AI Security Assessment
Assesses how AI systems can be attacked, misused, manipulated, or used to expose sensitive data.
Scope
Reviews prompt injection, data leakage, model abuse, access control, insecure integrations, model poisoning, output manipulation, logging, and monitoring.
The gaps it finds
AI manipulation paths, sensitive data leakage, weak AI access control, insecure plugins and tools, unsafe integrations, output abuse, and monitoring gaps.
Value to the board
Protects AI-enabled services from security threats and helps prevent sensitive data exposure or unsafe outputs.
Framework alignment
- NIST AI RMF
- OWASP Top 10 for LLM Applications
Reporting
Like every assessment in the portfolio, this engagement ends with the full deliverable set, from executive summary and maturity scorecard to remediation roadmap and board dashboard. The methodology page describes each deliverable.
Scope this assessment
A scoping conversation confirms objectives, stakeholders, systems, and the document request list before any work begins.