AI security assessment
This interactive AI security self-assessment turns current AI governance, identity, data protection, AI system security, and response guidance into a practical checklist for regulated teams. Check what is already in place, review the gaps, then discuss your score with Datapath.
30
controls
5
domains
10m
review
AI security assessment checklist
An AI security assessment should cover approved AI tools, sensitive-data handling, identity and access, connected apps, vendor terms, AI application risks, monitoring, incident response, and proof that controls are actually operating. This checklist gives leadership and IT teams a fast first pass before a deeper governance or cybersecurity review.
Approved tool inventory, policy, executive ownership, data rules, vendor review, and staff training.
MFA, least privilege, OAuth review, DLP, logs, device posture, encryption, and protected integrations.
Prompt-injection testing, safe automation, backup readiness, AI-enabled fraud planning, and response ownership.
Domain 1
0 / 6 complete
Domain 2
0 / 6 complete
Domain 3
0 / 6 complete
Domain 4
0 / 6 complete
Domain 5
0 / 6 complete
From score to remediation
A score is useful only if someone owns the next move. Datapath helps regulated teams move from AI security assessment findings to policy, access, vendor, data protection, monitoring, and response work that can be reviewed by leadership.
Review AI governance consultingTurn the assessment score into AI policy, tool inventory, approval workflow, vendor review, and executive reporting.
Validate AI-related identity, endpoint, data, vendor, and incident-response gaps alongside broader cybersecurity risk.
Move recurring AI-era monitoring, alert triage, vulnerability remediation, and reporting into an owned service model.
Review MFA, Conditional Access, OAuth grants, admin roles, Copilot data exposure, and tenant-level access controls.
The assessment is based on practical themes from NIST AI risk management guidance, OWASP LLM application risks, and joint government guidance for securely deploying AI systems. It is not a compliance certification; it is a quick readiness screen for leadership and IT teams.
AI security assessment FAQ
An AI security assessment reviews how an organization governs AI tools, protects sensitive data, controls identity and access, secures AI systems, and prepares for AI-enabled incidents. It should cover approved tools, vendor terms, prompt and response handling, connected apps, privileged access, monitoring, and response ownership.
Start with an inventory of AI tools and integrations, classify what data they can access, review user and administrator permissions, evaluate vendor terms, test AI application risks, and update incident response. Datapath uses the checklist results to prioritize policy, access, data protection, monitoring, and remediation work.
The assessment is built for regulated and mid-market teams using AI in Microsoft 365, SaaS tools, service desks, workflows, customer operations, clinical systems, finance, education, or government environments. It is useful when AI adoption is moving faster than policy, vendor review, identity controls, and evidence collection.
No. This self-assessment is a practical readiness screen, not a certification, legal opinion, or formal audit. It helps leadership and IT teams identify gaps that may need deeper review through AI governance consulting, cybersecurity risk assessment, vendor review, policy work, or managed security operations.