AI Security Measurement: Benchmarks Are Not Enough
Measure AI security using process-driven standards like BSIMM and avoid relying solely on benchmarks.
Implement process-driven AI security assurance using BSIMM-like frameworks.
Summary
The report argues that AI security cannot be reliably measured using conventional benchmarks, as these fail to capture emergent properties such as security. It traces the evolution of software security engineering from black‑box penetration testing to white‑box code analysis and finally to process‑driven standards like BSIMM. The authors contend that AI security should adopt similar maturity models, focusing on architectural risk analysis and assurance processes rather than simplistic metrics. They note that no current security meter exists for AI systems, emphasizing the need for rigorous risk management. The paper calls for cleaning up the WHAT piles and applying proven assurance practices to AI deployments. It suggests that AI security measurement should mirror established software security practices to provide actionable insights. The conclusion stresses that vigilance is essential, as benchmarks alone cannot guarantee secure AI behavior.
Key changes
- AI security cannot be reliably measured using conventional benchmarks
- Software security evolved from black‑box testing to BSIMM-like maturity models
- AI security should adopt process‑driven assurance rather than metrics
- No current security meter exists for AI systems
- The report calls for cleaning up the WHAT piles and applying proven assurance practices
- AI security measurement should mirror established software security practices
- Vigilance is essential because benchmarks alone cannot guarantee secure AI behavior