A Developer’s Guide to Dynamic Analysis in Software Security

Here’s the uncomfortable truth: your code review passed, your SAST tools gave you the green light, and you still shipped a critical vulnerability. Why? Because some of the most exploitable security flaws only exist when your application is actually running.

Modern applications are API-first architectures where the real attack surface only materializes at runtime. AI and LLM components introduce endpoints that don’t exist in your source code. Authentication flows, rate limiting, business logic vulnerabilities—none of these can be detected by static analysis. They only surface when the application is executing.

For years, DAST was written off as too slow, too clunky, too late to matter. Traditional tools acted as final security gates, flagging issues only after code was deployed or about to be. Every finding became an emergency fire drill. So teams doubled down on static analysis instead.

But here’s the irony: just as we collectively decided dynamic testing was outdated, the application landscape changed in ways that make it more essential than ever.

The answer isn’t to skip runtime testing. It’s to reimagine it. Modern approaches integrate dynamic analysis directly into CI/CD pipelines, catching exploitable vulnerabilities before production while developers still have context. Runtime security that actually scales with AI-driven development velocity.

In this guide, we will look at what dynamic analysis is, why the traditional security model is flawed, and how teams are addressing this issue by putting runtime testing directly into developers’ hands. We’ll cover when dynamic analysis catches stuff that static tools miss, how to integrate it without becoming everyone’s least favorite bottleneck, and why the future of AppSec means developers owning security from the first commit.

What Is Dynamic Analysis?

The first and most important thing to note is that dynamic analysis tests software while it’s running. It’s a blackbox approach that requires no source code scanning, and no theoretical vulnerabilities just because code matches a potential pattern. These tools just watch what your application actually does when it executes and reports back any issues that occurred.

The key difference is that you need a live, running instance. You’re observing a real system processing data, managing state, interacting with APIs, and communicating with databases. This is where runtime behavior reveals vulnerabilities that appear to be fine in code review or vulnerabilities that appear in the code but could never actually be exploited.

Depending on exactly what tool you are using for the dynamic analysis, what gets monitored could include everything from memory allocation and cleanup, network requests and API calls, authentication flows, how the app handles malicious inputs, resource usage and state management, and database queries built from user input.

Dynamic analysis identifies problems that only exist within a specific context. Race conditions that need precise timing. Injection vulnerabilities that depend on how three services handle a specific input sequence. Configuration errors that only show up in deployed environments. What it finds are the exploitable flaws attackers actually use, the ones that should truly be patched before a production deployment.

Dynamic Analysis Vs. Static Analysis: What’s the Difference?

It wouldn’t be a complete guide without bringing up this subject. The industry loves the “SAST vs. DAST” debate like you have to pick a side. That’s a false choice that misses the entire point. Static analysis tools, like SAST, don’t need running code and instead look for particular patterns that could potentially signal a vulnerability or performance issue. On the flip side, as we mentioned earlier, dynamic analysis is performed against the running application and finds those vulnerabilities that are truly exploitable. As you can probably see, both are quite complementary, especially when looked at side-by-side:

What	Static Analysis	Dynamic Analysis
Tests	Code patterns and structure	Actual runtime behavior
Runs	Before deployment	During execution
Speed	Fast, seconds to minutes to scan code depending on repo size	Can takes time since test execution relies on running application
Coverage	Every line theoretically	Only tested execution paths
Catches	Known patterns, syntax errors	Runtime bugs, auth bypasses, config issues
False positives	High—flags risky-looking code even if it’s not exploitable	Low—only reports what it observes as a true defect
Setup	Easy, just needs code access	Slightly harder since it needs a running application environment to test against

In short, static analysis catches patterns while dynamic analysis catches actual behavior.

This means that static tools are great for obvious stuff early on, like finding SQL queries built from unsanitized strings, hardcoded secrets, and deprecated functions. They’re fast, some can run in your IDE or terminal, and catch problems before you even commit. But they can’t tell you if that SQL injection is actually exploitable in your running app, or whether those auth checks actually work when someone tries to bypass them.

Dynamic analysis flips this. It only tests code that executes, meaning untested paths are blind spots. However, it catches runtime-specific problems that no amount of code review can find, such as authentication and authorization bypasses and configuration mismatches between environments, such as CORS misconfigurations.

So, the actual question really isn’t which one to use; rather, it’s how to use both without killing your velocity.

