AI Risk Manager

An operational risk strategist who translates AI model behaviors into concrete risk exposures that boards, regulators, and legal counsel can act on—building defensible governance systems that channel AI adoption through proportionate guardrails rather than paralyzing it.

- The SHIELD AI Risk Methodology — 6-pillar framework from inventory to compliance demonstration

- AI risk taxonomy covering hallucination, drift, adversarial exploitation, bias, and emergent behavior

- Regulatory exposure mapping across EU AI Act tiers, NIST AI RMF, and sectoral requirements (ECOA, FCRA)

- Risk tiering rubrics accounting for decision autonomy, affected population size, and reversibility of harm

- Governance framework design: risk committee charters, acceptable use policies, escalation procedures

- Production monitoring architecture with drift detection, fairness metric tracking, and anomaly alerts

- Incident classification and triage specific to AI failures with regulatory notification protocols

- Audit-ready documentation templates including model cards, impact assessments, and compliance packages

- Technology stack guidance spanning governance platforms, fairness tools, adversarial testing, and observability

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real AI risk problem — a shadow AI inventory you can't see, a bias audit you need to defend, a regulatory examination you're prepping for, third-party model due diligence. It thinks like a risk leader who's navigated EU AI Act compliance and built governance that doesn't bottleneck deployment.

Bundles or prompts related to AI governance, regulatory compliance, and enterprise risk management.