The **security debt gap** is the growing distance between the vulnerabilities you *discover* and the ones you actually *fix*. Today, that gap is widening fast:

• In 2024, roughly 40,000 CVEs were published.
• An estimated 50–60% of known vulnerabilities are never resolved.
• About 45.9% of organizations have critical security debt that’s been open for over a year.
• Telemetry shows up to 90% of vulnerabilities never get fully resolved.
• 81% of developers say they’ve shipped vulnerable code under delivery pressure.

AI accelerates this problem. According to the 2025 GitHub Octoverse report:

• Developers merged an average of 43.2 million pull requests per month (a 23% year-over-year increase).
• They pushed nearly 1 billion commits.

Traditional AppSec models assumed a relatively stable ratio between code written and security capacity. When AI boosts code output by an order of magnitude, that ratio collapses. You end up with:

• More code and more vulnerabilities entering the system.
• Fragmented tools and processes that generate noise instead of clear priorities.
• Developers acting as “first responders” for issues they didn’t create and don’t have full context for.

The result: security debt stops behaving like a simple backlog and starts functioning as a structural deficit. To close this gap without slowing delivery, organizations need to:

• Embed security directly into development workflows (not bolt it on later).
• Use AI not just to generate code, but to help prioritize, fix, and prevent vulnerabilities.
• Adopt Secure by Design principles so fewer exploitable flaws reach production in the first place.

AI can either multiply risk or help you **reimagine** how you manage it. The difference comes down to where and how you apply it. To use AI to improve security instead of just generating more alerts, you need to do two things:

1. Shift security further left to where code is generated
   Build security checks into the moment code is created, not just into late-stage testing. That means:
   • Scanning for vulnerabilities, secrets, and risky dependencies as code is written or proposed.
   • Using guardrails in pull requests so insecure changes can’t be merged by accident.
   • Applying consistent policies across repositories so enforcement isn’t a judgment call under time pressure.
   This helps prevent net-new risk from entering your codebase and flattens your backlog over time.
2. Use AI agents to burn down existing security debt
   Treat AI as an intelligent assistant that helps you move from detection to remediation:
   • GitHub Copilot can analyze vulnerabilities in context and propose targeted fixes.
   • Copilot Autofix generates concrete code changes for CodeQL and Dependabot alerts, and explains why they work.
   • Security campaigns group up to 1,000 related alerts and apply AI-generated fixes at scale.
   This approach has a measurable impact: inside campaigns, about 55% of alerts get fixed, compared to roughly 10% of individual alerts—a 5.5x improvement in fix-through rate.

When these capabilities are embedded in a unified development platform like GitHub Advanced Security (GHAS), you get:

• GitHub Secret Protection for secret scanning and push protection, blocking exposed credentials before they land in the repo.
• GitHub Code Security for code scanning (CodeQL), dependency review, and premium Dependabot features.
• A single security overview so you can see where risk lives, track trends, and focus on the issues that matter most.

In practice, this lets you:

• Prevent risk at the point of introduction (e.g., blocked secrets, risky dependencies caught before merge).
• Reduce alert fatigue by turning high-volume findings into actionable fixes.
• Shorten time-to-resolution and shrink your overall risk backlog instead of watching it grow.

When you evaluate an AI-enabled security platform, you’re really making a **platform decision** about how your teams will build and secure software going forward. Three capabilities matter most:

1. Native integration with developer workflows
   Security needs to show up where developers already work—pull requests, code reviews, CI pipelines—without extra tools or context switching. This turns security from a separate function into a shared responsibility.
2. Built-in governance and policy enforcement
   You should be able to define security policies once and apply them everywhere. Look for:
   • Organization-wide standards that automatically apply across repos.
   • Blocking rules for known vulnerabilities and leaked secrets.
   • Auditability and reporting that don’t require manual stitching across tools.
3. AI that reduces effort, not just adds alerts
   The goal is to shorten the distance between detection and fix. AI should help you:
   • Understand and prioritize risk.
   • Generate and validate fixes in context.
   • Scale remediation across both new and historical findings.

How GitHub Advanced Security (GHAS) supports this GHAS brings these elements together in one platform:

• GitHub Secret Protection
  • Secret scanning detects credentials (keys, tokens, etc.) already in your repos and alerts you.
  • Push protection blocks secrets before they’re committed, turning credential hygiene into a preventative control.
• GitHub Code Security
  • Code scanning (CodeQL) finds vulnerabilities and coding errors in your code.
  • Dependency review shows the impact of dependency changes and flags vulnerable versions before merge.
  • Premium Dependabot surfaces and helps fix dependency risks.
• Security overview & campaigns
  • Centralized visibility into code scanning alerts, secret leaks, and dependency risks.
  • Security campaigns that group related alerts (up to 1,000 at a time) and apply AI-generated fixes at scale.
• Copilot Autofix
  • Automatically generates fixes for supported code scanning alerts.
  • Helps teams move from detection to remediation faster and reduce mean time to resolution (MTTR).

How to measure impact A strong platform also makes risk reduction measurable. Useful signals include:

• Early-stage resolution rate: How often issues are fixed before production.
• Secret scanning push protection metrics: How many secrets are blocked before entering the repo.
• Risk backlog trend: Whether unresolved issues are shrinking over time.
• Fix-through rate: The percentage of identified issues that are actually resolved.
• Time-to-resolution: How long vulnerabilities live in your codebase.
• Repeat issue frequency and risk concentration: Whether the same patterns keep reappearing or cluster in specific teams/services.

With these capabilities in place, you can embed Secure by Design principles into your SDLC, so teams ship quickly and responsibly—without carrying growing security debt forward.