Cybersecurity

Why a Pre-Deployment Checklist?

Prompt Injection Local AI Credentials Hashing

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Key Takeaway: AI compliance is not a single checkbox — it is a structured pre-deployment process covering 10 domains with 50 items. Use this checklist to systematically verify your AI system is ready for production.

⚠️ Not Legal Advice: This checklist is an educational tool for developers. It does not constitute legal, compliance or regulatory advice. Requirements vary by jurisdiction, industry and use case. Consult qualified professionals for specific compliance requirements.

Disclaimer: This article provides general educational information about AI compliance practices. It is not legal advice. Compliance requirements vary by jurisdiction, industry and use case. Consult qualified legal or compliance professionals for specific requirements.

Why a Pre-Deployment Checklist?

AI systems fail in ways that traditional software does not. A conventional application crashes visibly. An AI system can fail silently — producing plausible but incorrect answers, exhibiting bias, leaking data, or degrading without obvious alerts.

A pre-deployment checklist ensures:

All compliance areas are addressed before production
Evidence exists for each decision
Risks are identified and documented
Accountability is clear
The system can be monitored and audited

AI Compliance Checklist with 10 categories and 50 items for pre-deployment

The 10-Category Checklist

This checklist covers 10 compliance domains with 5 items each (50 items total). Every item must pass before production deployment.

AI Compliance Checklist visualization showing 10 categories with checkbox items

1. Data Compliance (5 items)

✓	Item	Evidence Required
□	Data sources documented	List of all data sources with collection dates
□	Data quality assessed	Quality metrics and acceptance criteria
□	Bias tested	Bias assessment across protected groups
□	Data lineage tracked	Documentation of data flow from source to model
□	Consent and licensing verified	Legal basis for data use documented

2. Privacy Compliance (5 items)

✓	Item	Evidence Required
□	PII handling implemented	Detection, redaction, or exclusion of PII
□	Data minimization applied	Only necessary data sent to external APIs
□	Retention policy defined	Data retention periods documented and enforced
□	User consent obtained	Consent mechanisms implemented where required
□	Third-party provider assessed	Data policies and terms reviewed

3. Security Compliance (5 items)

✓	Item	Evidence Required
□	Prompt injection tested	Security testing results documented
□	Access controls implemented	Least-privilege access documented
□	Secrets properly managed	No hardcoded credentials, secret manager used
□	Dependencies scanned	Vulnerability scan results documented
□	Code execution sandboxed	Isolation controls documented

4. Model Compliance (5 items)

✓	Item	Evidence Required
□	Model version tracked	Version ID, training config documented
□	Performance tested	Benchmarks, accuracy, quality metrics
□	Limitations documented	Known failure modes and limitations
□	Fairness assessed	Bias testing across demographic groups
□	Robustness verified	Edge case and adversarial testing results

5. Vendor Compliance (5 items)

✓	Item	Evidence Required
□	Terms of service reviewed	Relevant terms documented
□	Data policies assessed	How vendor handles your data
□	SLAs defined	Uptime, support, response times
□	Exit strategy documented	Plan if vendor relationship ends
□	Fallback plan exists	Alternative if primary vendor fails

6-10. Remaining Categories

The remaining five categories follow the same structure:

Category	5 Items Each
6. Transparency	Users informed, AI disclosed, limitations stated, model card, evaluation public
7. Oversight	Review process, override capability, escalation path, decision logging, kill switch
8. Logging	Model version, prompt logged, output logged, tool calls, retention set
9. Monitoring	Drift detection, quality metrics, latency tracked, error rates, alerts configured
10. Documentation	Model card, dataset card, risk register, audit trail, version history

Complete Checklist Summary

Category	Items	Pass	Fail
1. Data	5	___	___
2. Privacy	5	___	___
3. Security	5	___	___
4. Model	5	___	___
5. Vendor	5	___	___
6. Transparency	5	___	___
7. Oversight	5	___	___
8. Logging	5	___	___
9. Monitoring	5	___	___
10. Documentation	5	___	___
TOTAL	50	___	___

Pre-Deployment Decision

Score	Decision
All 50 pass	Ready for production deployment
1-5 failures (non-critical)	Document risk acceptance, deploy with monitoring
6+ failures	Remediate before production
Any security/privacy failure	Block production until resolved

Conclusion

AI compliance is a pre-deployment discipline, not a post-deployment afterthought. This checklist provides a structured approach to verifying your AI system addresses 10 compliance domains before production.

Use this checklist as a starting point. Customize it for your specific regulatory requirements, industry standards and risk tolerance. Document evidence for each item and maintain records for audit purposes.

The goal is not a perfect score — it is informed decision-making about when and how to deploy AI systems responsibly.