- AI can increase development output by 10 to 50 times, making security review the bottleneck.
- Traditional CVE-driven remediation may break down when software is built at machine speed.
- Attackers can use the same AI models, squeezing security teams from both sides.
- Secure-by-default development and stronger guardrails are needed before code reaches production.
At a webinar hosted by The Hacker News on Aug 10, 2026, Chainguard experts presented “The True Cost of Building at Machine Speed” to address how security teams can keep pace with AI-driven development.
AI is helping development teams produce far more code, far faster, but security teams still review vulnerabilities, manage dependencies, and prioritize fixes at human speed.
Consequently, 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.
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.
More scanning alone does not solve this problem, because it can simply create a larger backlog.
Meanwhile, the same powerful AI models that help developers write and understand software are also available to attackers, meaning security teams are being squeezed from both sides.
The core question is how to move at AI speed without accepting AI-speed risk.
The webinar looks beyond whether AI-generated code is secure, focusing instead on what happens to security when the amount of software being created grows faster than people can realistically review and remediate it.
It explores where traditional CVE-driven remediation starts to break down, what secure-by-default development should look like, and how to build controls that keep working as AI adoption grows.
The session also examines how AI is expanding the software attack surface, why existing vulnerability-management processes may struggle at machine scale, and where stronger guardrails are needed before code reaches production.
On the governance side, 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, because companies are adopting AI to build faster. The better approach is to make security work at that speed, with controls designed around how software is being built now.
The webinar offers a practical framework for securing AI-driven development before the gap between development speed and security control gets even wider.
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