AI Defense Matrix: Open Framework for Securing AI Systems Released

Summary

Security researchers Lenny Zeltser and Sounil Yu have released the AI Defense Matrix, an open framework designed to help organizations systematically defend their AI systems. The framework provides a structured approach to identify security gaps, assign ownership, and select appropriate controls for AI-specific assets and infrastructure.

The AI Defense Matrix extends security principles to AI-specific contexts, aligning with NIST CSF 2.0 functions. It uses a grid structure where rows represent AI-specific asset classes—including model serving platforms, AI orchestration tools, AI-generated code, and AI development environments—and columns map to the five NIST functions: Govern, Protect, Detect, Respond, and Recover. Each cell identifies the control category, objective, or representative tooling needed to secure that intersection.

The framework emphasizes that defending AI systems requires AI-specific considerations that traditional cybersecurity approaches cannot adequately address. For example, hardening an LLM inference server requires understanding model loading paths, safetensors provenance, and GPU memory isolation—areas where generic Kubernetes hardening is insufficient. The matrix is available in CSV, YAML, and Markdown formats, enabling organizations to map their current security posture, identify gaps, and develop targeted defense roadmaps based on their priorities.

• Distinguishes between 'security for AI' (the focus of this matrix) and 'AI for security' (covered in the Cyber Defense Matrix)
• Available as open-source files in multiple formats for adoption by enterprises and security vendors