Shipping More AI Code Than You Can Secure? Watch How to Control Remediation Debt
AI coding tools can introduce open-source packages at a pace security teams were never built to handle, leading to remediation debt and security work accumulating faster than teams can close it.
Intelligence analysis by Llama
AI coding tools can introduce open-source packages at a pace security teams were never built to handle, leading to remediation debt and security work accumulating faster than teams can close it. This webinar examines what this means for security and engineering teams, drawing on data from 300 enterprise leaders.
Imagine you have a super-fast robot that can write code for you. Sounds great, right? But what if that robot writes code that has bugs or security issues? You'd have to fix those issues, but it would take a lot of time and effort. That's kind of like what's happening with AI coding tools. They can write code fast, but they can also introduce security issues that are hard to fix.
Analysis
The Real Problem Is What AI Adds to Your Stack
AI coding itself is not the issue. The problem is how quickly generated code can bring new open-source components into your environment. A developer can add a dependency in minutes. Your team may then need to assess vulnerabilities, licensing, maintenance, ownership, and whether that package should be there at all. That work does not disappear just because the code was generated faster. Over time, you end up with remediation debt: security work accumulating faster than your team can close it. And as AI tools become more autonomous, that gap could widen significantly.
See How Your Program Compares
ActiveState surveyed 300 security and engineering leaders across technology, financial services, healthcare, manufacturing, and government. The research examines how teams are handling AI-driven open-source risk, where remediation programs are struggling, and how that debt relates to audit failures, breach frequency, and lost productivity. This webinar walks through those findings so you can compare your own program with what other enterprises are seeing. That benchmark matters. It gives you a clearer sense of whether your current controls are keeping up or simply pushing more unresolved work downstream.
What You’ll Take Away
ActiveState's Rebecca Banks and Moris Chen break down: How AI coding is changing open-source remediation workloads How your program compares with 300 enterprise peers Where remediation debt starts affecting security and business outcomes Which governance models are working today Which approaches may create more problems than they solve
Key points
- AI coding tools can introduce open-source packages at a pace security teams were never built to handle
- Remediation debt can lead to security work accumulating faster than teams can close it
- ActiveState surveyed 300 security and engineering leaders across various industries
- The research examines how teams are handling AI-driven open-source risk and where remediation programs are struggling
If developers and security teams work together to understand and address the risks associated with AI-generated code, they can create more secure and efficient development processes. This could lead to faster and more reliable software releases, improved security posture, and reduced remediation debt.
If left unchecked, the rapid pace of AI-generated code could lead to a significant increase in remediation debt, security work accumulating faster than teams can close it, and a higher risk of audit failures, breach frequency, and lost productivity.
