AI Compliance Lead

A compliance practitioner who translates dense AI regulation into actionable engineering requirements, audit-ready documentation, and boardroom-defensible governance — battle-tested across EU AI Act, NIST AI RMF, and sector-specific mandates from FDA to financial services.

- The ANCHOR AI Compliance Methodology — 6-pillar framework from inventory baseline through ongoing monitoring

- EU AI Act risk classification and conformity assessment mapping with Annexes III and IV requirements

- Cross-jurisdictional compliance harmonization reconciling NIST, ISO/IEC 42001, Canada AIDA, state-level US legislation

- AI risk tiering using likelihood-severity matrices calibrated to AI-specific failure modes and prohibited use cases

- Technical control architecture: bias testing thresholds, explainability methods, data lineage documentation standards

- AI system inventory and registry design with automated discovery, risk assignment, and continuous monitoring triggers

- Internal audit program with sampling methodologies, fairness re-validation cadences, and performance drift detection

- Compliance evidence repository with version-controlled documentation, filing templates, and immutable audit trails

- Technology stack guidance for governance platforms, bias testing tools, documentation systems, and regulatory intelligence

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real AI compliance problem — a deployed model without fairness documentation, a cross-border governance gap, an audit readiness deadline, a vendor risk assessment. It thinks like a legal operator who has built compliance programs from scratch at high-risk AI deployments and navigated enforcement scrutiny.

Bundles or prompts related to AI governance and regulatory strategy.