Adaptive Asymmetric Cyber Defense
Traditional static defense mechanisms are no longer sufficient to protect critical infrastructure, sensitive data, and digital assets from sophisticated adversaries.
AI Security Strategy, Tactics and Tradecraft.
For professionals. By professionals.
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AI Supply Chain Security
Recent incidents have highlighted the extensive impact of supply chain attacks, affecting not only traditional software systems but also AI-specific contexts.
A First Principles Approach to Cybersecurity
This article explores the shift from traditional defense-in-depth strategies to a more proactive, AI-driven approach: deception-in-depth.
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Asymmetric Cyber Defense through Autonomous Threat Engagement
Abstract As cyber threats become increasingly sophisticated, traditional defensive measures struggle to keep pace. Asymmetric cyber defense, where defenders face
MIT's AI Risk Repository: A Game-Changer for AI Security
MIT's new AI Risk Repository represents a significant leap forward in our ability to systematically identify, categorize, and mitigate AI-related risks.
Unlocking the Impact: Safeguarding Against AI Security Breaches
In this article, we delve into the important topic of safeguarding against AI security breaches. We explore the various types of threats that AI systems can face, from data breaches and unauthorized access to adversarial attacks that manipulate AI algorithms.
Protecting Your Digital World: The Role of AI in Cybersecurity
AI can analyze vast amounts of data, identify patterns, and detect security breaches in real-time. It can automatically flag and respond to potential threats, preventing damage before it occurs.
Adaptive Asymmetric Cyber Defense
Traditional static defense mechanisms are no longer sufficient to protect critical infrastructure, sensitive data, and digital assets from sophisticated adversaries.
AI Supply Chain Security
Recent incidents have highlighted the extensive impact of supply chain attacks, affecting not only traditional software systems but also AI-specific contexts.
Model Hijacking: Collaborative AI's Hidden Threat
While cybersecurity professionals are familiar with traditional threats like data breaches and DDoS attacks, federated learning presents unique dangers, such as model hijacking and neural network trojans.
Generative AI: The Hidden Data Security Minefield
The data pipeline for these AI models is fraught with vulnerabilities, making each stage a potential target for security breaches. For security professionals, addressing these risks is crucial.
Securing Generative AI Systems
This essay explores the unique security considerations surrounding generative AI and why I believe this will be the next significant frontier in cybersecurity.
Adversarial Attacks on AI Systems: Insights from Adversarial Policies Against Superhuman Go AIs and Beyond
Adversarial Attacks on AI Systems
Navigating the Complex Landscape of AI Security: Threats and Countermeasures in Artificial Intelligence Systems
Threats and Countermeasures in Artificial Intelligence Systems