Because static tools see code structure, while dynamic tools see runtime behaviour. If you only use one, you miss either defects that never reach execution or flaws that only appear after deployment, such as misconfigurations, injection paths, and environment-specific trust failures.
Why This Matters for Security Teams
Using only static testing or only dynamic testing creates a blind spot in application assurance. Static analysis is strong at catching insecure patterns in source code, dependency misuse, and weak control logic before release. Dynamic testing is better at exposing runtime weaknesses such as authentication failures, environment-specific trust issues, and misconfigurations that only appear in a live stack. Security teams that treat either method as complete tend to overestimate coverage and understate residual risk.
This matters because modern application risk is not limited to code defects. Deployment pipelines, cloud configuration, secrets handling, API exposure, and third-party integrations all shape the attack surface. A single testing method can validate one layer while missing another, which is why guidance in the NIST Cybersecurity Framework 2.0 emphasises coordinated risk management across identify, protect, detect, respond, and recover activities. In practice, many security teams encounter the missing failure mode only after an application has already been deployed into a real environment.
How It Works in Practice
Static testing, such as SAST, software composition analysis, and code review, evaluates what the application could do based on code, libraries, and configuration artifacts. Dynamic testing, such as DAST, interactive testing, and runtime scanning, evaluates what the application actually does when requests, sessions, and integrations are exercised in a live or staged environment. The two methods answer different questions, so they should be treated as complementary controls rather than substitutes.
A practical assurance program usually layers both methods around the delivery lifecycle:
- Run static checks early to catch insecure coding patterns, unsafe dependencies, hardcoded secrets, and control gaps before merge.
- Run dynamic checks after deployment to validate authentication flows, session handling, input handling, and exposed services in the target environment.
- Use findings from one method to improve the other, for example by turning a runtime issue into a new static rule or test case.
- Include cloud and infrastructure checks, because application risk often comes from misconfigured storage, network policy, or identity permissions rather than source code alone.
Current guidance suggests this layered approach is especially important for applications that consume APIs, use containerised delivery, or depend on external identity providers. It also helps security teams separate code defects from operational drift. The Anthropic report on the first AI-orchestrated cyber espionage campaign is a reminder that adversaries increasingly combine automation, code abuse, and live environment probing, so assurance must cover both build-time and run-time exposure. These controls tend to break down when teams test only in a synthetic lab that does not match production identity, network, or secrets configurations because runtime trust failures never surface.
Common Variations and Edge Cases
Tighter testing coverage often increases delivery time and review overhead, requiring organisations to balance release speed against assurance depth. That tradeoff becomes more noticeable in fast-moving DevOps environments, where teams want rapid merges but also need evidence that both code-level and runtime risks are being controlled.
There is no universal standard for how much static and dynamic testing is enough. Best practice is evolving toward risk-based coverage, where internet-facing systems, payment flows, authenticated admin paths, and applications handling sensitive data receive deeper combined testing than low-risk internal tools. For example, a public-facing API may need strong dynamic validation for auth and rate limiting, while a library service may need more emphasis on static dependency and code analysis.
Edge cases also matter. Static tools can miss issues hidden behind feature flags, environment variables, or generated code. Dynamic tools can miss dormant defects if test data does not exercise the vulnerable path. In identity-heavy applications, a good test strategy should also include SSO, token handling, privilege boundaries, and session lifecycle checks, because many failures appear only when the application interacts with real identity and access control systems. For broader security governance, this is where application testing starts to overlap with operational control evidence rather than just secure coding.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
MITRE ATT&CK and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0 and NIST AI RMF set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST CSF 2.0 | GV.RR-01 | Testing choice should reflect enterprise risk and assurance responsibilities. |
| MITRE ATT&CK | T1190 | Runtime testing helps expose externally exploitable application weaknesses. |
| NIST AI RMF | MAP | Risk mapping supports identifying where testing coverage leaves gaps. |
| OWASP Agentic AI Top 10 | Agentic and API-driven apps need validation beyond code review alone. |
Use dynamic testing to validate exposure paths that map to application-layer exploitation techniques.
