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Use AI security scanners as one layer in a repeatable review, not as an approval stamp. Start by inventorying the repository, then run source-code analysis, secret detection, and dependency analysis. Review alerts in pull requests, inspect the affected data flow, test any AI-proposed change, and run the checks again. A clean result means only that the configured tools found nothing within their supported languages, rules, files, and context for that run.
The review workflow at a glance
An effective review combines deterministic rules with AI assistance. The sequence below keeps the scanner focused on the repository’s actual stack and prevents an attractive AI summary from replacing engineering judgment.
- Inventory the repository. Record languages, frameworks, build systems, package manifests, generated code, test locations, and the CI provider.
- Choose supported analyzers. Confirm that the scanner handles the languages and frameworks in use and note whether compiled-language extraction requires a successful build.
- Enable three risk layers. Use source-code analysis for vulnerabilities and coding errors, secret scanning for credentials, and dependency analysis for vulnerable third-party packages.
- Run checks on pull requests. Put new findings beside the changed code so reviewers can decide before merging. Keep a scheduled or full-repository scan for issues outside the current diff when the product supports it.
- Triage every material alert. Read the rule, severity, affected path, and data-flow details. Decide whether the path is reachable and whether repository context changes the impact.
- Review and verify fixes. Treat an AI-generated patch as a proposal. Inspect the diff, run project tests and security checks, and review dependency changes before merging.
- Reassess coverage. Record what was not analyzed, including unsupported languages, failed builds, excluded files, pull-request-only features, and beta workflow limitations.
What an AI security scanner actually examines
“AI security scanner” describes several different capabilities rather than one universal test. Some products use AI to explain a rule-based alert or draft a fix. Others use an AI workflow to search for additional issues in a pull request. Traditional static analysis, secret detection, and dependency checks remain separate risk controls.
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Source-code analysis
Code scanning looks for potential vulnerabilities and coding errors in source. GitHub describes its code-scanning feature as analysis of repository code for security vulnerabilities and coding errors. GitHub CodeQL is one supported engine, and third-party tools can publish findings in SARIF format for display in the same code-scanning experience.
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Static analysis is conditional on its configuration. A rule must recognize the language and framework, the relevant files must be included, and the analysis must have enough context to follow a source-to-sink path. A result is therefore evidence about the analyzed program, not proof that every defect was found.
Secret scanning
Secret scanning searches for credentials such as tokens, keys, and other authentication material. It addresses accidental exposure, not general insecure logic. A repository can pass secret detection while still containing an exploitable authorization flaw, and it can contain a real credential in a location that a configured detector does not cover.
Dependency analysis
Dependency analysis evaluates third-party packages and their known vulnerabilities. It relies on package manifests, lockfiles, version resolution, and the advisories recognized by the service. A proposed code fix can also introduce or upgrade a dependency, so dependency review belongs in the same change review as an AI-generated patch.
AI explanation, triage, and remediation
AI may summarize an alert, suggest whether it is reachable, or generate a code change. These functions have different failure modes. A concise explanation can omit a dangerous data flow, and a plausible patch can alter behavior without removing the vulnerable path. Keep the underlying rule result and code diff visible to the human reviewer.
Prepare the repository before scanning
Map the technology stack
List the repository’s primary and secondary languages, frameworks, package managers, build tools, deployment targets, and generated artifacts. Note whether code is compiled, interpreted, vendored, or generated during the build. Include infrastructure and configuration files when the selected scanner supports them.
For each language, identify the normal build command and the test command used in CI. Compiled-language CodeQL extraction observes compiler activity during a build. If configuration or compilation fails, the database can be incomplete even though the workflow itself appears to have run.
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Check repository boundaries
Decide whether vendored libraries, generated code, examples, fixtures, and documentation should be analyzed. Excluding generated output can reduce noise, but an exclusion that hides executable code creates a coverage gap. Record exclusions in the repository configuration and explain why they are safe.
Confirm credentials and permissions
Make sure the CI identity can read the source, resolve dependencies, and upload results. For private repositories, verify that the selected GitHub code-security licensing is available. Do not grant a scanner broad production credentials merely to inspect code; use least-privilege tokens and replace any credential that appears in a finding.
Enable the three scanning layers
| Layer | Primary question | Typical evidence | What it does not prove |
|---|---|---|---|
| Source-code analysis | Does the program contain a recognizable insecure pattern or data flow? | Rule or query, affected lines, severity, source-to-sink path | That all languages, files, paths, or vulnerability classes were analyzed |
| Secret scanning | Does repository content contain a credential or token pattern? | Secret type, location, detection status, revocation guidance | That business logic is safe or that every undiscoverable secret was detected |
| Dependency analysis | Do declared third-party packages have known vulnerable versions? | Package, resolved version, advisory, affected range, upgrade option | That an upgrade is behavior-compatible or that unknown vulnerabilities do not exist |
Do not infer that an AI feature automatically performs all three jobs. GitHub documents generic secret detection, CodeQL code scanning, and AI-assisted features as distinct capabilities. Semgrep likewise documents source, dependency, and secrets scanning as separate parts of its ecosystem.
