AI Governance for Business Systems: Policies, Approvals and Evidence
A practical AI governance framework covering model authority, sensitive data, human approvals, evidence, evaluation, monitoring, incident response and vendor risk.
Govern the capability, not the model name
AI governance should describe what a system can access, infer, generate, recommend and change. Two products using the same foundation model may have radically different risk because one drafts internal notes while another can communicate with customers or alter business records.
Inventory AI use cases and classify them by data sensitivity, external impact, reversibility, financial consequence and human oversight.
Authority boundaries
Define actions models may perform automatically, actions requiring deterministic validation and actions requiring human approval. These boundaries should be enforced in software rather than written only in policy.
A model should never be able to expand its own permissions or bypass the approval mechanism that constrains it.
Evidence and explainability
Store source material, tool outputs, policy decisions and significant prompts required to reconstruct high-impact outcomes. When the system makes an inference, distinguish that from a verified fact.
Operational explainability comes from evidence, decisions and actions that can be audited; it does not require exposing private model reasoning.
Evaluation and change management
Models and prompts change behaviour. Treat upgrades as software changes with regression tests, red-team cases, sensitive-data checks and rollback capability. Monitor production for drift and unexpected tool use.
Evaluation should include refusal quality and policy compliance, not only task completion.
Incident response and vendor risk
Plan for leaked data, compromised integrations, harmful output, provider outages, model degradation and abusive users. Know how to disable a capability quickly without shutting down unrelated systems.
For external AI providers, assess data handling, retention, access controls, subprocessors and contractual commitments rather than assuming the word enterprise resolves the risk.
What to do next
If this is the problem you are solving, start with the operating constraints and evidence rather than a technology shopping list. The related service page explains the engineering approach.
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