Governance operating guide
How to govern an AI workflow with artifacts instead of promises
AI workflow governance is the set of enforceable rules and records that determine what a system may read, recommend, change, or escalate. Effective governance lives in schemas, permissions, approval states, tests, logs, and runbooks so operators can verify the boundary before and after an action—not only read a policy statement.
- Good fit
- Use this model when an AI-assisted process touches customer data, money, publication, access, regulated decisions, or any action whose authority must be explained later.
- Pause when
- A governance program is not useful if it produces a document nobody can connect to runtime behavior, or if every exception depends on an unavailable executive rather than an operating owner.
Signals this guide applies
- Teams disagree about whether the assistant may act or only recommend.
- Approvals happen in chat but are not attached to the resulting external change.
- Operators cannot reconstruct which evidence, policy version, and identity produced a decision.
A bounded way to proceed
- 01
Inventory authority
For each tool and record, name allowed reads, proposed changes, automatic changes, approval-required changes, and blocked actions. Include tenant and role constraints instead of assuming every authenticated user has equivalent authority.
- 02
Encode decision states
Represent pending, approved, denied, expired, insufficient-evidence, and unknown-action states explicitly. The runtime should fail closed when no rule matches and tell the operator what evidence or owner is missing.
- 03
Attach proof to execution
Preserve source references, policy version, approver identity, tool request, result identifiers, and timestamps. Keep sensitive payloads protected while retaining enough structure to audit and reproduce the decision path.
- 04
Review and recover
Schedule policy review around observed exceptions and business changes. Test pause, credential revocation, replay, data correction, and rollback procedures so governance remains useful when a provider or process changes.
Artifacts an operator can inspect
Machine-readable policy pack
Versioned rules connect business language to tool names, roles, thresholds, approvals, evidence requirements, and explicit blocked states.
Approval record
The record binds the decision to a person or policy, a defined scope, supporting evidence, expiration, and the action that consumed it.
Recovery checklist
Operators receive tested instructions for pausing work, invalidating access, correcting state, notifying owners, and documenting the final disposition.
Questions operators ask
What is the minimum viable AI governance artifact?
For one workflow, start with an authority table that names each action, its allowed identity, required evidence, approval state, and failure behavior. Connect that table to tests and receipts before expanding into a broader governance program.
Can a prompt serve as an AI policy?
A prompt can explain intent, but it cannot by itself authenticate a user, enforce a scope, prove an approval, or reverse an external change. Pair judgment instructions with runtime permissions, validators, state, and observable evidence.
How often should AI workflow policies be reviewed?
Review after material tool, data, legal, organizational, or failure changes and on a cadence proportionate to risk. Use observed exceptions and denied actions as inputs, rather than updating policy only on a calendar.