Set up source-code analysis correctly
Understand the CodeQL stages
CodeQL analysis has three conceptual stages: create a database from the repository, run queries against that database, and interpret the results. Extraction for compiled languages monitors ordinary compiler activity, so the analysis must be configured around the repository’s real build rather than a simplified command that skips important modules.
- Create the analysis configuration. Select the repository languages, query suites, build mode, paths, and event triggers supported by the GitHub workflow.
- Make the build reproducible. Install the same toolchain and dependencies used by normal CI. Resolve private packages through a narrowly scoped credential.
- Run extraction and queries. Watch for dependency-installation, compiler, or configuration errors. A failed build can reduce the extracted program model.
- Publish and inspect results. Confirm that findings include the expected branch, commit, language, and query suite before relying on the status.
Use third-party analyzers through the repository’s result channel
GitHub supports third-party code-scanning tools that emit SARIF findings. When selecting one, verify that its SARIF output preserves rule identifiers, severity, locations, and remediation details. A successful upload does not mean the analyzer covered every file; check the tool’s own logs and supported-language list.
Separate analysis failures from security alerts
A pull request can fail because a scanner found a vulnerability, because the analysis could not build the project, or because the workflow lacked permissions. These are different problems. Fix a build or configuration failure before interpreting a “no findings” result as meaningful.
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Make pull requests the main review point
Pull-request checks put a new alert next to the code that introduced it. GitHub’s documented code-scanning workflow can display annotations, severity, and data-flow paths, with configurable thresholds that determine when a check fails. Set thresholds deliberately: blocking every low-confidence warning can train contributors to ignore checks, while allowing a critical alert through defeats the control.
Review the changed path, not only the summary
Open the alert’s rule description and trace the data flow from input to sensitive operation. Compare the finding with the pull request’s intended behavior, tests, feature flags, and authentication boundaries. If the changed code calls a shared helper, inspect that helper and its callers rather than limiting the review to the highlighted line.
Keep a backlog for issues outside the diff
Pull-request analysis is excellent for preventing new risk but does not replace a repository-wide review. Schedule a full scan when the product supports it, and track accepted risks, false positives, and deferred fixes in the repository’s normal issue process.
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Triage an alert before changing code
Read the rule and severity
Identify the query or rule, the vulnerability class, the severity calculation, and any confidence indicator. Severity is a prioritization aid, not a substitute for understanding exploitability. A lower-severity issue on an internet-facing administrator path can deserve attention before a high-severity pattern in unreachable test code.
Validate reachability and context
Follow the reported source-to-sink path through sanitizers, authorization checks, framework routing, and configuration. Ask whether an attacker can control the input, whether the sink is reachable in production, and whether a compensating control changes the impact. Record the reasoning in the alert or pull request.
Handle false positives transparently
If the path is impossible or safely constrained, use the scanner’s supported suppression or feedback mechanism and state the technical reason. Do not dismiss an alert because an AI summary calls it harmless. A future refactor can invalidate an informal assumption that was never recorded.
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Respond to exposed secrets immediately
Revoke or rotate a detected credential first, then remove it from the repository and history according to the provider’s procedure. Cleaning the file without invalidating the credential leaves the exposure active. Check related logs and deployments for use of the leaked value.
Review AI-generated fixes safely
AI-generated remediation is a candidate patch, not a verified patch. GitHub’s Copilot Autofix supports only a subset of CodeQL queries, so the absence of a suggested fix does not indicate that an alert is unimportant.
- Read the proposed diff. Look for changed validation, authorization, error handling, encoding, cryptography, and dependency declarations.
- Confirm the vulnerable path is closed. Re-run the data-flow reasoning. A patch that suppresses an error message or moves a call may leave the dangerous sink reachable.
- Check behavior and compatibility. Run unit, integration, and end-to-end tests covering normal, boundary, malformed, and unauthorized inputs.
- Inspect new dependencies. Use dependency review to examine package identity, version, license policy, and known advisories before accepting an AI-proposed import or upgrade.
- Run CI security checks again. The resulting code, not the AI’s explanation, must satisfy the configured source, secret, and dependency checks.
- Ask for human ownership. The pull-request author or security reviewer should be able to explain why the change fixes the issue and what assumptions it relies on.
GitHub’s responsible-use guidance recommends CI testing before enabling Autofix and dependency review for risky dependency changes. Apply that discipline even when the patch is small or the explanation sounds certain.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Know the boundaries of AI scan features
GitHub AI Scan for pull requests
GitHub documents AI Scan as a public-preview, advisory feature for pull requests. The documented behavior is pull-request-only: it does not provide a full-repository scan, its findings do not appear as repository backlog alerts, and during the documented preview they cannot be used in rulesets to enforce merge requirements. It also does not run on fork or Dependabot pull requests.
