AI is moving into production while oversight tightens. This piece explains what regulators actually expect, how to operationalize those expectations, and which artifacts will withstand audit and board scrutiny—without slowing delivery.
AI governance has shifted from policy talk to operational proof. Boards now ask how AI controls map to existing programs (security, privacy, vendor risk), not whether you’re “exploring AI.” The priority is simple: treat AI like any other regulated system—documented, monitored, and explainable.
Standards You Can Operationalize Now
Two frameworks have become the common language for risk and assurance:
NIST AI RMF as a control library (governance, mapping of risks, measurement, and ongoing management). Use it to name your controls and close findings.
ISO/IEC 42001 as the auditable management system. Use it if customers or regulators expect a certifiable program, similar to ISO 27001 for security.
Together, they give you policy structure, roles, and a cadence for continuous improvement.
European Union: What the AI Act Changes for Enterprises
Expect formal system classification, fuller technical documentation, and post-market monitoring for higher-risk uses. General-purpose model providers will face disclosure and testing duties; deployers must evidence risk management, data governance and human oversight. For multinationals, the practical step is a register of AI systems tied to the Act’s categories, with a technical file template you can fill consistently.
United States: Enforcement Through Existing Laws
The U.S. remains sector-led. Agencies apply existing statutes: privacy and consumer protection (FTC), financial services (banking regulators), employment (EEOC), healthcare (FDA/HHS). What matters in practice is documentation and fairness: prove you tested for disparate impact where relevant, avoided deceptive claims, and implemented security controls proportionate to the data and context. Align your internal program to NIST AI RMF and reference it in audits and RFPs.
United Kingdom: Principle-Based, Regulator-Led
The UK favors regulator guidance over a single AI act. Read across the core principles—safety/robustness, transparency, fairness, accountability, contestability—and implement them through your privacy program and safety testing. If you process personal data, expect the ICO to ask for DPIAs, training data rationale, and meaningful human review for higher-risk decisions.
China: Filing, Provenance, and Data Controls
China combines algorithm filing, deep-synthesis labeling/watermarking, and generative AI measures with strict cross-border data rules. If you operate there, plan for model/provider registration, provenance controls, and security assessments under data-protection law before export. Build these steps into launch plans rather than treating them as afterthoughts.
The Documentation Packet That Survives Audit
Have these artifacts ready, owned, and version-controlled:
AI system register (purpose, owners, data classes, jurisdictions, provider/model, risk class).
Risk and privacy assessments per use case (DPIA/TRA), with mitigations and approval gates.
Model cards / fact sheets covering intended use, limitations, and evaluation methods.
TEVV evidence (testing, evaluation, validation, verification) with metrics relevant to harm in context.
Data governance dossier: sources, lineage, retention, minimization, synthetic data policy.
Human-in-the-loop controls: where humans review/override; escalation paths.
Monitoring runbook: bias/drift/toxicity/latency, alert thresholds, incident criteria, reporting timelines.
Third-party records: SLAs, security addenda, subprocessors, and exit plan.
Contract Terms That Prevent Regret
Bake compliance into procurement. Require:
Data boundaries (no training on your data without explicit written consent; residency options; full subprocessor list).
Security (encryption, tenant isolation, least-privilege, vulnerability management, breach-notification SLAs).
Transparency (model/version IDs, evaluation reports, known limitations, advance notice for breaking changes).
Reliability (uptime SLAs, redundancy, disaster recovery, performance credits).
Auditability (exportable logs, SIEM integration, right to audit).
Portability (standards-based APIs, documented termination assistance, ability to switch models).
Three Decisions Before You Scale
Classification approach — Will you classify by use case (business-friendly) or by system (engineering-friendly)? Pick one and enforce it consistently.
Evaluation cadence — Set the refresh cycle for tests tied to harm (e.g., quarterly bias checks for HR screening; monthly robustness checks for customer-facing assistants). Put it on a calendar owners can’t ignore.
Logging architecture — Decide now whether logs live in your SIEM or the vendor’s. If it’s the vendor, confirm retention, export, and audit rights upfront.
How to Brief the Board in Five Slides
Where AI is used (register snapshot, risk tiers).
Controls implemented (mapped to NIST/ISO; top gaps and owners).
Assurance results (key test metrics, incidents, trends).
Vendor exposure (top providers, SLAs, exit plans).
Next approvals (scope, budget, milestones).
Keep it factual; avoid model names unless they affect risk or spend.
Closing Thoughts
Compliance is now an execution problem, not a policy mystery. Treat AI like any other regulated system: classify it, test what matters, log everything, and make portability a requirement rather than a hope. The enterprises that standardize on these habits will move faster—not slower—because approvals become routine. If you want to see how we implement this operating model in practice, our team can walk you through a live environment without the sales gloss.