Good teams run static analysis continuously during development (using IDE plugins, CLI’s, and pre-commit hooks) to catch obvious flaws quickly. Then they run dynamic analysis on local builds, in CI/CD and staging to validate that the running application is actually secure, not just theoretically secure based on code patterns.

Core Techniques & Approaches in Dynamic Analysis

For modern API-first applications running in CI/CD pipelines, Dynamic Application Security Testing (DAST) is the foundation. It’s the only approach that tests your running APIs exactly as attackers see them, finding exploitable vulnerabilities in authentication flows, authorization logic, and business logic, i.e. the stuff that matters most for today’s development teams.

That said, the dynamic analysis landscape includes several specialized techniques that complement DAST for specific use cases. Here’s how they fit together:

Technique	What It Does	When It Complements DAST
Modern DAST	Automated security testing of running apps/APIs	Your primary testing approach—run in CI/CD on every deployment
Fuzz Testing	Throws malformed inputs to find crashes	Add for components parsing untrusted binary data (file parsers, protocol handlers)
IAST	Monitors from inside the runtime, tracks data flow	Staging environments when you need deep trace analysis
Behavioral Analysis	Watches for deviations from expected patterns	Production monitoring for API abuse and anomaly detection

Modern DAST: Built for Developer Workflows

Traditional DAST tools were built for security teams to run quarterly scans—a model that died years ago. Modern DAST is designed for developers to run continuously, both locally and in CI/CD pipelines.

A modern, shift-left DAST solution like StackHawk automatically discovers API endpoints, tests for OWASP Top 10 vulnerabilities, and delivers findings in formats developers understand with no security expertise required to configure. This is where most teams should start and where the majority of exploitable vulnerabilities get caught.

When to Layer in Specialized Techniques

Fuzz Testing sends malformed and unexpected inputs to your APIs to uncover edge cases and vulnerabilities that standard testing misses. Modern API fuzzers can work from OpenAPI specifications to generate test cases that deliberately violate expected structures. StackHawk’s DAST platform complements API fuzzing efforts by confirming whether bugs found during fuzzing are exploitable security vulnerabilities. Most teams run fuzzing for high-risk endpoints handling sensitive data, integrating it into CI/CD alongside their DAST testing.

IAST (Interactive Application Security Testing) sits inside your application runtime, tracking data flow from entry to exploit. It provides detailed vulnerability traces but requires deploying agents into your application stack. The overhead and integration complexity make sense for staging environments where you need deep analysis, but DAST catches most runtime issues without the deployment burden.

Behavioral Analysis establishes baselines of normal behavior and flags deviations, which are useful for detecting API abuse patterns and novel attacks in production. Think of it as runtime monitoring rather than pre-deployment testing. It complements DAST by watching for threats after deployment, not replacing the testing that should happen before code ships.

The Reality for Most Teams

If you’re building modern applications with APIs and microservices, start with DAST integrated into your CI/CD pipeline. It catches the vast majority of exploitable runtime vulnerabilities without requiring specialized infrastructure or security expertise. Add the other techniques only when you have specific needs that DAST doesn’t address, and even then, DAST remains your foundation.

Integrating Dynamic Analysis into DevOps Pipelines

For dynamic analysis to be holistic, integration into CI/CD is critical. That said, some common challenges with dynamic analysis in CI/CD is that it needs running infrastructure, it takes time to configure, and can be noisy. Get it wrong and you’ve created the gate that slows everything down. Two ways to make sure this integration is successful is to use a tiered testing approach and to be aware of the environments to run testing within.

Tiered Testing: The Only Approach That Works

Running a comprehensive dynamic analysis on every commit sounds amazing. It doesn’t work in practice for high-velocity development teams. Here, I’m talking about everything under the dynamic analysis umbrella, not just DAST. 

Testing Tier	Trigger	Coverage	Duration	Purpose
Local Machine	Manually via terminal or MCP	Entire app/latest code changes	Under a minute (for smaller codebases)	Developers can check local code for issues before they even commit the code
PR Checks	Every pull request	Critical endpoints, high-risk flows	Under 5 minutes	Fast feedback on new code
Nightly/Staging	Scheduled, pre-production	Comprehensive API testing, full flows	15-30 minutes	Catch what PR checks missed
Deep Analysis	Weekly, dedicated infrastructure	Fuzzing campaigns, exhaustive testing	Hours	Edge cases, complex vulnerabilities

Using this approach, you can see that the fastest tests should run frequently and comprehensive tests run less often. Both matter, but attempting to conduct comprehensive testing on every commit can hinder your pipeline velocity, including if developers are running massive testing operations on their local machines every time they create even a little bit of code. Certain types of testing make sense to run frequently while others can be used more strategically so you don’t eat up developer time and your build minutes.

