Jason Edwards
Vulnerability management is presented as an operational control loop that converts discovery into verified remediation and measurable risk reduction. The chapter clarifies what vulnerability management is and is not, distinguishing it from patch management, vulnerability scanning, and incident response while explaining why activity metrics alone do not equal improved security. It emphasizes that credible outcomes depend on disciplined intake, consistent prioritization, and verification that “closed” reflects real state change rather than telemetry gaps or ticket hygiene. The chapter then establishes the foundational dependencies that make the program executable at scale: stable asset identity, explicit ownership, and a defensible scope across systems and environments. It explains how scope drift, incomplete discovery, and ambiguous ownership create backlog chaos and undermine executive trust, and it outlines how frequency and coverage expectations should be set by asset class and environment tier. Practical attention is given to assessment depth, authenticated assessment versus unauthenticated scanning, and the operational need to tune assessment cadence to remediation capacity so the queue remains actionable. Finally, the chapter focuses on decision quality and governance, showing how to prioritize work based on business risk using exposure, exploitability, service criticality, and control posture. It addresses how to manage backlogs without losing control through explicit status states, time-bound risk decisions, and verifiable compensating controls. AI-assisted prioritization is treated as decision support with strong requirements for input quality, bias and drift monitoring, human-in-the-loop approval boundaries, and auditability. Throughout, the chapter provides practical guidance for building sustained confidence in program outcomes by tying reporting to evidence-based verification and transparent, accountable decisions.