AI is changing software development in a way that many security teams did not have to plan for—at least not at this scale. When teams produce 10 to 50 times more code in the same time window, the challenge quickly stops being only about finding vulnerabilities. The bigger risk is that security becomes the bottleneck, or that organizations lose control over what actually reaches production.
A recent webinar, “The True Cost of Building at Machine Speed”, focuses on how security teams can keep up with AI-driven development without letting risk grow at the same pace. Instead of treating AI code generation as a narrow question—“Is the output secure?”—the session goes further and addresses what happens when the volume of software outpaces human review and remediation.
Why more code changes the security problem
For years, application security often followed a familiar cycle: developers write code, scanners detect issues, security teams prioritize them, and engineers fix what matters. At moderate software growth rates, that process can work—especially when the backlog is manageable and triage rules are well understood.
AI-driven development pressures that model. If software output jumps dramatically, security teams may suddenly face a much larger set of components, dependencies, scanner findings, and fixes. In practice, this can mean more scanning alone doesn’t solve the underlying problem. It may instead generate a bigger backlog that still can’t be handled in time.
That shift matters because the security objective isn’t only “reduce vulnerabilities.” It is also “keep risk controlled while the business keeps shipping.” When volume rises faster than remediation capacity, delays and partial fixes can become the norm—exactly the conditions that lead to weaker oversight.
Securing at machine scale is harder than remediation at human speed
Another uncomfortable reality highlighted in the webinar is that the software acceleration benefiting defenders also benefits attackers. The same kinds of powerful AI models that help developers write and understand code are also available to those trying to exploit weaknesses.
So security teams get squeezed from both sides. Attackers can move faster, and organizations can generate more software faster. The result is a security program stretched beyond its traditional operating tempo, particularly in how it handles vulnerability-management workflows.
The core question becomes straightforward: How do you move at AI speed without accepting AI-speed risk? The webinar frames this as more than a tooling problem—it’s a system design problem that affects the whole pipeline from development to governance.
Where traditional CVE remediation breaks down
The webinar discussion goes beyond general concerns about AI-generated code security. It examines how classic vulnerability-management approaches—especially those driven by CVE remediation schedules and human prioritization—can start to fail when code volume increases too quickly.
At machine scale, the issue isn’t that vulnerabilities suddenly become invisible. It’s that the organization can’t reliably triage, assess, and remediate everything before the next waves of code and dependencies appear. That creates a gap between what scanners report and what security teams can realistically fix.
Instead of treating remediation as a linear queue, the webinar points toward the need for stronger guardrails earlier in the process—controls that can prevent risky outcomes before they accumulate as backlog items.
Secure-by-default development: build controls into the process
A key takeaway from “The True Cost of Building at Machine Speed” is that security must work with how software is being created now—not how it was created a few years ago. Slowing developers down is presented as a tempting but ultimately limiting response, particularly because companies adopt AI to build faster.
Rather than stopping the pace, the better approach is to redesign security so it operates at the same speed as development. That means adopting secure-by-default development patterns and controls that can enforce safe outcomes throughout the lifecycle.
In other words, the goal isn’t only to react quickly to findings. The goal is to reduce the likelihood that risky code, unsafe dependencies, or fragile configurations enter the production path in the first place.
How AI expands the attack surface
When AI accelerates development, it can also expand the effective attack surface. More code typically brings more opportunities for mistakes, misconfigurations, or insecure dependencies. It can also increase the number of places where security teams must verify correctness and compliance.
The webinar highlights that existing vulnerability-management processes may not be designed for this kind of scale. Processes built around periodic review cycles can struggle when software changes rapidly and the number of potential issues grows faster than the review team’s available time.
That’s why the emphasis shifts toward controls that remain effective under higher throughput—controls that can scale even when software production scales.
Governance and ownership of risk
Another important element of the webinar is governance. AI-assisted development is not only an engineering decision; it changes who owns risk and how that risk is communicated across leadership levels.
Security leaders are asked to understand and explain questions such as: Who owns the risk? How much exposure is the organization accepting? And how can the organization communicate these choices to executives and boards?
At higher development speeds, it becomes easier for risk to become “everyone’s problem” in a way that no one can effectively measure or manage. Strong governance helps prevent that outcome by making responsibilities explicit and by aligning security controls with business expectations.
Practical framework: keep speed without losing control
The webinar positions its content as a practical direction for securing AI-driven development before the gap between development speed and security control gets worse. It emphasizes that organizations need stronger guardrails before code reaches production—especially when output volume increases by multiples.
If you’re considering AI adoption, this is a useful framing: treat security as part of the operating model, not just a set of retrospective checks. That means designing controls that can keep pace with how code is produced and how dependencies and findings are generated.
Instead of waiting for the backlog to overwhelm the team, the aim is to reduce risky outcomes upstream, enforce safe defaults, and establish governance that supports accountable decision-making.
What to watch for in “The True Cost of Building at Machine Speed”
If you plan to watch the session, focus on how it connects three themes: throughput, risk control, and governance. The webinar addresses why scanning alone may not keep up, where traditional remediation workflows start to break under machine-scale output, and what secure-by-default development could look like in practice.
It also tackles the broader reality that AI speeds up both defenders and attackers. That makes the security challenge time-sensitive and strategic, not just technical. In this context, AI-speed security controls are about keeping the organization’s ability to steer software delivery intact—even as velocity increases.
Conclusion
AI can accelerate software delivery dramatically, but security can’t simply rely on the same review and remediation patterns that worked at lower volumes. When code output grows 10 to 50 times, the problem shifts from detecting vulnerabilities to maintaining control of what gets shipped and ensuring security can operate at machine scale.
“The True Cost of Building at Machine Speed” offers a roadmap for teams that need to move at AI speed without accepting AI-speed risk. By focusing on secure-by-default development, stronger guardrails, and accountable governance, organizations can build AI-speed security controls that scale alongside modern development.
Source: https://thehackernews.com/2026/08/shipping-1050-more-code-watch-this.html
