Decoding the Flavors of DAST: From Legacy Scanners to AI Pen Testing

AppSec is no stranger to acronyms, buzzwords, and fancy marketing terminology that catches on faster than actual technology can prove out. This has been especially true in the dynamic application security testing realm, where interest is still higher than ever.

DAST started gaining traction in the mid-2000s as web applications exploded in complexity. Fast forward to today, and those original tools have been dubbed “legacy”—replaced by “modern” and “shift-left” players (guilty) that evolved to solve the challenges of agile software development. Now, a new wave of vendors has arrived with “business logic testing” and “AI pen testing,” making bold promises about AI-powered risk detection at scale.

The application security testing landscape has genuinely evolved, but here’s what hasn’t: applications are getting more complex and vulnerable, and teams still have limited resources to find and fix risks before attackers exploit them.

So let’s cut through the hype. Here’s what these different “flavors” of DAST actually are, what they’re good at, what they struggle with, and the gaps between vendor promises and reality.

DAST 1.0: Legacy DAST

Traditional DAST tools came from a simpler time, when applications lived on a single server, changed quarterly, and the security team was a completely separate entity from engineering.

How it works: Legacy DAST scans your production (or pre-production) application by crawling it like a spider, finding all the URLs it can access, then throwing known attack payloads at each parameter. Think: automated SQL injection attempts, XSS tests, and other OWASP Top 10 classics. Then, findings are handed over to security teams who then have to translate and create tickets for anything to get fixed.

Who it’s for: Security and compliance teams who need documentation that scanning happened.

What it’s good at: Finding common, well-documented vulnerabilities such as the OWASP Top 10 in relatively static applications. Checking compliance boxes. Generating PDFs for auditors.

What it struggles with: Modern applications. Anything behind authentication. SPAs and complex JavaScript frameworks. APIs that require specific workflows. Beyond just the what, because of X, scans can take hours or days making it impossible to integrate with developer workflows. Forget getting developers to actually fix anything before the next release cycle.

Legacy DAST isn’t inherently bad, but it’s built for a world where security happened after development, not during it. If your release cycles are less agile, you have a dedicated security team to triage thousands of findings, or you just want to tick a compliance checkbox, it might work just fine. If you’re deploying multiple times per day, it’s like {metaphor).

DAST 2.0: Shift-Left/Modern DAST

Modern DAST (or “Shift-Left DAST“) emerged to solve legacy DAST’s biggest problem: by the time security found issues, they were difficult and expensive to fix and blocked releases.

How it works: Modern DAST integrates into CI/CD pipelines and tests applications in pre-production environments. It executes faster, incremental scans and gives a developer-friendly output that shows up in the tools developers already use. The goal is finding vulnerabilities when there’s still time to actually fix them.

Who it’s for: AppSec teams that know they need to work with developers to actually get vulnerabilities fixed, and development teams who need security testing that doesn’t slow them down. Mutual ownership, mutual success.

What it’s good at: Pre-production testing in developer workflows. Testing authenticated workflows. Finding vulnerabilities in APIs and modern application architectures. Providing actionable results that developers can actually use to fix vulnerabilities.

What it struggles with: The same things legacy DAST struggles with: business logic, complex multi-step workflows, and understanding whether a behavior is a bug or a feature.

The “Modern DAST” Trap: Legacy in Disguise

Not all “modern” DAST is created equal. The term “modern DAST” has become popular enough that many legacy vendors have slapped it on their existing products. They’ll tell you they’re shift-left. They’ll show you CI/CD integrations. They’ll talk about API support. But under the hood, they’re running the same architecture that made legacy DAST problematic in the first place.

What to watch for:

• Scan duration: If scans take 30+ minutes (or hours), that tool isn’t built for CI/CD. True shift-left DAST is designed to complete scans in minutes, so it fits within your build pipeline without creating bottlenecks.
• Infrastructure requirements: Does it require you to spin up dedicated scanning infrastructure or route traffic through scanning proxies? Legacy architecture. Modern DAST should work with your existing ephemeral environments and testing infrastructure.
• How it handles authentication: True modern DAST supports complex authentication workflows, where legacy DAST can only support basic authentication.
• Where results show up: Are findings only available in the vendor’s separate portal that requires security teams to translate and manually create tickets? That’s a legacy workflow with a CI/CD trigger. Modern DAST surfaces findings directly in pull requests, Jira, Slack—wherever developers actually work.

Real modern DAST is architecturally different. It’s designed from the ground up for:

• Pre-production environments and ephemeral infrastructure
• Developer workflows and tool integration
• Speed that doesn’t block deployments
• Testing authenticated applications without extensive configuration
• Providing context developers need to fix issues, not just lists of CVEs

The litmus test: Can a developer add meaningful security testing to their application in under 30 minutes without talking to the security team? If the answer is no, it’s probably legacy DAST with better marketing.

Business Logic Testing

Here’s where the marketing really gets creative—and further from reality. Business logic vulnerabilities aren’t about code-level exploits. They’re about understanding workflows, intent, business context, and what’s supposed to happen versus what actually happens. Can users access features they shouldn’t? Can they manipulate workflows to get unauthorized outcomes? Can they submit $-100 orders and get paid to take your products?

