AI Security HubAssessUpdated 2026-08-08
AI Security Assessment Checklist
A useful checklist should cover governance, inventory, data, privacy, identity, model security, third-party AI, logging, monitoring, incident response, resilience, and evidence.
A checklist should be specific enough to produce evidence, not just yes/no answers.
The strongest checklist covers both technical and governance controls.
Every checklist result should lead to a concrete next step.
Checklist areas
Use the checklist below as a practical starting point for an AI security assessment. Each area should be backed by evidence, not assumptions.
| Area | What to verify | Example evidence |
|---|---|---|
| Governance | Named owner, policy, approval path | Policy docs, approvals, exception log |
| AI inventory | Known AI systems and vendors | Inventory export, intake register |
| Data security | Sensitive data handling and retention | Data maps, DLP, retention rules |
| Privacy | User notice and data processing review | Privacy review, DPIA/PIA records |
| Identity & access | Access control for users and admins | SSO config, role matrix, MFA |
| Model/application security | Prompt, output, tool, and runtime controls | Config, test results, logs |
| Secure development | Code review, release gates, testing | SDLC docs, test evidence |
| Third-party AI | Vendor risk and contract terms | DPA, security review, SLA |
| Logging & monitoring | Alerts and review process | Log samples, dashboards, alert rules |
| Incident response | Playbooks and escalation | IR runbooks, tabletop notes |
| Resilience | Fallbacks, rate limits, recovery | Failover design, test logs |
| Evidence | Repeatable proof for each control | Screenshots, exports, audit trail |
What to do with the results
Use the checklist to identify the highest-risk gaps, then convert those gaps into a scored assessment so teams can compare effort, impact, and progress.
Next step
Guides explain what good AI security looks like. SecureAIScore helps you measure where your organization stands.