SAST vs DAST: How SAST, DAST, SCA, IAST and Pentesting Fit Together

SAST vs DAST: the short answer

SAST (static application security testing) reads your source code without running it. It finds flaws early, while code is being written. DAST (dynamic application security testing) attacks your running app from the outside, the way a user or attacker would. It finds problems that only show up when the app is live. Most teams need both, because each one is blind to what the other sees.

Neither replaces a human tester. A good program layers SAST, DAST and software composition analysis (SCA) in the pipeline. SCA checks the open-source code your app borrows. Then it adds manual penetration testing for the flaws that tools cannot understand.

What is SAST?

SAST tools read source code, bytecode or binaries. They trace how data moves through the app and look for patterns that lead to flaws. One example is user input reaching a SQL (database) query without parameterization. Others are unsafe deserialization, passwords written into the code (hardcoded secrets) and weak encryption. Semgrep, CodeQL and SonarQube are common examples.

• Strengths: runs early, in the code editor (IDE) or on every pull request. It points to the exact file and line. It covers code paths that are hard to reach in a live app.
• Weaknesses: more false positives (warnings that turn out to be harmless), especially before tuning. Support varies by language and framework. It cannot see live settings, server setup or how parts behave once deployed.

SAST works best when rules are tuned to your code and findings show up as comments on pull requests. A pull request is a proposed code change waiting for review. A tool that dumps thousands of unchecked results into a dashboard tends to get ignored.

What is DAST?

DAST tools work with a running app, usually in a test or staging setup. They crawl pages and API endpoints, the addresses other programs call. They send crafted inputs and study the replies for signs of flaws. Common finds include cross-site scripting (XSS), where an attacker plants harmful script in a page. Others are SQL injection, missing security headers, unsafe cookies and exposed debug pages. ZAP and Burp Suite are widely used examples.

• Strengths: works with any programming language and tests the real deployed setup. Its findings can usually be repeated with a single request and response.
• Weaknesses: runs later in the cycle and cannot point to the line of code. It struggles with complex logins and single-page apps unless set up with care. It only tests what it can reach. API scanning needs an OpenAPI definition or recorded traffic to be thorough.

SCA, IAST and manual testing

SCA (software composition analysis) checks the open-source libraries and packages your app depends on. It compares them against lists of known flaws, and many tools flag license issues too. Modern apps pull in many third-party packages, so SCA often finds the most issues for the least effort. Dependabot, OWASP Dependency-Check and Snyk are common examples. OWASP is a nonprofit that publishes free web security guides. Many SCA tools can also produce a software bill of materials (SBOM), an ingredients list for your software.

IAST (interactive application security testing) places an agent inside the running app, usually during QA or automated test runs. The agent watches code run from the inside. So it can report a flaw with both the HTTP (web) request that triggered it and the line of code involved. It tends to raise fewer false alarms than SAST. But it only sees code paths your tests exercise, and it supports a limited set of languages and frameworks.

Manual penetration testing is a skilled tester using your app with intent to break it. Testers use tools, but the value is human judgment. They learn what the app is supposed to do, then find ways to make it do something else.

Application security testing methods compared

This table shows when each method runs, what it finds best and what it costs.

False alarm rates and cost depend a lot on the tool, the language and how much tuning you do. Treat this table as a general guide.

Where automated application security tools fall short

Scanners are good at spotting patterns. They are poor at understanding intent, and some of the most damaging flaws have no pattern to spot. Broken access control means users can see or do things they should not. It was ranked first in the 2021 edition of the OWASP Top 10. It is exactly the kind of issue automated tools tend to miss. Typical examples:

• Insecure direct object references (IDOR): changing an invoice ID in a request returns another customer's invoice. To a scanner, the reply looks normal, because it is a valid invoice.
• Privilege escalation: a regular user calls an admin-only API endpoint. The screen hides it, but the server never checks who is asking.
• Business logic abuse: using a discount code twice, skipping a payment step or entering a negative quantity to trigger a refund.
• Tenant isolation failures: data from one customer account leaks into another through a shared search or export feature.
• Chained issues: several low-severity findings that together let an attacker take over an account.

To find these, a tester needs to know what each role should and should not be able to do. That is why manual penetration testing stays in every serious program. It is also why large customers often ask to see a recent pentest report. See does SOC 2 require a penetration test for how this plays out in compliance reviews.

How to combine SAST, DAST and SCA in a CI/CD pipeline

Here is a practical layout for a team that ships code weekly or faster. Each step catches a different kind of problem:

1. Developer workstation: SAST and secrets scanning in the code editor or as pre-commit hooks. This catches issues before code is pushed.
2. Pull request: SAST on changed code and SCA on any dependency changes. Block merges only on new high-severity findings, so people keep trusting the gate.
3. Build: SCA on the full dependency tree, container image scanning and SBOM generation.
4. Test or staging deploy: a DAST scan that logs in, with an up-to-date API definition. Add IAST here if your stack supports it and your test coverage is good.
5. Production: ongoing SCA checks for new CVEs (public IDs for known flaws) in libraries you already ship. Add logging and monitoring for attacks.
6. Before major releases and at least once a year: a manual penetration test focused on login, access rights and business logic.

Send every finding, automated or manual, into the same backlog with the same deadlines by severity. Our vulnerability management guide covers how to set those deadlines (SLAs).

How NextGarrison helps

NextGarrison runs manual penetration tests of web apps and APIs. We focus on what scanners cannot see: access control, login and business logic. Share your SAST and DAST results with us. Then we can spend testing time on the gaps instead of repeating what your tools found. We retest fixes. If your product has LLM features (AI built on large language models), we test those too.