How should security teams combine static and dynamic analysis for AI-assisted code review?

How to Use Static Analysis to Seed AI Review

Static analysis is most useful here when it narrows the review surface before the model reasons about code paths. It should identify suspicious patterns, risky sinks, weak input handling, unsafe deserialisation, insecure defaults, and review hotspots that deserve dynamic proof. For a broader programme view of application security workflow design, pair that triage with explicit review gates rather than asking the model to inspect every line equally.

Good teams use the static pass to create a ranked hypothesis list, not a final verdict. That means the model should be given the relevant file, function, dependency, and control-flow context, plus the specific question it is trying to answer. The more precise the input, the less likely the agent is to recycle generic findings or overstate a pattern that only looks risky in isolation.

Static analysis also works best when it is used to preselect candidates for human validation. If the output is too noisy, the model will waste tokens re-litigating false positives instead of building evidence around the findings that matter. This is why secure code review practices still matter even when AI is assisting, because the review process needs a disciplined way to separate likely defects from merely suspicious code.

What Dynamic Validation Should Prove

Dynamic validation should answer a different question: does the suspected weakness actually change behaviour in the running application? That can include whether a payload is accepted, whether a control is bypassed, whether an error path is reachable, or whether an apparently dangerous branch is dead code in production. The dynamic pass should confirm exploitability, scope, and observable effects rather than repeat the static suspicion in different words.

That distinction matters because some code smells are only architecture-level warnings, while others become real only when state, configuration, data, or execution order line up. Dynamic testing therefore gives the AI reviewer an evidence anchor. It reduces the chance that a plausible-looking issue is promoted without runtime confirmation, especially in systems where feature flags, middleware, or framework behaviour change the effective attack surface.

When teams need a more structured testing lens, AppSec programme guidance is a useful reference point because it treats SAST, DAST, IAST, and review triage as complementary controls. The practical lesson is simple: static analysis finds where to look, dynamic validation tells you whether the weakness survives contact with the runtime environment.

How to Orchestrate the Two Without Creating Review Noise

The strongest pattern is thin orchestration and thick context. Thin orchestration means the AI should receive a bounded task, such as “verify these five hotspots,” rather than a broad prompt like “review this repository.” Thick context means the agent gets the code fragment, surrounding call chain, config assumptions, and the exact expected security property, so it can compare the static hypothesis against runtime behaviour without wandering.

This pattern is especially effective when the review pipeline separates discovery from confirmation. Let static analysis nominate candidates, then let dynamic tooling test the nominated behaviours, and only then ask the model to summarise evidence. If those stages are collapsed, the agent tends to overfit to the first clue it sees, which lowers evidence quality and makes duplicate findings more likely.

For teams formalising this kind of workflow, the AI-Assisted Code Review and AI SAST Guide is directly relevant because it covers AI triage, autofix guardrails, and safe review rollout. The value of that approach is not that the model does everything, but that it is constrained to high-signal work where the static and dynamic stages reinforce each other.

Risk and Threat Considerations

AI-assisted review creates a failure mode where static suspicion and runtime proof get blended too early. When that happens, teams can either ship false confidence from an untested static pattern or waste time chasing issues that never materialise in execution. The risk grows when reviewers treat a model’s explanation as equivalent to evidence, especially in codebases with complex control flow or environment-dependent behaviour.

Failure mechanism: the agent infers exploitability from source-level patterns without validating the behaviour in a live or instrumented environment, or it treats a dynamic anomaly as proof of a defect without checking whether the condition is reproducible and security-relevant.

Impact: teams miss real defects, over-prioritise harmless ones, and lose trust in the review pipeline. At scale, that can create backlog churn, delayed remediation, and a false sense of assurance around code that has not been evidence-checked.

Practitioner Guidance

What to prioritise: start by defining which classes of findings require runtime confirmation before they are allowed to influence severity. Authentication bypass, injection, access-control questions, and state-dependent logic usually need dynamic proof; style issues and low-risk hygiene findings often do not.

What to verify: make sure the static stage emits a narrow, inspectable hypothesis, and that the dynamic stage records the exact input, environment, and observable result used to confirm or reject it. If the evidence cannot be reproduced, the finding should stay provisional.

Common mistake: letting the model “explain” a static alert as if explanation were validation. The better operating rule is to let static analysis select candidates, then require runtime evidence before the issue becomes a trusted security finding.

Practitioner takeaway: use static analysis to focus attention, but reserve credibility for findings that dynamic testing can actually demonstrate in the running system.

Related resources from NHI Mgmt Group

• AI-Assisted Code Review
• Static and Dynamic Analysis
• AI-Assisted Code
• Dynamic Analysis