The application security landscape is experiencing its most dramatic transformation since the shift to DevOps and the cloud. AI coding assistants are fundamentally changing how organizations build software—generating code at velocities that make traditional security approaches mathematically impossible to sustain.
This is a once-in-a-decade reshaping of the security stack. Some tools are getting absorbed. Others will become more critical than ever. The question every security leader should be asking: which is which?
AI Is Exacerbating the SAST Triage Crisis
Here’s the math every security leader knows but doesn’t want to talk about: One AppSec engineer manually triaging 15,000 SAST findings from 50 developers was already a losing battle. Now those same 50 developers using AI assistants produce 75,000+ findings. The model doesn’t just strain under AI velocity—it completely breaks.
Per StackHawk’s recent AI-Era AppSec Survey, the majority of AppSec teams spend at least 40% of their time triaging SAST alerts, and when you actually test at runtime, 98% of those findings turn out to be unexploitable. The math doesn’t check out.
SAST is Being Eaten by Your IDE
I’ll take the challenge of SAST a step further. It has always been valuable for one reason: catching vulnerabilities early, before they compound into expensive fixes. The earlier you find it, the cheaper it is to fix. That principle hasn’t changed.
What’s changing is where that capability lives and fundamentally how it works.
SAST is pattern matching, and pattern matching is exactly what AI does best. AI code assistants already understand security context across languages, identify vulnerabilities in real-time, and fix issues automatically during code generation. The capability isn’t disappearing. It’s relocating directly into AI-powered IDEs, embedded in the development workflow itself.
But it might look different from the SAST we’re used to. The paradigm may shift from detection-centric to secure-by-default. Either way, security teams will need to reevaluate what they expect from secure code tooling.
Logistically, Runtime Testing Can’t Be Absorbed
Static analysis can move into the IDE because it’s pattern matching. Runtime testing can’t because it requires something AI cannot replicate: a running application.
An AI model can tell you a code pattern might be vulnerable to SQL injection. What it cannot tell you is whether that vulnerability is actually exploitable in your environment, with your database configuration, through your actual API endpoints. That requires running the application. Sending real requests. Observing real responses. This isn’t a limitation that better models will solve. It’s a fundamental constraint.
AI analyzes code. DAST validates reality.
Three capabilities exist only at runtime: actual exploitability versus theoretical risk, business logic and access control validation that requires understanding product intent, and infrastructure context that doesn’t exist in source code.
The Risks That Matter Don’t Show Up in Static Scans
Business logic flaws—broken authorization, access control failures, BOLA/BFLA—are now the #1 API security risk. They don’t show up in code patterns. They show up when you test whether user A can actually access user B’s data. SAST analyzes syntax and data flow, but it can’t answer runtime questions like “Does this API respect role-based permissions?” or “Can attackers chain these calls to escalate privileges?”
AI development widens this gap. When developers generate complete functions with AI, they review for “does this do what I want?”—not “is this secure?” That creates risks SAST can’t see: misunderstood auth flows, copy-pasted authorization logic applied wrong, endpoints developers don’t realize they’ve exposed.
And as teams ship AI-powered features—LLM integrations, autonomous agents—they’re introducing entirely new risk categories. Prompt injection. Data leakage through model responses. Behaviors that only emerge at runtime. No static rule catches these. No matter what they tell you, you have to run the application to identify them.
The Way Forward: DAST First
Up until now, DAST has always played second fiddle to SAST and SCA. Not because runtime testing is less valuable—it’s more valuable. It finds what’s actually exploitable, not what might theoretically be a problem. But legacy DAST tools required weeks of manual configuration, and that baggage still shapes perception.
That barrier is gone. Modern DAST takes hours to implement, not weeks. And here’s the real cost equation: implementation is a one-time effort, but operationalization is what you pay every day. DAST might take more thought upfront, but then you’re triaging hundreds of findings—not tens of thousands. SAST is easier to turn on. DAST is easier to actually run.
Combining source code analysis for attack surface discovery with a shift-left approach means automatic discovery of what to test, configurations that adapt to each application, and remediation guidance that understands your specific code. Time-to-value flips. You can be fixing exploitable vulnerabilities faster than you can sort through your SAST backlog.
Static analysis is moving into the IDE. Runtime validation is where the gap is widening—and where this shift creates the biggest leap forward.
DAST isn’t dying in the AI era. It’s finally becoming what it should have been all along: the testing that actually matters.
