Deployment

Ensure that third-party AI products and AI-enabled services are subject to risk-based due diligence covering data handling, model provenance, security testing, incident response, compliance posture, and contractual safeguards before procurement and on an ongoing basis.

Ensure that each AI system is subject to a documented risk assessment and AI System Impact Assessment (AIIA) aligned with ISO/IEC 23894 and ISO/IEC 42005, completed prior to production deployment and refreshed on material change or annually.

Ensure that AI systems producing decisions with legal or similarly significant effects on individuals incorporate meaningful human oversight in the decision flow, with documented design, training, and audit trails.

Apply layered output filtering and content moderation to LLM and generative AI systems to prevent disclosure of sensitive data, prohibited content, executable payloads, and policy-violating outputs.

Ensure that AI systems produce logs sufficient to detect abuse, investigate incidents, demonstrate compliance, and reconstruct decisions, with logs protected from tampering and retained per regulatory requirements.

Ensure that every model promoted to production is uniquely versioned, traceable to its training data, evaluation results, and approval, and subject to controlled change-management with rollback capability.

Maintain current, accessible technical documentation (model cards, datasheets, system cards) for every production AI system, sufficient to enable risk-based use, regulatory review, and downstream consumer understanding.

Ensure that personal data used in training, fine-tuning, evaluation, and inference is minimized to the data strictly necessary for the stated purpose, processed under a defensible lawful basis, and subject to the rights of affected individuals.

Require explicit, documented evaluation against defined thresholds — covering performance, fairness, robustness, safety, and (where applicable) drift baselines — as a gate to production deployment for every AI system.

Restrict agentic AI systems to least-privilege tool authorizations; enforce approval boundaries for high-impact actions; log and review all tool invocations; and prevent privilege escalation across multi-step or multi-agent workflows.