An approval gate is a defined checkpoint where a named function reviews specific evidence and records a decision before a deployment proceeds. Gates work when they are few, sequenced sensibly, and evidence-driven; they fail when they are vague ("legal needs to be comfortable") or when every gate re-reviews everything. Below is a four-gate structure most enterprises can adapt, sequenced so cheap disqualifiers come first. Review depth at every gate should scale with the use case's risk tier — low-risk cases can traverse a fast track version of the same gates.
The four gates in sequence
| Gate | When | Core question | Evidence required |
|---|---|---|---|
| Business | At intake triage | Is this worth doing, and who owns it? | Named owner, stated purpose, rough value estimate, sponsor sign-off |
| Privacy / data | Before build starts | May this data be used this way, on this platform? | Data types mapped to the sensitivity matrix, retention posture of the chosen platform, deletion plan |
| Security | Before pre-production testing | Is the integration built and operated safely? | Access model and key management, vendor/platform assurance documents, adversarial-testing results for higher tiers |
| Legal / compliance | Before launch | Do commitments, disclosures, and obligations line up? | Contract terms for the deployment path, disclosure plan, records-retention decision |
The sequencing logic: business kills bad ideas before anyone spends review effort; privacy rules on data before engineering builds around the wrong assumption; security reviews a real design rather than a slide; legal reviews the finished shape of the thing. Each gate records its decision — approved, approved with conditions, or rejected with reasons — in the AI registry, so the audit trail assembles itself.
What the privacy gate looks at, concretely
For Claude deployments, the privacy gate's platform questions have documented answers to start from. On the Claude API, Anthropic's stated default is deletion of inputs and outputs within 30 days, inputs and outputs are not used for model training by default, and zero-data-retention (ZDR) arrangements are available by contract. Note the model-level nuance: Claude Fable 5 and Claude Mythos 5 are designated Covered Models requiring 30-day retention and are not available under ZDR — so a use case's model choice is itself a privacy-gate input. On Amazon Bedrock and Google Cloud, the cloud provider is the data processor, and the gate should review those platforms' data retention and compliance terms instead.
What the security gate can ask for
Security gates run on assurance evidence, not assertions. For the Anthropic side of a deployment, Anthropic states it has obtained ISO 27001:2022, ISO/IEC 42001:2023 (the international standard for AI management systems), SOC 2 Type I & Type II, and offers a HIPAA-ready configuration with a BAA available — scoped to its commercial products such as Claude for Work and the Anthropic API, with compliance documentation available through the Anthropic Trust Center.
For higher risk tiers, the security gate should also require evidence of adversarial testing — see the red-teaming introduction — and confirm the access model: who holds API keys, how they rotate, and how access is revoked when the use case winds down.
Keeping gates from becoming bottlenecks
Three practices keep the structure fast. Publish evidence checklists per gate so teams arrive prepared instead of iterating by rejection. Let gates pre-approve patterns, not just projects — "internal summarization of Internal-tier data on the approved platform" can be approved once as a template, letting matching cases skip straight through. And time-box every gate with an escalation path to the steering committee, so a silent reviewer cannot stall a launch indefinitely.
Where to go next
Gates sit inside the intake funnel and depend on honest risk tiering. For what evidence to keep afterward, see records your regulators may ask for.