AI Security and Responsible AI Checklist

Evidence for a security review

Test the actual roles and data paths: what users can retrieve, what leaves the environment, what the system can execute, and what gets recorded. Include attempts to obtain restricted information and instructions embedded in untrusted source material.

Assign an owner to each failure and rehearse a pause or manual fallback. The agentic AI guide explains permission boundaries. The data-governance checklist examines the sources, and the launch checklist connects the evidence to the go-live decision.

Purpose

This checklist helps organizations evaluate security and responsible AI controls for enterprise AI deployments. It supports go-live decisions, periodic assurance, and executive risk discussions.

Who Should Complete It

• Chief information security officer or security lead
• AI engineering or platform lead
• Privacy, legal, and compliance partners
• Risk management
• Business owners of high-impact AI systems

Instructions

Answer each item Yes, No, In Progress, or Not Applicable based on evidence. Apply the checklist to a specific system, a platform, or the enterprise control environment. State the scope at the top of your working copy.

Scoring Guidance

• Yes = 2
• In Progress = 1
• No = 0
• Not Applicable = exclude

Interpret overall and section scores using the guidance below. Security and oversight weaknesses should be weighted heavily for high-impact systems.

Assessment Questions

Cybersecurity Foundations

1. AI systems inherit enterprise security baselines. (Yes / No / In Progress / N/A)
2. Vulnerability management covers AI applications and dependencies. (Yes / No / In Progress / N/A)
3. Network and environment segmentation are appropriate to risk. (Yes / No / In Progress / N/A)
4. Secrets are stored in approved vaults, not prompts or code. (Yes / No / In Progress / N/A)
5. Security testing occurs before production release. (Yes / No / In Progress / N/A)
6. Hardening standards exist for AI serving infrastructure. (Yes / No / In Progress / N/A)

Identity, Authentication, and Authorization

7. Users authenticate through enterprise identity providers. (Yes / No / In Progress / N/A)
8. AI service identities are unique and managed. (Yes / No / In Progress / N/A)
9. Authorization is least privilege by role and data domain. (Yes / No / In Progress / N/A)
10. Privileged actions require elevated controls. (Yes / No / In Progress / N/A)
11. Access reviews include AI systems and connectors. (Yes / No / In Progress / N/A)
12. Token and key rotation practices are enforced. (Yes / No / In Progress / N/A)

Model and Prompt Security

13. Prompt injection risks are assessed for relevant systems. (Yes / No / In Progress / N/A)
14. Tool and plugin permissions are allowlisted. (Yes / No / In Progress / N/A)
15. Unsafe outputs are filtered or escalated where feasible. (Yes / No / In Progress / N/A)
16. Model and prompt versions are controlled. (Yes / No / In Progress / N/A)
17. Evaluation includes adversarial and misuse scenarios. (Yes / No / In Progress / N/A)
18. Untrusted content is handled cautiously in retrieval workflows. (Yes / No / In Progress / N/A)

Third-Party Risks

19. Vendor security assessments are completed before use. (Yes / No / In Progress / N/A)
20. Data-use and retention terms are reviewed. (Yes / No / In Progress / N/A)
21. Subprocessors are known and accepted. (Yes / No / In Progress / N/A)
22. Vendor incident notification obligations are contractual. (Yes / No / In Progress / N/A)
23. Fourth-party dependencies are considered for critical systems. (Yes / No / In Progress / N/A)
24. Exit and deletion commitments are verified. (Yes / No / In Progress / N/A)

Monitoring and Incident Response

25. Production AI systems emit security-relevant logs. (Yes / No / In Progress / N/A)
26. Anomalous usage can be detected and investigated. (Yes / No / In Progress / N/A)
27. Incident response playbooks include AI scenarios. (Yes / No / In Progress / N/A)
28. Kill switches or disable mechanisms exist. (Yes / No / In Progress / N/A)
29. Roles for incident command are predefined. (Yes / No / In Progress / N/A)
30. Post-incident lessons are tracked to closure. (Yes / No / In Progress / N/A)

Bias Mitigation and Fairness Controls

31. People-affecting use cases are identified. (Yes / No / In Progress / N/A)
32. Fairness risks are assessed before deployment. (Yes / No / In Progress / N/A)
33. Relevant testing across segments is performed where appropriate. (Yes / No / In Progress / N/A)
34. Monitoring includes outcome disparities for material systems. (Yes / No / In Progress / N/A)
35. Channels exist for challenge and redress. (Yes / No / In Progress / N/A)
36. Changes that affect fairness are re-evaluated. (Yes / No / In Progress / N/A)

