AI on AWS · Guide ·
AI governance: what every CIO needs before deploying generative AI
Policies, data boundaries, and approval workflows — before you give 500 employees a chat box.
Generative AI pilots fail in legal and security review more often than they fail in engineering. Governance is not bureaucracy — it is how you scale from 10 enthusiastic users to the whole company without a headline.
Minimum viable governance (start here)
- Approved use cases — what is in scope (summarization, internal search) vs. out (legal advice, HR decisions)
- Data classification rules — which document types may enter which systems
- Human review for high-impact outputs — define which decisions always need a person
- Model and vendor register — Bedrock models, any external API, owners, review dates
- Incident path — what happens if sensitive data appears in a log or prompt leak
Technical controls that back the policy
| Policy | Technical backing |
|---|---|
| No customer PII in pilots | Separate AWS account; synthetic data only |
| Least privilege | IAM policies per corpus; no shared admin keys |
| Auditability | CloudTrail, retention limits, access reviews |
| Cost caps | Budgets/alerts per account; per-team tags |
Sequencing that works
Week 1–2: policy draft + legal review of vendor terms (Bedrock vs. external API)
Week 3–4: pilot with named sponsors and success metrics
Week 5+: expand only after security sign-off and training snippet for users
Architecture without governance becomes shadow IT. Pair this with building secure internal ChatGPT on AWS and Bedrock vs OpenAI when you brief leadership.