The engineering leader's guide to AI-powered vulnerability resolution
Faster shipping meets rising risk: a new reality for Vulnerability Management
Development velocity is up thanks to AI coding assistants. So is the volume of security vulnerabilities. According to the Forum of Incident Response and Security Teams’ annual Vulnerability report, the total number of common vulnerabilities and exposures (CVEs) disclosed in 2026 is expected to exceed 50,000 and may reach six figures for the first time.
Stay ahead of the growing security debt backlog
This is a practical playbook for CTOs, VPs of Engineering, and engineering leaders who need to:
- Resolve more vulnerabilities faster without pulling engineers off feature work.
- Shorten issue cycle time from detection to merged fix, not just detection to ticket.
- Free up engineers from repetitive remediation so they can focus on architecture, design, and high-impact security decisions.
- Scale vulnerability management across teams, repos, and scanners — even as AI-generated code expands your attack surface.
You’ll learn how to build a full-cycle remediation system that combines AI-powered fixes with human approval, so security posture, engineering velocity, and governance scale together.
In this whitepaper, you’ll find:
- A step-by-step playbook to stand up a scalable vulnerability management system
- How to divide the work between AI and humans across detection, triage, and remediation
- How to use Jira as both the system of record and the automation layer for vulnerability remediation at scale
- A practical ROI framework to quantify time saved, risk reduced, and engineering capacity reclaimed
- How Atlassian resolves 52% of vulnerabilities automatically and cut issue cycle time in half with Jira Coding Agent
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