AI is helping development teams produce far more code, far faster. But security teams still have to review vulnerabilities, manage dependencies, prioritize fixes, and control risk at human speed.
When software output jumps 10 to 50 times, the problem is no longer just finding vulnerabilities. It is keeping security from becoming the bottleneck, or worse, losing control of what gets shipped.
In our latest webinar with Chainguard experts, “The True Cost of Building at Machine Speed,” you can now watch how security teams can keep AI-driven development fast without letting risk scale with it.
For years, application security followed a familiar cycle: developers wrote code, scanners found problems, security teams prioritized them, and engineers fixed what mattered most.
AI puts that model under pressure.
If teams can suddenly create many times more code, security can also end up with many more components, dependencies, findings, and fixes to manage. More scanning alone does not solve that. It can simply create a larger backlog.
And this is not only a defensive problem.
The same powerful AI models helping developers write and understand software are also available to attackers. As both software production and attacker capabilities accelerate, security teams are being squeezed from both sides.
The core question becomes simple: How do you move at AI speed without accepting AI-speed risk?
Security Needs a New Operating Model
That is the focus of The True Cost of Building at Machine Speed.
The webinar looks beyond the usual discussion about whether AI-generated code is secure. It gets into the harder issue: what happens to security when the amount of software being created grows faster than people can realistically review and remediate it?
Join the webinar to see where traditional CVE-driven remediation starts to break down, what secure-by-default development should look like, and how to build controls that can keep working as AI adoption grows.
The session examines how AI is expanding the software attack surface, why existing vulnerability-management processes may struggle at machine scale, and where organizations need stronger guardrails before code reaches production.
It also tackles the governance side.
AI-assisted development is quickly becoming more than an engineering decision. Security leaders need to understand who owns the risk, how much exposure the organization is accepting, and how to explain those choices to executives and boards.
Slowing developers down is not the answer. Companies are adopting AI because they want to build faster.
The better approach is to make security work at that speed too, with controls designed around how software is being built now, not how it was built five years ago.
Watch now “The True Cost of Building at Machine Speed” and get a practical framework for securing AI-driven development before the gap between development speed and security control gets even wider.
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