Static Application Security Testing (SAST) found favor among security teams as an easy way to deploy security testing without really engaging developers. With the ability to analyze source code early in the software delivery lifecycle, SAST solutions offered a more proactive approach to finding security issues prior to production. But this came with a cost.
Many of the findings of SAST tools are potential vulnerabilities – meaning that a lot of manual effort and time is required to evaluate and prioritize the risks. Developers are left to parse through thousands of alerts with minimal context into which issues are most critical, let alone how to map them to CWE categories to pinpoint the real problem and necessary fix.
A pain in the SAST
SAST’s initial promise of proactive security testing for security teams, quickly established itself as a fundamental problem for developers due to its extreme noise. By flooding developers with thousands of findings, mostly “false positives”, and flagging code patterns as “potentially insecure” without assessing actual exploitability, it has made it nearly impossible for developers to prioritize which issues to fix. SAST also fails to provide meaningful context; it might detect a problematic code pattern, but it cannot determine if that code is actually in use, exposed to the internet or actively contributing to an exploitable vulnerability. It fails to answer the questions that matter, e.g. Is this code running in production? Is it exposed? Can it actually be exploited?
The result? Thousands of alerts, yet nothing gets fixed. Developers are left swimming in Jira tickets, scattered across PRs and backlogs, with vague “ASAP” deadlines and no clear sense of priority.
Meanwhile, software still needs to ship.
SAST requires runtime context for prioritization
Security without context is just guesswork. Without knowing what’s exploitable, where the risk exists in the codebase, and how to fix it – security testing is just a flood of meaningless alerts. Modern Dynamic Application Security Testing (DAST) flips the script by testing applications in runtime, pinpointing real risks instead of theoretical ones. Instead of dumping a report full of “potential” issues, it tells developers clearly: This vulnerability is exploitable, in this service, on this line of code. That’s the power of context we’ve built at StackHawk.
Now, I’ll be the first to admit that legacy DAST had its own problems. These solutions have traditionally been slow, misaligned with developer workflows, and blind to APIs. Running scans against production took hours or days, and even when vulnerabilities were found, there was no way to trace them back to the source code for easy remediation. Without API testing, legacy DAST missed a massive portion of the modern attack surface, leaving organizations exposed.
But modern DAST solutions solve these problems by running as code, testing APIs and microservices in real time, and delivering instant feedback where developers work.
Modern DAST + SAST = actionable insights
Modern DAST will continue to evolve and support the speed of modern software delivery, which is only moving faster with the rise of AI. In the world of DAST 2.0, APIs and applications are being tested in real-time as developers write and merge code. Engineers and security teams alike now get immediate, actionable security insights with their workflows, instead of sifting through a flood of potential vulnerabilities.
To put it succinctly: by combining SAST findings with modern DAST context, engineers can prioritize what matters and open the floodgates for faster, safer software delivery.
Conclusion
SAST helped define the early era of AppSec but it was never designed for the scale, complexity, and speed of modern development. Developers are drowning in noise, and security teams are buried under vulnerabilities that lack clear prioritization. With AI-assisted coding, the future for SAST may become obsolete, with AI copilots generating fixes and recommendations.
Modern DAST delivers validation where it matters at the point of risk, in real time, integrated directly into engineering workflows. It’s not just about finding vulnerabilities but proving what’s exploitable and enabling fast, effective fixes.
