Board-Ready AI Risk Posture: The 12 Questions That Prevent Regret —

Why This Matters Now

AI systems are moving from experimentation to production—often without the control framework needed for board-level accountability. The gap between deployment velocity and governance maturity creates regret: regulatory exposure, reputational risk, and operational incidents that could have been contained. This briefing provides 12 questions that pressure-test AI initiatives before they reach production, ensuring boundaries, logging, evaluation, and oversight are non-negotiable from day one.

The 12 Questions

These questions span four control domains: Boundaries (what can it access?), Governance (who approves and reviews?), Logging (can we reconstruct what happened?), and Evaluation (how do we know it works safely?). Each question maps to a specific control requirement and evidence artifact.

• Boundaries: What data can the system access? What APIs/tools can it invoke? What guardrails prevent scope creep?
• Governance: Who approved this system's deployment? What's the approval threshold for changes? Who reviews incidents?
• Logging: Are all inputs, outputs, and tool invocations logged immutably? Can we reconstruct a session for audit?
• Evaluation: How do we test for safety failures (jailbreaks, prompt injections, data leakage)? What's the evaluation cadence?
• Accountability: Who owns the risk register? What's the escalation path for incidents? How do we report to the board?
• Data Scoping: Can the system only access data it's authorized for? Are permissions enforced at runtime?
• Human Oversight: Are there approval gates for high-risk actions? Can a human pause or override the system?
• Model Provenance: Do we know which model/version is in use? Can we trace decisions back to model lineage?
• Incident Response: Do we have runbooks for AI-specific incidents (hallucination, data leakage, unauthorized access)?
• Vendor Risk: If using hosted APIs, what guarantees do we have on data retention, model training, and compliance?
• Version Control: Can we roll back to a previous version? Are changes auditable and reversible?
• Compliance Mapping: Which regulations apply (SOC 2, GDPR, HIPAA, FedRAMP)? Are controls mapped to requirements?

Implementation Path

Start with boundaries and logging—these are table stakes for any production AI system. Then layer in governance (approval workflows, incident response) and evaluation (safety testing, monitoring). The goal is to achieve 'audit-ready by default': every deployment comes with control mappings, immutable logs, and evidence packs. For enterprises, this becomes a repeatable pattern that scales across all AI initiatives.

Deliverable Standard

When we deliver this briefing to clients, it includes: (1) Control mapping to your compliance framework (SOC 2, FedRAMP, etc.), (2) Evidence pack template with sample logs and approval workflows, (3) Board reporting template with risk posture summary, (4) Prioritized implementation backlog with effort estimates. It's designed for executive sign-off and audit scrutiny.