Checklist

API Security & Operational Controls Checklist for AI Services

Interactive, security-focused checklist to help teams verify authentication, authorization, throttling, logging, data minimization, secrets rotation, and incident playbook integration for AI-enabled APIs and services. Save responses and evidence for audits and remediation tracking.

Interactive Tool

API Security & Operational Controls Checklist for AI Services

This checklist helps IT, SRE, security, and development teams operationalize safe, repeatable practices for exposing AI capabilities via APIs. Use it to identify gaps, record evidence, assign owners, and track remediation. Entries are saved so you can build organizational memory, measure progress, and support audits.

Suggested use: walk through the checklist during a design review, pre-production security review, or post-deployment audit. Attach links to evidence and create follow-up tasks for any items marked No or Partially.

Are all AI / model-serving endpoints protected by authentication?

Verify no endpoints expose model functionality without requiring an authenticated principal (service or user).

Primary authentication method in use

Choose the primary auth mechanism used by callers. Prefer OAuth2/OIDC for user-scoped access and mTLS for service-to-service.

Is single sign-on (SSO) integrated for user access where applicable?

SSO reduces identity sprawl and improves auditability. Note any exceptions.

Is role-based access control (RBAC) applied to limit capabilities (e.g., prompting, model selection, export)?

RBAC should limit who can call sensitive operations, change model parameters, or extract large volumes of data.

Describe key RBAC roles and least-privilege mappings

Give concise examples such as 'analyst: read-only model queries; dev: model-deploy but no customer PII access'.

Are rate limits and quotas configured to prevent abuse and runaway costs?

Include per-user and per-service quotas; ensure burst handling and backoff strategies.

If yes/partial, summarize throttling strategy (per-minute, per-second, quotas)

E.g., '100 req/min per client, 10k req/day per org, 429 response for overage'.

Is input validation and sanitization applied before requests reach the model?

Validate size, type, and reject obviously malicious payloads to avoid injection, denial-of-service, or unexpected model behavior.

Are requests restricted to send only necessary data (data minimization)?

Avoid sending full documents or PII unless strictly required; consider client-side redaction.

Is TLS enforced for all API traffic, including service-to-service?

Ensure strong TLS ciphers and certificate management; disable insecure protocols.

Is stored model data, embeddings, logs, and training data encrypted at rest?

Confirm encryption keys use enterprise key management or cloud-managed CMKs.

Is there automated detection and handling of PII in requests/responses?

Automated PII handling reduces leakage risk; note any manual exceptions.

Are detailed audit logs enabled (who, what, when, request metadata) for AI API calls?

Logs should capture caller identity, model used, request size, and response metadata while avoiding storing sensitive payloads.

Log retention policy configured and documented?

Specify retention lengths and access controls (e.g., '90 days for raw logs, 1 year for summarized audit events').

Is there a secrets management and rotation policy for API keys, service principals, and model credentials?

Include use of a secrets manager and automated rotation where possible.

Are cryptographic keys managed with an enterprise KMS (cloud or on-prem)?

KMS integration supports key rotation, access control, and audit.

Are monitoring and alerting in place for abnormal usage patterns, error rates, latency, and model drift?

Define thresholds, escalation paths, and on-call responsibilities.

Is an incident playbook available that includes AI-specific scenarios (data exfiltration, prompt injection, model misuse)?

Playbooks should integrate with your incident response tooling and include communication and mitigation steps.

Has third-party model/API risk been assessed (SLA, data handling, vulnerability management)?

Document vendor responsibilities, data residency, and acceptable use clauses.

Is there an up-to-date inventory of model-serving endpoints, dependent services, and data stores?

An inventory enables SRE runbooks, patching, and impact analysis during incidents.

If any required controls are missing, is there a remediation plan with owner and due date?

If yes, fill the Owner and Due Date fields below.

Remediation owner (name or team)

Person or team accountable for next actions.

Target remediation due date

Use YYYY-MM-DD or your team's preferred date format.

Link to evidence (runbook, config, log snippets, tickets)

Provide URLs to internal artifacts that demonstrate compliance or progress.

Overall risk rating for this service

Scale: 1 (low) to 5 (critical). Use this to prioritize remediation.

1.0 10.0

Additional notes or recommended next steps

Capture observations, stakeholders to contact, or suggested mitigations.

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API Security & Operational Controls Checklist for AI Services

API Security & Operational Controls Checklist for AI Services

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