AI Governance
Also known as: AI policy, AI controls, AI oversight
Definition
AI governance defines approved tools, restricted data, human review, customer-facing output standards, agent oversight, incident reporting, and risk review. In a growing business, governance should be practical enough to help safe workflows move faster.
Governance is not a blocker when it is designed well. It creates clear rules so employees know which tools they can use, what data is restricted, and when output needs review.
Weak governance usually shows up as shadow AI, data leakage, inconsistent customer communication, and unowned tools.
Related terms
- AI Acceptable-Use Policy — A company policy that defines approved AI tools, restricted data, human review, and escalation rules for employee AI use.
- Human-in-the-Loop — A workflow design where a person reviews, approves, corrects, or escalates AI output before sensitive action is taken.
- Shadow AI — Employee use of AI tools without company visibility, approval, data rules, or review standards.
Where this gets applied
- Process Documentation — Sales process, customer success playbooks, technical runbooks, financial close calendars, hiring rubrics.
- Technical Debt — Quantification in dollars, not adjectives. Then a remediation plan that runs in parallel with delivery.
- Compliance & Security — SOC 2, CMMC, FedRAMP, security baselines for post-acquisition standardization.