Semgrep combines code rules and AI reasoning for security review

Semgrep combines static analysis with AI detection, triage and remediation. Its practical fit depends on scan mode, repository access and contributor-based pricing.

01 / ProductRules and AI have distinct jobs inside Semgrep

Semgrep Code combines conventional static analysis with AI-assisted inspection. Rules can identify repeatable patterns, while contextual reasoning can help investigate logic that is harder to express as a simple pattern. The company groups its AI capabilities under Semgrep Multimodal. Code, Supply Chain and Secrets remain distinct products, so the AI branding should not obscure what is actually scanned.

The useful question is how these layers contribute evidence about a change. A static rule might identify a dangerous operation; AI can help explain whether surrounding code mitigates it or suggest a repair. Conversely, an AI finding about business logic needs a clear account of the unsafe behavior. Neither a rule identifier nor a persuasive explanation is sufficient alone. Reviewers need enough code context to verify the result.

The Multimodal overview distinguishes explanations, remediation guidance, suggested fixes and Autofix. Suggested fix guidance is not itself an inline code diff; Autofix can generate proposed changes in pull or merge requests. This distinction is useful for planning work. A security team may want a better explanation before it is ready to let an integration open branches, and a developer may need an actual patch rather than another instruction paragraph.

02 / AudienceA fit for engineering teams that can act on findings

Semgrep is relevant when developers work through repositories and pull requests, and security engineers want to make the findings in that workflow more useful. It can also serve a team that has repeatable internal coding standards to express as rules. The strongest pilot includes someone who understands the threat and someone who maintains the code. Otherwise, success can become a count of comments rather than a count of corrected vulnerabilities.

A team with unusual build steps or strict limits on source processing should first choose the execution route. A managed scan offers a different operating model from a job inside an existing CI pipeline. An organization needing to inspect runtime behavior will still need appropriate dynamic or production evidence. Static analysis can inform a release decision, but it cannot observe every behavior of a deployed service.

Snyk’s developer security workflow is a useful comparison when code and dependency risk both matter. CodeRabbit’s AI review approach helps frame a separate question: whether a pull request needs broad review feedback or security-specific analysis and policy. These approaches may coexist. Compare the actual artifacts a reviewer receives, the data they need and the decisions they can support.

03 / WorkflowA proposed workflow that separates detection from repair

Begin this proposed evaluation with one service and a fixed reference branch. Sequenced has not tested this workflow. Choose representative changes with known security implications, including one true issue and one case protected by an existing control. Preserve the expected behavior before running scans. This lets the team examine both false positives and missed issues rather than celebrating a quiet dashboard.

Choose either managed scanning or a CI integration based on the repository. Managed Scans documentation describes weekly full scans and pull-request or merge-request scans, and warns that existing custom semgrep.yml configurations are not automatically copied. It also says managed scanning does not generate missing dependency manifests or lockfiles. A project that needs preparation steps may therefore need its existing CI environment instead.

Run the initial scan, review the underlying finding and then inspect the AI explanation. Ask the reviewer to identify the vulnerable path and any mitigating context independently. Keep a sample of findings that AI recommends ignoring. Noise reduction is useful only when the ignored material is safe to deprioritize; it should not make errors invisible. Track whether the team can reconstruct why a decision was made after the original author is unavailable.

For accepted issues, compare guidance with a generated patch where supported. Require normal tests, targeted behavior checks and a rescan before merging. Measure the time required to understand and validate the change, not just the time to generate it. If several patches affect a shared authentication helper, review their combined behavior instead of treating each local change as independent. That is where a superficially reasonable repair can introduce inconsistency.

Scan mode is consequential. The Multimodal guide says AI-powered detection does not support diff-aware scans, even though AI can analyze eligible findings from those scans. Plan a full-scan path for that detection capability rather than assuming every pull request receives the same analysis. The pilot should document which source of findings produced each result so that stakeholders understand the coverage they actually observed.

04 / PricingPrice contributors, products and AI capacity together

The pricing page lists Free, Teams and Enterprise offers. The paid unit is a contributor, defined there as someone who committed to a scanned private repository in the preceding 90 days. That differs from counting the people who log into the security dashboard. Establish the contributor inventory before comparing the advertised monthly rate with a budget.

The usage and billing guide specifies monthly AI allowances: 60 credits on Free, 20 per contributor on Team and 50 per contributor on Enterprise. Actions consume different amounts: analysis costs one credit per finding, Autofix costs 20, and AI detection scanning varies with scope. Estimate the intended mix of analysis and repairs rather than treating a credit as a completed fix. Confirm purchased-credit terms and any additional capacity before committing.

As illustrative arithmetic, one Teams product at the listed $30 rate for 20 contributors would imply $600 per month before any contract adjustments or additional products. That is not a quote. Adding another separately priced product changes the total, and a larger repository estate may change the plan required. The calculation is useful because it keeps the seat-like charge separate from the benefit of any particular AI action.

05 / DistinctionsContext can make security findings easier to use

A meaningful distinction is the ability to combine repeatable rules with explanations about the surrounding application. Rules create a stable starting point for a security policy; contextual analysis can make an individual result less costly to interpret. The point of evaluation is to discover where that combination works in the reader’s codebase. Vendor-wide accuracy or speed claims cannot answer that question for a different language, framework or internal architecture.

Semgrep’s secure guardrails approach emphasizes presenting selected findings within developers’ existing work and encouraging preferred secure patterns. That suggests a gradual rollout: start with a small set of well-understood, high-value rules, then expand after developers trust the findings. A flood of blocking comments can encourage people to search for bypasses; a clear finding with an appropriate repair can strengthen the workflow.

The separation between guidance and code changes is also useful. Sometimes the security issue needs a design decision, such as deciding which role should be allowed to call an operation. A generated patch cannot legitimately make that policy choice without an authoritative requirement. In other cases, the requirement is settled and the repair is repetitive. Semgrep’s different outputs let the team distinguish these cases rather than expecting every finding to end in the same automation.

06 / QuestionsSource access and AI mode need explicit decisions

The Multimodal privacy guide states that relevant source portions are processed by AI subprocessors. It also distinguishes supported provider configurations and notes that some bring-your-own routes are unavailable for AI-powered detection scans. A security team should approve the actual enabled feature and provider combination. Approval of a local scanner does not automatically cover a hosted AI analysis route.

Managed scanning separately requires repository read access and uses temporary clones that are deleted after a scan. That describes the scan environment, while AI processing introduces another data flow. Document both. Ask what evidence, findings and user feedback remain in the platform, and which integration permissions can create comments or changes. A narrowly scoped initial repository makes those boundaries easier to inspect.

Finally, test findings at the boundaries of support. Custom rules, community rules, full scans and diff-aware scans do not necessarily receive identical AI output. Record a case where guidance is absent or delayed and decide how the team proceeds. A reliable workflow should still produce an understandable security decision when an AI feature cannot contribute, rather than leaving a pull request in an unexplained state.

07 / DecisionChoose the deployment route before expanding coverage

Semgrep is worth a pilot when a team wants security findings that fit its repository workflow and is prepared to examine both detection and remediation quality. Start with the languages, scan mode and source-handling route that represent real development. Then use accepted fixes, justified suppressions and actual contributor counts to decide whether broader deployment makes sense. The free route can answer some questions, while specialized enterprise requirements need a scoped agreement.