The preview requires the applicable GitHub Advanced Security and Copilot licenses and uses AI credits. Those commercial and access terms can change, so verify the current account requirements before enabling it. Treat its output as additional review evidence rather than a replacement for CodeQL or rule-based scanning.
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Semgrep labels Agentic Workflows as beta. Its coverage depends on the selected workflow, language, framework, and context available to the agent. The documentation warns that a workflow might not analyze every file or detect every instance of a vulnerability class, and it recommends continuing rule-based scanning. AI findings can also include false positives.
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Beta behavior and supported coverage are subject to change. Pin the workflow configuration where possible, review release notes, and retain deterministic checks as the merge gate.
Compare scanners by the decisions that matter
| Decision | Questions to ask | Why it changes the result |
|---|---|---|
| Language and framework coverage | Are all production languages supported? Does compiled analysis require a successful build? Are framework-specific data flows modeled? | Unsupported syntax or a failed extraction can leave important code unanalyzed. |
| Risk categories | Does the product cover source flaws, secrets, dependencies, or only one category? | Separate controls catch different classes of risk; one green status cannot stand in for all three. |
| Execution model | Can it run locally, in GitHub Actions, in another CI provider, or as a hosted service? Can results be uploaded as SARIF? | Execution affects permissions, repeatability, data handling, and developer feedback. |
| Finding workflow | Are alerts annotated on pull requests? Are severity and data-flow paths visible? Can thresholds fail a check? | Reviewers need actionable context, and teams need a deliberate merge policy. |
| AI boundaries | Does AI find issues, explain alerts, generate fixes, or triage? Does it scan a pull request or the full repository? Can it block merges? | Marketing terms often hide materially different coverage and authority. |
| Access and cost | Are Actions minutes consumed? Is a private-repository license required? Are AI credits or paid tiers involved? | Unexpected limits can stop scheduled scans or leave private code without the intended feature. |
GitHub-centered setup
GitHub provides CodeQL, code-scanning alert workflows, SARIF ingestion for third-party tools, and pull-request annotations. It is a natural fit when the repository already uses GitHub Actions, but Actions usage, private-repository licensing, Advanced Security, Copilot access, and AI-credit requirements must be checked separately for the features you select.
Semgrep-centered setup
Semgrep documents a Community Edition and a hosted AppSec Platform with free and paid tiers, plus source, dependency, and secrets capabilities. Its CI integrations can fit repositories that need local or hosted execution. Keep the beta status and context-dependent coverage of Agentic Workflows distinct from the broader rule-based scanning products.
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Common failure modes and recovery steps
The scan passes after a build error
Inspect workflow logs for compiler, dependency, or configuration failures. Restore the normal build, confirm that expected files were extracted, and rerun the analysis. Do not treat the passing status as a clean result until extraction succeeds.
Alerts are missing from a pull request
Check that the workflow ran on the event type, the analyzer supports the changed language, the file is not excluded, and SARIF upload permissions are present. For AI Scan specifically, confirm that the change is an eligible pull request rather than a fork or Dependabot pull request.
The AI proposes a large unrelated refactor
Reject or split the patch. Ask for the smallest change that closes the vulnerable path, then review and test that focused diff. A broad refactor makes it harder to prove that the security property was restored.
The same alert keeps returning
Determine whether the fix changed the actual source-to-sink path, whether generated code recreates the pattern, or whether the suppression was applied to the wrong rule instance. Update the code or configuration only after identifying which condition persists.
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Use the lockfile and test results to identify the incompatible transitive change. Pin a supported version, apply the vendor’s migration guidance, or isolate the upgrade in a separate pull request. Do not keep a known-vulnerable version solely because an AI-generated upgrade was inconvenient.
Operational checklist for every repository
- Inventory languages, frameworks, manifests, build commands, generated files, and CI events.
- Confirm scanner support and document exclusions.
- Enable source, secret, and dependency checks as separate controls.
- Ensure compiled-language builds complete during extraction.
- Publish findings with rule, severity, location, and data-flow context.
- Set pull-request failure thresholds that match the team’s risk policy.
- Review reachability and repository context before suppressing an alert.
- Rotate exposed credentials before cleaning their files.
- Inspect every AI-generated diff and its dependency changes.
- Run tests and all relevant security checks after remediation.
- Schedule broader scans where pull-request-only features leave gaps.
- Record unsupported languages, skipped files, failed analyses, and accepted risks.
- Recheck licensing, preview status, beta status, AI-credit use, and pricing whenever the tooling changes.
What a trustworthy result looks like
A trustworthy review has an auditable chain: the repository’s stack was identified; the selected analyzers covered that stack; builds and uploads succeeded; source, secret, and dependency checks were run; each important alert was traced to code and context; proposed fixes were tested; and known gaps were recorded. The final decision is therefore not “the AI said it was safe.” It is “these checks examined this code under these conditions, these findings were resolved or accepted for stated reasons, and the remaining limitations are understood.”
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