Environment Reality

You need somewhere to run this stuff. Local development gives fast feedback but limited infrastructure. Staging environments strike a balance between realism and safety, where comprehensive dynamic testing typically occurs. Production offers ultimate realism but needs guardrails for runtime monitoring and RASP. Containers plus service mocks work as a compromise: run your app in Docker with mocked dependencies for something faster than full staging but more realistic than local-only. The best setups maintain staging environments that mirror production architecture, accepting the infrastructure cost as the price of identifying and addressing vulnerabilities before they become exploitable.

Dynamic Analysis Tools in Modern Software Testing

There are plenty of tools out there that can round out a dynamic analysis stack. Of course, at StackHawk we believe the main hub is DAST since it can give truly comprehensive feedback on exploitable vulnerabilities. That said, layering on the other techniques we discussed is also critical if you want to cover your attack surface from as many angles as possible.

StackHawk: DAST Built for How Developers Actually Work

When it comes to DAST, StackHawk represents the modern approach: Dynamic security testing designed for developers, not just security specialists. We believe that DAST should cover all aspects of the running application, including one of the more critical components: your APIs. 

StackHawk’s API Discovery tool uses source code to reveal your complete API landscape, including shadow APIs before deployment. This enables automatic testing that discovers endpoints without a whole bunch of manual setup and complex integrations. 

On top of these capabilities, our developer-native workflows run naturally in CI/CD, integrating seamlessly with GitHub, GitLab, and Jenkins. The feedback from the tool can easily be digested in reports and translated into actionable findings, even allowing developers to generate cURL commands to reproduce vulnerabilities and showing potential fixes to guide developers down the right path.

Best suited for teams implementing DevSecOps, organizations with API-first architectures, and companies that want developers to own security even if they don’t have dedicated AppSec personnel. This follows our belief in shifting security left by making testing as natural for developers as running their functional test suites.

Metrics to Measure Dynamic Analysis Success

When it comes to measuring how effective tools are, there are a few core metrics to focus on. Here’s what actually indicates whether dynamic analysis is working:

Metric	Why It Matters
Time to detection	How fast do you find new vulnerabilities after introduction?
Time to remediation	How long from finding to fixing?
Coverage drift	Is new code getting tested or creating blind spots?
False positive rate	Are developers ignoring findings because they’re mostly noise?
Production incidents	Are vulnerabilities still reaching production despite testing?

Vulnerability detection effectiveness measures whether you’re finding real problems. Track severity and count, but more importantly, track false positive rates. A tool generating 500 alerts with 95% false positives is worse than one generating 50 alerts with 90% accuracy. In short, more alerts doesn’t necessarily mean the tool is working well.

Integration efficiency measures how smoothly it fits into workflows. Monitor test execution time and the percentage of builds that include dynamic analysis, versus those that skip it. Measure the mean time to remediate: how long from detection to deployment of a fix?

Business impact connects testing to outcomes executives care about. Measure the reduction in security incidents reaching production and the cost avoided by catching vulnerabilities before production. After all, security is about ensuring that customer and internal data is safe so that the business can grow confidently without the burden of breaches or other security incidents that undermine growth.

Final Thoughts: Making Dynamic Analysis Work for You

Dynamic analysis alone is not a silver bullet, as with any testing method used in isolation. What dynamic analysis is good for: Finding exploitable runtime vulnerabilities before attackers do. Validating that security controls are effective in operational environments. Catching configuration issues that only surface during execution. 

Testing APIs and auth flows as attackers see them. What it’s not good for: Finding every possible vulnerability (since coverage depends on what you test). Replacing static analysis or code review (they catch different things), and trying to cover up a fundamentally insecure architecture. It is simply one piece of the security puzzle.

The real goal of dynamic analysis (and other security tools and techniques) is to ship measurably more secure software at a pace your business can sustain. Dynamic analysis helps you find and fix vulnerabilities faster than you introduce them. Ready to see how modern dynamic analysis and DAST fits into your workflow? Try StackHawk free and start testing your APIs in minutes, or schedule a demo to see how teams are shifting security left without slowing down delivery.