AI Governance
Questions Answered.

AI governance is the operational discipline of documenting what AI systems your organization uses, assessing their risks, and implementing controls that are defensible to regulators, customers, and boards. It moves beyond "we have a policy" to "we have evidence of control."

Even "off-the-shelf" AI creates liability: data leakage, hallucinated outputs, bias in hiring or customer decisions, and vendor lock-in. Governance isn't about stopping use—it's about proving you assessed risk before deployment and have monitoring in place.

Ethics asks "should we?" Governance asks "how do we control it, document it, and prove it to an auditor?" Method 9 focuses on the operational mechanics: inventories, risk scoring, evidence capture, and oversight cadence.

Start with a use-case intake that scores capability risk (what the AI does) and context risk (where it operates). Shadow AI is discovered by asking business units direct questions about tools they've procured without IT/legal review—and validating against expense records.

Shadow AI is any AI tool used by employees without governance approval, security review, or legal sign-off. It's dangerous because you can't control what you don't know exists—and regulators are increasingly asking for complete AI inventories.

Every organization needs at minimum: an AI Acceptable Use Policy (what employees can/cannot do), a Vendor Assessment Protocol (how you evaluate AI suppliers), and an Incident Response Procedure (what happens when AI fails).

Evaluate across four dimensions: data handling (where does my data go?), model transparency (can we explain decisions?), security posture (SOC 2, penetration testing), and business continuity (what if the vendor changes terms or goes under?).

You can't eliminate them, but you can control for them: implement pre-deployment testing (red-teaming for high-risk use cases), ongoing monitoring with human-in-the-loop checkpoints, and documented acceptance criteria tied to protected classes.

The NIST AI RMF is a voluntary U.S. standard built around four functions: Govern (set principles), Map (inventory and context), Measure (test and evaluate), and Manage (respond and monitor). It's quickly becoming the baseline expectation for enterprise AI accountability.

The EU AI Act regulates AI based on risk level (minimal, limited, high, unacceptable). If you have employees or customers in the EU, or use AI for hiring/credit/scoring decisions globally, you likely have obligations starting in 2026.

ISO/IEC 42001 is the international AI management systems standard—essentially "ISO 9001 for AI." It provides a certifiable framework for governance, but certification requires documented controls, evidence, and external audit.

Inventory your existing AI, assess the highest-risk use cases, and implement targeted controls for those specific systems. Rather than trying to govern everything at once, prioritize by risk and establish a governance cadence of monthly reviews and quarterly re-assessments to ensure evidence of control.

Frame governance as liability protection and competitive advantage, not cost center. Show the specific regulatory exposure (fines, litigation, reputational damage) and the cost of retroactive compliance versus proactive control.

Auditors want evidence: an AI inventory, risk assessments per system, documented controls, testing results, and an escalation log. Method 9's approach produces an "Annual Assurance Report" that packages this into an audit-ready deliverable.