Here’s a structured list of 50 auditing rules for AI workflow automation, covering governance, compliance, technical integrity, and operational safety. I’ve grouped them into domains for clarity.
1. Governance & Compliance
- Verify the workflow aligns with organizational AI governance policies.
- Ensure all automation steps comply with applicable laws (e.g., GDPR, HIPAA).
- Check that model usage respects licensing and open-source obligations.
- Confirm that data processing agreements (DPAs) are in place for third-party integrations.
- Audit adherence to industry standards (e.g., ISO/IEC 42001 for AI management systems).
- Validate that workflow changes are subject to approval through change management.
- Ensure automated decision systems provide human-in-the-loop options where required.
- Confirm bias and fairness assessments are logged and reviewed.
- Verify that audit logs are immutable and tamper-proof.
- Ensure ethical AI principles (transparency, accountability) are explicitly documented.
2. Data Integrity & Privacy
- Verify input datasets are validated, cleansed, and version-controlled.
- Ensure PII/PHI is anonymized or encrypted before processing.
- Confirm retention policies are enforced for intermediate and output data.
- Audit data lineage from ingestion → preprocessing → output.
- Check that sensitive data isn’t exported to non-authorized services.
- Ensure secrets (API keys, OAuth tokens) are stored in secure vaults.
- Verify compliance with data minimization principles.
- Test whether unauthorized modifications of input data trigger alerts.
- Confirm that access to training/serving datasets is role-based.
- Ensure reproducibility of data splits used in model training.
3. Workflow Logic & Control
- Verify workflows implement error handling for failed API calls.
- Ensure retries/backoff policies are implemented to prevent cascading failures.
- Audit conditional branching logic for unintended bypasses.
- Confirm deterministic workflows where determinism is required (e.g., auditing pipelines).
- Ensure workflows enforce rate limiting for external APIs.
- Verify loops and recursion have termination checks.
- Audit scheduling (cron/trigger) to prevent excessive execution.
- Ensure that escalation paths exist for stuck workflows.
- Validate that rollback/compensation logic exists for partial failures.
- Check workflow isolation: one automation should not impact another without control.
4. Security Controls
- Ensure workflows authenticate against services with least privilege.
- Verify transport encryption (TLS/HTTPS) for all external connections.
- Confirm that stored outputs are encrypted at rest.
- Ensure identity propagation (OAuth, JWT) is verified and logged.
- Audit integrations for possible SSRF or injection attacks.
- Verify rate limits against brute-force or DDoS via automation triggers.
- Confirm sandboxing of untrusted model outputs (e.g., generated code).
- Audit workflows for hardcoded credentials or tokens.
- Ensure anomaly detection exists for unusual workflow execution patterns.
- Verify auditability of access to workflow definitions and configurations.
5. Model & AI-Specific Checks
- Validate that models used in automation have passed fairness/robustness tests.
- Confirm inference outputs are validated before being consumed downstream.
- Ensure fallback logic exists for low-confidence AI responses.
- Audit model versioning in the workflow for traceability.
- Verify retraining triggers (data drift, concept drift) are logged and approved.
- Ensure explainability metadata (e.g., SHAP, LIME outputs) is captured for review.
- Confirm that workflow doesn’t propagate hallucinations into critical systems.
- Audit prompt templates for injection vulnerabilities.
- Verify red-teaming/adversarial testing was performed on workflow AI components.
- Ensure continuous monitoring of AI outputs for bias, toxicity, or harmful actions.
✅ These 50 rules create a baseline auditing checklist for AI workflow automation across governance, data, logic, security, and AI-specific dimensions.