The Artificial Intelligence Security Verification Standard (AISVS) is a community-driven catalogue of testable security requirements for AI-enabled systems. It gives developers, architects, security engineers, and auditors a structured framework to design, build, test, and verify the security of AI applications throughout their lifecycle, from data collection and model training to deployment, monitoring, and retirement.
The Artificial Intelligence Security Verification Standard (AISVS) is a community-driven catalogue of testable security requirements for AI-enabled systems. It gives developers, architects, security engineers, and auditors a structured framework to design, build, test, and verify the security of AI applications throughout their lifecycle, from data collection and model training to deployment, monitoring, and retirement.
AISVS is modeled after the OWASP Application Security Verification Standard (ASVS), and every requirement is intended to be verifiable, testable, and implementable.
AISVS 1.0 is the latest stable release. The standard is available as a PDF and as Markdown source in the project's GitHub repository. Development of AISVS 1.01 is in progress in the 1.01-dev directory; stable release folders are locked.
Level 1 — Essential baseline controls for all AI applications, including internal tools and low-risk systems.
Level 2 — Standard controls for production systems, customer-facing AI, systems processing personal data, or making consequential decisions.
Level 3 — Advanced controls for critical infrastructure, safety-critical AI, high-value targets, regulated industries, and other high-assurance environments.
Most production systems should aim for at least Level 2.
During design — Use requirements as a security checklist when architecting AI systems.
During development — Integrate requirements into CI/CD pipelines, code reviews, and testing.
During security assessments — Use AISVS as a verification framework for penetration testing and audits.
For procurement — Reference specific requirements when evaluating AI vendors and third-party models.