NightVision: AI Coding Assistants Are Creating Invisible APIs That Security Teams Can’t See

AI coding assistants are quietly redrawing the boundaries of application security, and many security teams are not ready for what that means.

As generative AI tools accelerate software development, they are also accelerating the creation of new application programming interfaces. These APIs increasingly appear without formal design reviews, documentation, or inventory processes. The result is an expanding class of shadow APIs that exist in production but remain invisible to the security controls designed to protect them.

According to Ryan Kinkead, Director of Solutions Engineering at NightVision, the issue is not simply speed but visibility. “AI coding assistants are fundamentally changing the math of application security. They’re not just writing code faster; they’re spinning up new API endpoints faster than teams can document, creating shadow APIs at unprecedented scale.”

For years, security teams have leaned on traffic monitoring, manual documentation, and post deployment discovery to understand what APIs exist in their environments. That model assumes humans are the primary authors of software and that changes occur at a pace security tools can observe. AI driven development breaks both assumptions.

“Traditional API discovery methods that rely on traffic analysis or manual documentation will miss these endpoints entirely because they were never cataloged in the first place,” Kinkead says. “The only reliable way to maintain a complete API inventory in an AI accelerated development environment is to discover APIs directly from the source code itself, finding every endpoint at the moment it’s created, not months later when it shows up in a breach report.”

This shift exposes a deeper mismatch between modern development practices and legacy application security tooling. Dynamic application security testing tools were built for slower release cycles and often require hours to complete a scan. In environments where teams deploy multiple times a day, those tools quickly become impractical. Static analysis, while faster, often fails to capture runtime behavior and business logic flaws that only emerge when an application is live and handling real data.

“The velocity problem cuts both ways,” Kinkead explains. “When teams ship multiple times a day, legacy DAST tools that take hours to run become irrelevant, while static analysis misses the runtime and business logic flaws that only surface in a live application.”

The implications for 2026 are stark. As AI systems continue to generate code at scale, organizations that rely on outdated security approaches may lose track of what they are exposing to the internet. Attackers do not need to break sophisticated defenses if they can simply find an undocumented endpoint with weak controls.

“In 2026, organizations relying on yesterday’s security tooling will find themselves with an attack surface they can’t see and a release velocity they can’t secure,” Kinkead warns.

For security leaders, the message is clear. Application security can no longer be bolted on after deployment or treated as a periodic audit function. In an AI driven development world, visibility must begin at the moment code is written, or risk will accumulate faster than teams can respond.