Static analysis reads source code without running it, dynamic analysis attacks the running application, and machine learning helps sort the results or find patterns rules miss. Each sees a different slice of the problem. Together they find more real flaws and waste less time on false alarms.
What does static analysis see?
OWASP maintains a public list of source code analysis tools, which examine source code or binaries for quality and security defects. This is static analysis: it works on the code as written, without running it. It sees code paths that tests never reach, and it works early, on a pull request, before anything is deployed. Its weaknesses are false positives, because it lacks runtime context, and blindness to problems that only exist in configuration, deployment or the interplay of services.
What does dynamic analysis see?
Dynamic testing sends requests to a running application and watches the responses, the way an attacker would. The OWASP Web Security Testing Guide organizes this work as a testing framework. It finds problems that only appear at runtime: a missing authentication check in a deployed route, a misconfigured header, an exposed admin panel, a broken session setting. It cannot see code it never reaches, and it tells you where the symptom is, not which line of code caused it.
Where does machine learning fit?
In three honest places.
Triage. Ranking findings by likelihood that they are real and exploitable, so engineers read the top of the list first.
Pattern finding. Spotting suspicious code or behavior that a hand-written rule would not describe, such as unusual package behavior.
Explanation and fixes. Summarizing a finding and proposing a patch for a human to review.
It is not a substitute for the other two. A model can be wrong, can miss novel flaws and can produce confident nonsense. Keep a person in the loop for decisions that matter, and test the model's output against known cases.
Why not just pick one?
NIST's Secure Software Development Framework (SP 800-218) recommends a core set of high-level practices, including verifying and testing software for vulnerabilities. It does not say one technique is enough. A single method creates blind spots.
A constructed example
A web application has a route that returns a user's invoice by id. Static analysis flags a database call built from a request parameter, a likely SQL injection, and points to line 84. Dynamic testing finds that the route also lets any signed in user fetch another user's invoice by changing the id, an authorization flaw, which static analysis had no way to know was wrong because the code looks tidy. A machine learning ranker puts both near the top because the route handles financial data and is internet facing, and drops a dozen low-value style warnings to the bottom. Neither tool alone would have given the team that picture.
Safeguard provides code scanning and dynamic web app scanning together with reachability analysis to help rank what matters. The demo below shows the web scan.
See the full walkthrough at /demos/dast-web-app-scan.
What does this cost in practice?
More tools mean more noise unless you manage it. Agree on a small set of rules that block a merge, and send the rest to a backlog with owners. Run dynamic scans only against environments you own and are authorized to test, because they send real attack traffic. Budget time for tuning in the first month. The payoff comes when engineers trust the results enough to act on them, and that trust comes from a low false positive rate on the findings that block their work.
How to check or apply this
- Run static analysis on every pull request and fail on a short list of serious, high confidence rule types.
- Run dynamic scans against a staging copy on a schedule and before major releases.
- Feed findings into one list and de-duplicate across tools.
- Use ranking, whether manual or model assisted, to decide the order, and review it periodically against what turned out to be real.
- Track false positive rates and time to fix for each method.
- Keep human review for fixes and for anything affecting authentication, payments or personal data.
Quick check
Can machine learning replace static analysis?
Not reliably. It can help rank and explain findings, but rule based and data flow analysis remain useful for catching known classes of flaw.
Which should I run first?
Static analysis, because it runs early and cheaply on code. Add dynamic testing as soon as you have a deployable test environment.
Sources
- OWASP, Source Code Analysis Tools
- OWASP Web Security Testing Guide
- NIST SP 800-218, Secure Software Development Framework
Last reviewed: 18 October 2026.
