Why Dynamic Application Security Testing (DAST) needs AI
Traditional Dynamic Application Security Testing (DAST) tools are designed to always follow the same route, crawling through web applications and firing off predetermined attack payloads. But as applications get more complex, with so many technologies, and security built around them, we need to be more aware of the context they are operating in. Also, configuring these scanners often requires a lot of configuration so we needed something much faster to set up with minimum user input.
From static rules to adaptive intelligence – Our AI-enhanced approach
Traditional DAST scanners are powerful but indiscriminate. They fire the same payloads regardless of the context and treat vulnerabilities in a comments field the same way it would be in a payment processing endpoint.
Our AI-enhanced approach uses Large Language Models to:
- Generate dynamic configuration depending on the context: When a user says “scan my modern e-commerce site” the AI will configure AJAX spider settings to enable SPA specific configuration.
- Optimize tool parameters: After technology detection, the AI configures different tools like Nuclei with technology specific parameters and templates.
- Intelligent Analysis: After finding a vulnerability like XSS or SQLi, the AI can execute an attack to confirm the vulnerability and provide the reproduction steps and remediation strategy.
With this AI-enhanced approach, organizations achieve significantly faster and more accurate vulnerability detection. Security teams spend less time on manual configuration and validation, freeing up their time for higher-value tasks. See the below demo to see how this works in practice.
The role of LangGraph in orchestrating security workflows
LangGraph is an open source framework for building AI agents. It really shines on making it easier to orchestrate tasks called “nodes” where we can combine predictable flows with AI decisions as well as easily define asynchronous and dependent tasks.
AI-powered tool configuration and optimization
This is probably one of the best features. We can use the most popular cybersecurity tools without needing to configure them manually. The AI analyzes what it is testing, interprets the context, and automatically adjusts tool settings based on its observations and the outputs of previously run tools.
Common tools it configures are:
- ZAP
- Nmap
- Nuclei
- Sqlmap
- Fuff
Automated reporting with contextual insights
AI brings so much flexibility for reporting, the scans and agent findings are aggregated, and sometimes duplicated by different tools. We can easily prompt the AI to analyse those results and generate the expected report with examples, explanations and remediation instructions.
Section of a vulnerability report on the vulnerable demo website testfire.net
Closing the gap between pentesters and machines
It’s not about replacing human security professionals. It’s about providing them with a better tool that is easy to set up and flexible. It also helps with the simple, repetitive tasks of setting up different tools and consolidating the results into a report.
At the same time, many smaller projects lack the resources to hire dedicated security professionals. For them, this solution provides an opportunity to access powerful capabilities that help developers build more secure software from the start.
Conclusion: Towards smarter application security
We are at a pretty exciting inflection point in application security. For too long, we’ve been stuck choosing between thorough-but-dumb automated tools and smart-but-unscalable human expertise.
This AI-driven approach feels like the first real attempt to bridge that gap. By combining LangGraph’s orchestration capabilities, modern LLM reasoning, and established security tools, we’re able to both find and understand vulnerabilities to a much greater extent than we could in the past.
