What is AI governance and why is it crucial for businesses?
AI governance is how you decide, document, and demonstrate responsible AI use. It protects customers, reduces regulatory risk, and keeps innovation moving.
Definition
A practical system of policies, roles, and controls that set boundaries for AI across data, models, tooling, and outputs, plus evidence to show you follow them.
Why it matters
Regulators and buyers expect it. Even if you’re not “regulated,” enterprise customers are.
Model risks are unfamiliar. Hallucinations, bias, and data leakage can create real harm.
Governance ≠ slowdown. Guardrails speed delivery by making rules clear up front.
Core components
Use cases & risk tiers: Classify AI uses (low/med/high).
Data guardrails: No‑go data, retention, and redaction patterns.
Human‑in‑the‑loop: When must a person review or sign off?
Evaluation: Basic accuracy, bias, and safety checks for high‑risk uses.
Change control: How to approve new providers/models.
Evidence: Log prompts, outcomes, and exceptions.
Implementation basics
Publish a one‑page policy (scope, roles, no‑go rules).
Create a register of AI use cases; tag risk and owners.
Add a lightweight review for high‑risk changes.
Centralize logs (prompts/outputs) for sensitive flows.
Train staff on do’s/don’ts; track completions.
Common pitfalls
Only “model” controls; no procurement or vendor review.
No clarity on who can approve a new AI tool.
Keeping no evidence, making audits painful.
Next steps
Start with the one‑pager and the register; expand only where risk is real.