Vulnerability Assessment
Identifying, analyzing, and prioritizing security weaknesses in AI infrastructure and applications to guide remediation efforts.
Definition
A systematic program including automated vulnerability scans of codebases and dependencies, penetration tests of APIs and infrastructure, adversarial testing of model endpoints, and threat modeling workshops. Findings are rated by severity and likelihood, tracked in a remediation backlog, and verified by retesting. Governance defines assessment frequency, roles (security team, ML engineers), and SLA for patching critical vulnerabilities.
Real-World Example
A cloud-based recommendation engine undergoes quarterly vulnerability assessment: code scanners detect outdated library versions, pen-testers simulate API abuses, and model adversarial tests reveal an injection risk. All high-severity issues are remediated within 30 days, with retests confirming closure—ensuring the AI platform maintains a strong security posture.
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