Beyond Copilots – Building an Agentic Workforce for Vulnerability Remediation

Overview

Copilots help you find more. They don’t help you fix faster.
The gap between vulnerabilities discovered and vulnerabilities remediated is widening and frontier AI is making it worse. Attackers now chain low-severity findings across silos in minutes. Scanners produce more high-fidelity signals than any human team can act on. Bolting a copilot onto each tool doesn’t close the gap. It just adds another interface to manage.

The Problem

• Vulnerability management was built for a world where humans triaged findings one tool at a time. That world is gone.
• Scanners are outpacing analysts
• Findings live in silos: AppSec, cloud, infra, AI — with no shared context
• Attackers exploit the seams between tools faster than teams can correlate them
• A copilot per tool means more assistants, not more remediation

You don’t need another copilot. You need a workforce.

What This Session Covers

An agentic, scanner-agnostic control plane that unifies every finding into a single context-aware graph — and deploys purpose-built AI agents that reason over that graph to go from find to fix.

1. A Single Source of Truth for Agents to Reason Over
How to unify findings, assets, ownership, threat intel, and business context into an operational graph — the foundation agentic remediation actually needs.

2. The Agentic Control Plane
How specialized agents get orchestrated across the full lifecycle — triage, risk analysis, remediation, zero-day exposure — spanning AppSec, infrastructure, cloud, and AI security.

3. Human-Guided, Not Human-Replaced
How to design agent scope, guardrails, and approval boundaries that security leaders can actually sign off on.

4. The Token-Efficiency Gap
Why dynamic, context-enriched agents beat brute-force LLM pipelines on token cost — and why that gap compounds fast at enterprise scale.

Who This Is For

Whether you own AppSec, vulnerability management, AI security, or cloud security, you’ll leave with a clear picture of what AI-scale, agentic remediation architecture looks like — including real examples of purpose-built agents automating the path from find to fix.