Data risk

Establish and maintain documented provenance and lineage for all training, fine-tuning, and evaluation data used by AI systems, including legal basis, licensing, sensitivity classification, and transformations applied.

Validate that production-bound LLM and agentic AI systems have been tested against direct prompt injection, indirect prompt injection, jailbreak, refusal evasion, and (where applicable) multi-modal adversarial inputs, with documented findings, remediation, and re-test cycles.

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.

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.

Maintain a tested AI-specific incident response capability that detects, triages, contains, and recovers from AI incidents (prompt injection, model misbehavior, data exfiltration, decision errors with material impact), with regulator notification per applicable rules.

Validate performance, fairness, and disparate-impact metrics for AI systems with protected-class implications, using methods appropriate to the system type and use case, with documented findings and remediation.

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.

Define and enforce retention and deletion of AI-related data (training datasets, fine-tuning data, embeddings, prompts, completions, model artifacts, logs) per a documented schedule aligned to legal, regulatory, and operational requirements.

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.

Ensure that AI system decommissioning follows a documented procedure addressing dependent system continuity, data retention/deletion, model artifact handling, vendor termination assistance, documentation preservation, and stakeholder communication.

Detect, triage, and remediate use of unsanctioned AI services and unauthorized AI tooling by employees, contractors, and other authorized users, with documented response and metrics.