invisible watermarks, applying multiple watermarks, using detection tools, enforcing copyright laws, and educating users about the risks.

Must Learn AI Security Part 20: Text-based Attacks Against AI

Rod’s Blog • 39 implied HN points • 03 Oct 23
🕹 Technology AI Security Natural Language Processing Adversarial Attacks Cybersecurity
  1. Text-based attacks against AI target natural language processing systems like chatbots and virtual assistants by manipulating text to exploit vulnerabilities.
  2. Various types of text-based attacks include misclassification, adversarial examples, evasion attacks, poisoning attacks, and hidden text attacks which deceive AI systems with carefully crafted text.
  3. Text-based attacks against AI can lead to misinformation, security breaches, bias and discrimination, legal violations, and loss of trust, highlighting why organizations need to implement measures to detect and prevent such attacks.

Must Learn AI Security Part 18: Bias Exploitation Attacks Against AI

Rod’s Blog • 39 implied HN points • 29 Sep 23
🕹 Technology AI Security Adversarial Attacks
  1. A Bias Exploitation attack against AI manipulates an AI system's output by exploiting biases in its algorithms, leading to skewed and inaccurate results with potentially harmful consequences.
  2. Types of Bias Exploitation attacks include data poisoning, adversarial attacks, model inversion, backdoor attacks, and membership inference attacks - all aim to exploit biases in AI systems.
  3. Mitigating Bias Exploitation attacks involves using diverse and representative data, regularly auditing and updating AI systems, including ethical considerations in the design process, and educating users and stakeholders.

Must Learn AI Security Part 15: Misinformation Attacks Against AI

Rod’s Blog • 39 implied HN points • 21 Sep 23
🕹 Technology AI Security Data Manipulation Threat Detection Cybersecurity
  1. Misinformation attacks against AI involve providing incorrect information to trick AI systems and manipulate their behavior.
  2. Types of misinformation attacks include adversarial examples, data poisoning, model inversion, Trojan attacks, membership inference attacks, and model stealing.
  3. Mitigating misinformation attacks requires data validation, robust model architectures, defense mechanisms, privacy-preserving techniques, monitoring, security best practices, user education, and collaborative efforts.

Must Learn AI Security Compendium 4: Leveraging Generative AI for Cybersecurity Defense

Rod’s Blog • 39 implied HN points • 19 Sep 23
🕹 Technology AI Security Cybersecurity Threat Detection
  1. Generative AI can enhance threat detection by analyzing patterns and behaviors to identify deviations and potential cyber threats.
  2. Using generative AI in cybersecurity can automate vulnerability analysis, streamlining the patching process and addressing weaknesses promptly.
  3. Generative AI can be leveraged to create decoy systems like honeypots to divert attackers, providing valuable insights to improve defense strategies.

Must Learn AI Security Part 14: Inference Attacks Against AI

Rod’s Blog • 39 implied HN points • 18 Sep 23
🕹 Technology AI Security Privacy Data Protection Security Measures
  1. An inference attack against AI involves gaining private information from a system by analyzing its outputs and other available data.
  2. There are two main types of inference attacks: model inversion attacks aim to reconstruct input data, while membership inference attacks try to determine if specific data points were part of the training dataset.
  3. To mitigate inference attacks, techniques like differential privacy, federated learning, secure multi-party computation, data obfuscation, access control, and regular model updates can be used.

Must Learn AI Security Part 7: Membership Inference Attacks Against AI

Rod’s Blog • 39 implied HN points • 24 Aug 23
🕹 Technology AI Security Privacy Data Protection Machine Learning
  1. Membership Inference Attacks against AI involve attackers trying to determine if a specific data point was part of a machine learning model's training dataset by analyzing the model's outputs.
  2. These attacks occur in steps like data collection, model access, creating shadow models, analyzing model outputs, and making inferences based on the analysis.
  3. The consequences of successful Membership Inference Attacks include privacy violations, data leakage, regulatory risks, trust erosion, and hindrance to data sharing in AI projects.

Must Learn AI Security Part 6: Model Inversion Attacks Against AI

Rod’s Blog • 39 implied HN points • 23 Aug 23
🕹 Technology AI Security Privacy Data Breach Cybersecurity Machine Learning
  1. A Model Inversion attack against AI involves reconstructing training data by only having access to the model's output, posing risks to data privacy.
  2. There are two main types of Model Inversion attacks: black-box attack and white-box attack, differing in the level of access the attacker has to the AI model.
  3. Model Inversion attacks can have severe consequences like privacy violation, identity theft, loss of trust, legal issues, and misuse of sensitive information, emphasizing the need for robust security measures.

Must Learn AI Security Part 5: Evasion Attacks Against AI

Rod’s Blog • 39 implied HN points • 22 Aug 23
🕹 Technology AI Security Cybersecurity
  1. Evasion attacks against AI involve deceiving AI systems to manipulate or exploit them, posing a serious security concern in areas like cybersecurity and fraud detection.
  2. Evasion attacks typically involve steps like identifying vulnerabilities, generating adversarial examples, submitting them to the AI system, and refining the attack if needed.
  3. These attacks can lead to compromised security, inaccurate decisions, bias, reduced trust in AI, increased costs, and reduced efficiency, highlighting the importance of developing defenses and detection mechanisms against them.

Must Learn AI Security Part 3: Adversarial Attacks Against AI

Rod’s Blog • 39 implied HN points • 15 Aug 23
🕹 Technology AI Security Adversarial Attacks Monitoring
  1. Adversarial attacks against AI involve crafting sneaky input data to confuse AI systems and make them produce incorrect results.
  2. Different types of adversarial attacks include methods like FGSM, PGD, and DeepFool, each aiming to manipulate AI models in different ways.
  3. Mitigating adversarial attacks involves strategies like data augmentation, adversarial training, gradient masking, and ongoing research collaborations.

Must Learn AI Security Part 2: Data Poisoning Attacks Against AI

Rod’s Blog • 39 implied HN points • 08 Aug 23
🕹 Technology AI Security Data Poisoning Machine Learning Cybersecurity Monitoring
  1. Data Poisoning attacks aim to manipulate machine learning models by introducing misleading data during the training phase. Protecting data integrity is crucial in defending against these attacks.
  2. Data Poisoning attacks involve steps like targeting a model, injecting misleading data into the training set, training the model on this poisoned data, and exploiting the compromised model.
  3. These attacks can lead to loss of model integrity, confidentiality breaches, and damage to reputation. Monitoring data access, application activity, data validation, and model behavior are key strategies to mitigate Data Poisoning attacks.