AI helps growth groups produce way more code, far sooner. However safety groups nonetheless must evaluate vulnerabilities, handle dependencies, prioritize fixes, and management threat at human pace.
When software program output jumps 10 to 50 instances, the issue is now not simply discovering vulnerabilities. It’s preserving safety from changing into the bottleneck, or worse, dropping management of what will get shipped.
In our newest webinar with Chainguard specialists, “The True Price of Constructing at Machine Velocity,” now you can watch how safety groups can preserve AI-driven growth quick with out letting threat scale with it.
For years, software safety adopted a well-recognized cycle: builders wrote code, scanners discovered issues, safety groups prioritized them, and engineers fastened what mattered most.
AI places that mannequin underneath strain.
If groups can all of a sudden create many instances extra code, safety may find yourself with many extra parts, dependencies, findings, and fixes to handle. Extra scanning alone doesn’t remedy that. It will probably merely create a bigger backlog.
And this isn’t solely a defensive downside.
The identical highly effective AI fashions serving to builders write and perceive software program are additionally accessible to attackers. As each software program manufacturing and attacker capabilities speed up, safety groups are being squeezed from either side.
The core query turns into easy: How do you progress at AI pace with out accepting AI-speed threat?
Safety Wants a New Working Mannequin
That’s 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.