How it works: Business logic testing requires understanding what the application is supposed to do, then testing whether it actually enforces those rules. In theory, AI can help emulate human awareness and creativity, but in reality, it’s hard.

What it’s good at: Testing authorization, workflow logic, and multi-step processes. Discovering issues that automated scanners miss completely.

What it struggles with: Scaling. Cost. Automation. You can build some business logic tests into automated suites if you know exactly what to look for, but discovering novel business logic vulnerabilities still requires a human who understands both the application and how to break it. It also can be niche. If you’re looking for full-suite dynamic application testing, this isn’t it. You might be able to catch some of those vulnerabilities, but identifying known vulnerabilities in your running application is also super critical as well. 

New Kid on the Block: “AI Penetration Testing”

And now we arrive at the newest buzzword: AI Pen Testing. Let’s unpack what this actually means—and what it doesn’t.

What AI Pen Testing Promises

Vendors in this category promise AI that thinks like a penetration tester. AI that discovers complex vulnerabilities. AI that understands your application’s business logic. AI that replaces expensive human pen testing with automated intelligence.

Some claim their AI can reason about application workflows, chain multiple findings into complex attacks, and provide the same level of validation you’d get from a human security expert.

What Pen Testing Actually Requires

To understand the gap between promise and reality, let’s look to the real thing first. Real penetration testing has always included one critical component: third-party validation by experienced security professionals. A pen tester doesn’t just run tools—they understand the application’s business context, test workflows manually, chain together vulnerabilities that automated scanners miss, and validate whether behaviors are actually exploitable.

Pen testers know that three medium-severity issues might combine into something critical. They understand {insert example of a flaw that would be hard to determine if desired functionality or risk}. They grasp the concept of acceptable versus unacceptable values for parameters in your specific business context.

What an AI Pen Test Misses

So what is “AI Pen Testing” actually doing? In many cases, it’s business logic testing or DAST with a fancier name—and with AI plastered all over the marketing website. AI can help trace data flows through complex, obfuscated JavaScript. It can assist in understanding unfamiliar code patterns. It can enhance the efficiency of manual testing by automating certain reconnaissance and analysis tasks.

What it still struggles with—and this is critical—is reasoning about the entire application in context. AI doesn’t understand your business workflows. It can’t know for sure whether a behavior is acceptable. It can’t chain together findings the way an experienced pen tester does. It doesn’t grasp why three medium issues might be more severe together than apart. AI is a powerful tool for skilled security professionals, but it’s not a replacement for their expertise.

The False Sense of Security

There is real promise in this approach to application security testing as part of a robust program. But the danger is that teams might believe they’ve achieved pen testing-level security—at scale—when they’ve actually just run an automated scan with AI-enhanced capabilities. That’s not inherently bad—automated testing has real value—but calling it “pen testing” sets expectations the technology can’t meet and, in practice, could end up creating more distracting validation work for teams.

Even worse, some AI tools claim they can automatically fix vulnerabilities. Without understanding the broader context of your application, these “fixes” can introduce security regressions in other parts of your logic. Today, AI is too myopic to understand how changes to one component affect the entire system.

How to Choose: Start with Your Pain Point

So how does one navigate this landscape? Especially if you’re an AppSec engineer with a few years of experience and context in a very nuanced market under your belt.

Our advice: start with your actual problem, not the vendor’s buzzwords.

• If your problem is compliance and report-generating: Legacy DAST might be fine. Just don’t expect it to integrate with modern development workflows. And make sure that you buy a tool that supports your application architecture.
• If your problem is velocity and developer adoption: Look for true shift-left DAST that’s designed for CI/CD from the ground up. Ask vendors: “Does this trigger a separate legacy scan, or is it actually built for pre-production testing that runs within your pipelines?”
• If your problem is missing critical vulnerabilities in your business logic: You probably need human expertise—either internal security champions who understand your application or external pen testers. Some automated testing can support this, but don’t expect full automation.

If you’re getting excited reading an “AI Pen Testing” vendor’s website, when you meet with them for a demo, be sure to ask specific questions:

• What does your AI actually do versus what it can’t do?
• How does it handle business logic and multi-step workflows?
• Does it include third-party validation by security experts, or is it automated testing with AI enhancements?
• How does it scale to my SDLC processes? 
• Does it just create another pile of tickets I have to manage?

The vendors who give you straight answers about limitations are probably worth your time. The ones who promise their AI replaces all human expertise aren’t.

Conclusion: Ignore Buzzwords, Focus on Capabilities

At StackHawk, we are all in on AI. We’ve seen how it can be leveraged to provide real value to security testing by understanding applications and empowering developers to fix faster. It can enhance manual pen testing, improve code analysis, and automate certain reconnaissance tasks. But today, it cannot confidently replace human reasoning and organizational knowledge on its own.

The most effective application security programs combine multiple approaches:

• Shift-left testing integrated into development workflows
• Human validation where it matters most
• Realistic expectations about what automation can and can’t do

The next time a vendor tells you their AI-powered tool eliminates the need for pen testing, ask them this: If your AI scans an e-commerce application and finds that users can submit negative dollar amounts, does it know whether that’s a vulnerability or an intentional returns workflow?

If they can’t answer that honestly, you’ve learned everything you need to know about their “AI pen testing.”