Human Oversight

37. Oversight model matches system impact. (Yes / No / In Progress / N/A)
38. Reviewers are trained for escalation judgment. (Yes / No / In Progress / N/A)
39. Override and rejection paths are usable under time pressure. (Yes / No / In Progress / N/A)
40. Automation does not obscure accountability. (Yes / No / In Progress / N/A)
41. High-impact actions require confirmation where required. (Yes / No / In Progress / N/A)
42. Oversight effectiveness is sampled and reviewed. (Yes / No / In Progress / N/A)

Audit Logging and Evidence

43. Key decisions and outputs can be reconstructed. (Yes / No / In Progress / N/A)
44. Logs are protected against tampering. (Yes / No / In Progress / N/A)
45. Retention supports investigation and compliance needs. (Yes / No / In Progress / N/A)
46. Evidence packs can be produced for auditors. (Yes / No / In Progress / N/A)
47. Configuration changes are recorded. (Yes / No / In Progress / N/A)
48. Data sources used in material outputs are traceable. (Yes / No / In Progress / N/A)

Compliance and Responsible Use

49. Policy requirements are mapped to technical controls. (Yes / No / In Progress / N/A)
50. Restricted and prohibited uses are enforced. (Yes / No / In Progress / N/A)
51. Privacy reviews are completed for relevant systems. (Yes / No / In Progress / N/A)
52. Regulatory obligations are reflected in control design. (Yes / No / In Progress / N/A)
53. Ethical review occurs for sensitive use cases. (Yes / No / In Progress / N/A)
54. Transparency obligations to users are met where required. (Yes / No / In Progress / N/A)

Secure Development and Release

55. Secure design reviews include AI-specific threats. (Yes / No / In Progress / N/A)
56. Dependencies and model components are inventoried. (Yes / No / In Progress / N/A)
57. Release gates include security and responsibility checks. (Yes / No / In Progress / N/A)
58. Rollback plans are tested or rehearsed. (Yes / No / In Progress / N/A)
59. Production changes require approval. (Yes / No / In Progress / N/A)
60. Continuous monitoring follows release. (Yes / No / In Progress / N/A)

Enterprise Control Environment

61. Security and AI Governance forums coordinate. (Yes / No / In Progress / N/A)
62. Training covers secure and responsible AI use. (Yes / No / In Progress / N/A)
63. Shadow AI high-risk use is actively reduced. (Yes / No / In Progress / N/A)
64. Metrics for AI security incidents are reported. (Yes / No / In Progress / N/A)
65. Critical findings block launch or trigger compensating controls. (Yes / No / In Progress / N/A)
66. Controls are reassessed after major model or feature changes. (Yes / No / In Progress / N/A)
67. Red-team or abuse testing occurs for higher-risk systems. (Yes / No / In Progress / N/A)
68. Business owners accept residual security risk explicitly. (Yes / No / In Progress / N/A)

Interpretation of Results

High readiness (about 80%+): Security and responsibility controls are broadly operable. Maintain vigilance and reassess on change.

Moderate readiness (50% to 79%): Limit high-impact deployments. Remediate identity, logging, oversight, and vendor gaps before scale.

Low readiness (below 50%): Do not expand consequential AI use. Prioritize foundational security, inventory, and incident readiness.

For safety-critical or regulated systems, treat major "No" answers in oversight, identity, or logging as launch blockers regardless of overall percentage.

Recommended Next Actions

1. Scope the checklist to the highest-risk systems first.
2. Convert critical gaps into remediation work with owners.
3. Update launch gates and vendor standards.
4. Brief executives on residual risk before expansion.
5. Retest after significant model, tool, or permission changes.

Frequently Asked Questions

Is this checklist a replacement for penetration testing?

No. It assesses control readiness. Technical testing remains necessary.

Should every generative AI use case meet the same bar?

No. Use risk tiering. Higher-impact systems need deeper controls.

How does this relate to NIST or ISO guidance?

It is compatible with risk-based practices inspired by frameworks such as NIST AI RMF and ISO/IEC 42001, adapted for operational checklist use.

Summary

This checklist provides a practical security and responsible AI assessment covering cybersecurity, identity, model and prompt risks, vendors, monitoring, bias, oversight, auditability, and compliance. It helps enterprises decide what is safe to launch and what must be fixed first.