Episode 58 — Build AI vulnerability management from discovery to remediation (Task 7)
This episode focuses on Task 7 by explaining how to build AI vulnerability management from discovery to remediation, because AAISM treats vulnerability management as an end-to-end control process that includes identification, prioritization, ownership, fixes, and evidence—not just scanning and ticket creation. You’ll learn how “vulnerabilities” show up in AI environments, including misconfigured access to model endpoints, overly permissive connectors, unsafe prompt handling, weak logging, unreviewed model changes, and dependency vulnerabilities in pipelines and hosting platforms. We’ll use a scenario where security discovers an AI integration that exposes sensitive data through an overly broad retrieval connector, and you’ll practice triaging severity based on impact and likelihood, assigning accountable owners, coordinating changes through governance, and validating that the fix aligns with policy and compliance obligations. Troubleshooting covers common breakdowns like unclear ownership across data, model, and platform teams, and remediation that fixes one path while leaving alternate exposure paths untouched. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
