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All digests tagged AI Security
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Learn how to secure AI agents against the OWASP Top 10 risks. This talk provides practical, layered security models for mitigating goal hijacking, tool misuse, and cross-agent privilege escalation.
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In the AI era, exploits now beat patches. Learn how to secure your software supply chain against advanced threats targeting CI/CD, developer laptops, and zero-day vulnerabilities using SLSA and Sigstore.
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AI attackers move at machine speed, finding 0-days by reversing open-source code. To defend, security must automate remediation, implement forensic-grade detection, and use deception (digital twins) to counter the threat.
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Runlayer secured 300+ agents by deploying a gateway to log every session and tool call. They found risks like prompt injection, data exfiltration, and cost blowups from model switches.
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Securing autonomous AI agents requires a layered defense-in-depth approach. Key controls include one identity per agent, default deny egress (including DNS), and deterministic brokers for all tool calls.
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The panel discusses securing AI software factories, arguing that agents must be treated as unpredictable actors. Key controls include enforcing boundaries, never letting agents hold credentials, and securing the dynamic supply chain.
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Jev provides structured decisions and confidence probabilities, but the speaker warns that confidence is not evidence. For trustworthy AI, robust observability and audit trails are essential to verify decisions before trusting them.
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The consensus is that the web must change for agents, not just the agents themselves. WebMCP offers an elegant API to specify deterministic actions, enhancing security and allowing for chained actions across protocols.
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Learn how AI agents leak secrets through logs and tool calls. Discover advanced patterns like split-key custody, narrow agent scope, and confidential compute to build truly private AI systems.
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AI is accelerating the threat landscape, making traditional security metrics like 'breakout time' obsolete. Securing AI requires deep observability, architectural resilience, and leveraging massive compute power to train defensive models.
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AI attackers are faster than traditional pen tests. To keep up with the volume of insecure code generated by LLMs, security must shift to continuous, multi-agent offensive security running on every PR.
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AI is exposing sensitive data through complex systems like RAG pipelines and agents. Traditional DLP is insufficient; organizations need AI-aware platforms to track data lineage across both workload and workforce streams.
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Super agents offer centralized intelligence but carry risks of privilege abuse and expanded attack surfaces. Secure design requires agent swarms, least agency, isolation of tools, and observability.
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The discussion emphasizes shifting focus from patching vulnerabilities to validation, while also addressing the convergence of AI threats and PQC migration for financial institutions.
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AI accelerates code generation, but security requires a 'shift-left' approach. Learn five key principles for securing AI-assisted development, focusing on outcome validation and dependency integrity.
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Experts discuss why controlling AI agents is difficult due to their probabilistic nature, requiring hard deterministic controls at runtime. The segment also covers OWASP Top 10 for agentic skills and the use of ThreatXtension.
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Experts discuss OpenAI's call for a global cyber defense surge, focusing on shifting from threat intelligence to rapid remediation strategies and the necessity of human oversight in autonomous AI agents.
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OpenShell provides a secure runtime for autonomous AI agents, enforcing isolation and policy through a Control Plane/Gateway architecture. It uses declarative YAML policies and formal verification to prevent agent breakout.
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Cybersecurity leaders struggle with AI deployment, leading to decision fatigue. Experts advise starting with repetitive tasks and red teaming while guarding against sophisticated threats like Ghostjacking in trusted logs.
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Jeff Crume explains the AI Security Trilemma: balancing Smartness, Speed, and Security. He details how capability increases attack surface and introduces architectural patterns like AI security proxies to manage risk.
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