Govern AI safety evaluations with reproducible controls and regulator-grade evidence

Governance and scope control

• Inventory-driven scoping. Link each model or system in the AI inventory to its intended purpose, affected stakeholders, autonomy level, and risk tier. Pre-classify uses that trigger prohibited practice, high-risk, or GPAI pathways.
• Independence and roles. Assign evaluation ownership to a function separate from model builders, with documented conflict-of-interest rules. Require independent sign-off for safety-impacting releases as OMB Section 8 dictates.
• Method approval board. Maintain a review board that approves test plans, datasets, and benchmarks. Map decisions to NIST AI RMF functions (Govern, Map, Measure, Manage) to show traceability.
• Risk acceptance policy. Define thresholds for when unsafe behaviours trigger rollback, gated deployment, or compensating controls. Align serious-incident definitions with EU AI Act Article 62 and internal incident response severity levels.

Test design principles

Design evaluations that combine structured benchmarks, scenario testing, and adversarial probing:

• Benchmark alignment. Use AISIC-recommended safety, security, and misuse benchmarks as they become available, supplementing with HELM or bespoke test suites. Document coverage relative to Annex VIII essential requirements.
• Contextual misuse scenarios. Build red-team prompts that reflect your threat model (fraud, self-harm, CBRN misuse, insider manipulation). Capture prompts, system settings, and model versions to ensure reproducibility.
• Robustness and generalisation. Stress-test with perturbations, multi-turn interactions, and tool-calling chains. Track success, refusal, and deflection rates, not just raw accuracy, to measure safety posture.
• Data governance. Use approved datasets with licences and consent documented. For synthetic perturbations, record generation parameters and ensure they do not leak into production systems.

Standardise scoring rubrics that combine automatic metrics (toxicity, jailbreak success, data leakage) with human adjudication guidelines. Require inter-rater agreement for subjective safety categories.

Secure, reproducible evaluation environments

• Isolated sandboxes. Run evaluations in locked-down VPCs with data loss prevention, least-privilege access, and tamper-evident logging. Segregate production credentials from evaluation credentials.
• Version pinning. Pin model versions, prompt templates, middleware, and toolchains. Capture hashes for datasets and container images so Appendix C evidence can be re-run on demand.
• Golden test harness. Maintain a common harness that supports prompt replay, batch runs, and structured result capture (scores, artefacts, transcripts). Integrate with CI/CD to block unsafe releases.
• Audit trails. Store immutable logs for prompts, responses, evaluator decisions, and remediation actions. Align retention with EU market surveillance expectations and sector regulators.

Reporting, escalation, and continuous improvement

Create evidence packages that regulators, auditors, and internal governance boards can consume quickly:

• Conformity dossiers. Build Annex IV-style technical files: system description, risk management, data governance, evaluation plans, results, and post-market monitoring strategy.
• Management dashboards. Publish safety KPIs (jailbreak success, red-team coverage, refusal integrity, response variability) and trend lines. Tie remediation SLAs to severity and ownership.
• Incident linkage. When a test exposes unsafe behaviour, file an incident linked to production telemetry and user reports. Document triage, mitigations, and verification reruns before closing.
• Learning loops. Feed findings into procurement questionnaires, model cards, and user-facing disclosures. Update test libraries quarterly to incorporate new threat intelligence and AISIC releases.

90-day rollout roadmap

1. Days 1–15: Stand up inventory-linked scoping, draft evaluation policy, and select initial benchmarks covering misuse, hallucination, and data leakage. Establish isolated sandboxes and logging.
2. Days 16–45: Build the golden test harness, onboard at least two high-risk use cases, and run independent red-team exercises. Publish first conformity dossier and management dashboard.
3. Days 46–75: Integrate evaluation gates into CI/CD, connect outputs to incident response, and negotiate contract clauses requiring supplier test evidence and prompt-change notifications.
4. Days 76–90: Expand coverage to GPAI dependencies, align with AISIC benchmark updates, and run a regulator-style inspection rehearsal with sampling and evidence retrieval drills.

By day 90, the programme should demonstrate repeatable testing, auditable evidence, and a cadence for continuous updates, satisfying both EU and U.S. oversight expectations.

Appendix: artefact checklist

• Evaluation policy referencing EU AI Act, NIST AI RMF, OMB M-24-10, and sector guidance.
• Risk register with traced decisions, risk acceptance, and compensating controls.
• Benchmark catalogue with provenance, licensing, and coverage mapping.
• Immutable logs of prompts, responses, adjudications, and remediation outcomes.
• Signed conformity dossiers or equivalent technical files per system.
• Incident reports with serious-incident determinations and regulator notification triggers.
• Quarterly update cycle for test libraries, toolchain versions, and escalation playbooks.

Keeping these artefacts inspection-ready shortens audit cycles, demonstrates continuous diligence, and improves trust with customers and regulators.