AI API Compliance Checklist: GDPR, SOC 2, Enterprise AI

Enterprise buyers rarely ask only whether your AI feature works. They ask where data goes, who can access it, how long logs are retained, which vendors process prompts, and whether usage can be audited.

If your product uses LLM APIs, compliance needs to be part of the architecture.

Know what data you send

Document every category of data sent to model providers:

user messages
uploaded files
retrieved documents
account metadata
tool outputs
conversation history
support transcripts
analytics events

You cannot answer compliance questions if you do not know your data flows.

Track vendors and subprocessors

If you route requests to multiple providers, maintain a clear list of vendors and when each one is used.

Track:

provider name
region
data retention policy
model family
processing purpose
customer opt-out options

Enterprise buyers will ask.

Retention controls

Decide how long you keep:

prompts
responses
request metadata
uploaded files
embeddings
logs
evaluation data

Shorter retention reduces risk, but support and debugging may require some history.

Access control

Limit access to prompt and response logs. Use role-based permissions and audit access to sensitive AI data.

Raw prompt logs should not be treated like ordinary application logs.

Audit trails

For enterprise readiness, log:

user or service identity
model used
timestamp
request status
token usage
fallback behavior
admin changes
key creation and revocation

This helps with both security reviews and customer support.

Data processing agreements

For GDPR and enterprise procurement, you may need data processing agreements with AI vendors. Review whether providers use customer data for training, how they handle deletion, and which regions process data.

Final thoughts

AI compliance is easier when your LLM calls go through one controlled layer. Centralized routing, logging, retention, and vendor tracking make enterprise reviews far less painful.