Most enterprise AI governance fails in one of two directions: a review board so heavy that teams route around it, or a policy document so vague it governs nothing. With agents now taking real actions in production systems — Amazon's AgentCore evaluates every agent action for prompt injection and data exposure precisely because agents act — governance has become an operating discipline, not a compliance artifact.
Here is a framework that scales from first pilot to hundreds of use cases.
The Five Components
1. A Living Model and Use-Case Inventory
You cannot govern what you cannot see. Maintain a register of every AI use case: the model and vendor, the data it touches, the actions it can take, and a named owner. Shadow AI is the default state of every large organization; the inventory's job is to make registration easier than hiding.
2. Risk Tiers, Not Blanket Rules
Tier use cases by blast radius, and scale oversight to tier:
Tier 1 — Internal, read-only (summarization, search): self-service, automatic approval.
Tier 2 — Internal, write-capable (agents updating systems): eval suite + rollback plan required.
Tier 3 — Customer-facing (support, content): human review thresholds + brand/safety evals.
Tier 4 — Regulated or high-stakes (credit, health, employment): formal review, documented human accountability, audit logging.
The EU AI Act's obligations for high-risk systems and general-purpose model transparency map naturally onto Tiers 3–4; building the tiers first makes regulatory compliance a byproduct instead of a scramble.
3. Human Oversight That Names a Human
"Human in the loop" fails as a slogan. For each Tier 2+ use case, specify: who reviews, what triggers escalation, and who is accountable when the agent is wrong. If the answer is "the vendor," you don't have oversight.
4. Vendor and Model-Lifecycle Governance
Your governance inherits your vendors' behavior. Contract for: no-training-by-default data terms, deprecation notice periods (forced model retirements with hard cutoffs are now routine across providers), status transparency, and clarity on where inference runs. For regulated workloads, prefer deployment modes that keep data inside your perimeter — several vendors now support private deployment on a handful of GPUs.
5. Incident Response for AI Failures
AI incidents look different from outages: silent quality degradation, prompt injection, data leakage through model output, an agent taking a wrong-but-plausible action. Extend your incident process with AI-specific detection (eval regression alerts, anomalous action monitoring) and a kill switch per use case — a way to disable the AI path and fall back to the human process within minutes.
Making It Fast: The Governance SLA
The framework only survives if it's fast. Set internal SLAs: Tier 1 approval is instant, Tier 2 within a week, Tier 3–4 within a month. Publish the SLAs. When governance is predictable, teams stop routing around it.
What to Do This Quarter
Stand up the inventory (a spreadsheet is fine; completeness beats tooling).
Tier everything currently running.
Write the Tier 3–4 oversight rules with named owners.
Add deprecation and data-terms clauses to your standard vendor language.
Run one AI incident tabletop exercise.
