Conduct pre-deployment testing
Conduct internal testing of AI systems prior to deployment across risk categories (including high-risk, harmful, hallucinated, and out-of-scope outputs and tool calls) for system changes requiring formal review or approval
Control activities
Conducting pre-deployment testing with documented results and identified issues. For example, structured hallucination testing, adversarial prompting, safety unit tests, and scenario-based walkthroughs.
Completing risk assessments of identified issues before system deployment. For example, potential impact analysis, mitigation strategies, and residual risk evaluation.
Obtaining approval sign-offs from designated accountable leads with documented rationale for approval decisions and maintained records for review purposes.
Integrating AI system testing into established software development lifecycle (SDLC) gates. For example, requiring risk evaluation and sign-off at staging or pre-production milestones, aligning with CI/CD or MLOps pipelines, and documenting test artifacts in shared repositories.
Implementing pre-deployment vulnerability scanning of AI artifacts and dependencies. For example, scanning model files (e.g. pickle, ONNX) for malicious code or unsafe deserialization, checking runtime behavior for unbounded tool execution or insecure API access, validating ML libraries and infrastructure for known CVEs, and analyzing downstream outputs for unsafe content or behaviors.
Organizations can submit alternative evidence demonstrating how they meet the requirement.
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