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The reliable method is layered, not one-click: use source-code analysis to find vulnerable logic, secret scanning to detect exposed credentials, and dependency analysis to identify risky third-party packages. Run those checks on pull requests, inspect the affected code and data flow, and treat every AI finding or proposed fix as a reviewable signal rather than proof of a vulnerability or a verified patch.
A clean result means only that the configured rules analyzed the files, languages, frameworks and changes they could reach in that run. It does not prove that the repository is secure. Coverage varies by tool, build success, workflow context and product status; several AI features remain advisory previews or betas.
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What an AI security scanner actually does
“AI security scanner” describes several different capabilities. Some products use machine-learning models to identify suspicious code or prioritize alerts. Others add an AI explanation or generate a patch after a conventional rule finds a problem. A few analyze a pull request for additional issues. These functions should not be treated as interchangeable.
GitHub defines code scanning as a way to analyze repository code for security vulnerabilities and coding errors. Its service can run CodeQL or third-party analyzers that upload results in SARIF format. Semgrep documents separate products for source-code, dependency and secrets scanning. Those distinctions matter because a source analyzer can miss a leaked token, while a secret scanner will not normally reason about an unsafe data flow.
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“Code scanning is a feature that you use to analyze the code in a GitHub repository to find security vulnerabilities and coding errors.” — GitHub Docs, “Code scanning.”
| Layer | Primary risk covered | Typical evidence | Important blind spot |
|---|---|---|---|
| Source-code analysis | Injection, authorization mistakes, unsafe APIs and coding errors | Rule or query, affected lines, severity and sometimes a data-flow path | Unsupported languages, frameworks, files or execution paths are not analyzed |
| Secret scanning | API keys, passwords, tokens and other credentials committed to the repository | Secret type, location and often provider-specific verification | Not every private or newly invented credential format is recognized; a deleted secret may still be exposed in history |
| Dependency analysis | Known vulnerabilities and risky versions in third-party packages | Manifest or lockfile, package version and advisory information | Cannot by itself determine whether vulnerable code is reachable or safely configured |
| AI triage or remediation | Explanations, prioritization and candidate code changes | Natural-language summary or a proposed diff | May produce false positives, omit findings or introduce behavior and dependency changes |
Use all applicable layers. Do not assume that an AI feature automatically includes the other three.
Map the repository before selecting a scanner
Start with an inventory. Scanner coverage is a property of the repository and its build, not just of the product name.
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- Languages: list production, test, generated and infrastructure code. A scanner may support one language but not another in the same repository.
- Frameworks and runtimes: record web frameworks, serialization libraries, authentication components and deployment targets because rules often depend on framework context.
- Build systems: note compilers, package managers, monorepo tooling, lockfiles and generated sources.
- CI provider: identify where pull-request checks can run and whether the service can upload SARIF or annotate changed lines.
- Repository shape: include submodules, vendored code, examples and native extensions. Decide which directories are in scope and document exclusions.
For CodeQL, compiled-language extraction observes normal compiler activity while creating its database. A failed or incomplete build can therefore reduce what is represented in the database. Treat an extraction or build failure as an analysis failure to fix, not as a clean security result.
Enable the three essential scan categories
Source-code analysis
Configure a ruleset that matches the repository languages and frameworks. CodeQL creates a database, runs queries against it and presents interpreted results. Third-party tools can also run in CI and upload SARIF findings to GitHub. Start with a broad default ruleset, then add framework-specific rules where the project uses custom sinks, sources or sanitizers.
Secret scanning
Scan new commits and the full history when credentials may have been committed previously. A finding should trigger immediate credential rotation and an investigation of access logs; deleting the line does not revoke the credential. Keep secret scanning separate from code scanning in policy and ownership so that an exposed token is handled as an incident rather than merely a code-quality warning.
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Dependency analysis
Analyze manifests and lockfiles for direct and transitive packages. Record the vulnerable version, fixed version (when the advisory supplies one), whether the package is reachable in production and what upgrade would change. Review dependency changes in pull requests: an AI-generated fix can silently add, remove or upgrade a package.
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Set up CodeQL or another source analyzer
1. Prepare a reproducible build
Make the same dependency installation and build steps available locally and in CI. Pin the runtime and package-manager versions used by the project. If generated code is required for compilation, generate it before extraction. Capture build logs so a reviewer can distinguish a missing build from an empty result.
2. Create the analysis database
CodeQL extraction records the code and, for compiled languages, the compiler activity observed during the build. Verify that every intended module was compiled and that no workspace was skipped because of an error. A database produced from a partial build can yield a partial alert set.
3. Run the queries
Choose the query suite appropriate for the language and risk profile. Queries identify patterns and, where supported, trace data from a source through transformations to a dangerous sink. Keep the query version and configuration under review so that a result can be reproduced on a later commit.
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Open the rule description, severity, affected path and data-flow steps. Confirm that the source is reachable in the deployed configuration and that the supposed sanitizer really constrains the input. A high-severity label is a prioritization aid, not a replacement for understanding the code.
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Make pull requests the main review surface
Run scans for every pull request and, separately, on a scheduled full-branch or default-branch build. Pull-request checks put new findings beside the change that introduced them. GitHub’s documented interface can show annotations, severity and data-flow paths, and can fail checks at configurable severity thresholds.
Keep three outcomes distinct:
- Security alert: a rule identified a potential vulnerability, secret or vulnerable package.
- Analysis failure: the scanner could not complete extraction, query execution or result upload.
- Build failure: the project did not compile or tests did not run.
Do not merge because the security check is green when the analysis itself failed. Configure branch protection so that required checks represent successful analysis, not merely a job that reached its final step.
Triage each finding before changing code
- Read the rule or query. Identify the vulnerability class, assumptions and known limitations.
- Trace the path. Follow user-controlled or external input to the reported sink. Check validation, encoding, authorization and error paths.
- Check repository context. A framework configuration, middleware layer or deployment boundary may alter reachability and impact.
- Classify the result. Mark it as confirmed, false positive, accepted risk or needs investigation. Record evidence, not just a label.
- Use supported suppression only when justified. Add a narrow suppression with an explanation and owner. Avoid blanket exclusions that hide future instances of the same issue.
- Recheck after surrounding changes. A refactor, feature flag or dependency update can change reachability even if the originally flagged lines are untouched.
Never dismiss an alert solely because an AI summary calls it harmless. AI explanations are useful for orientation, but the code, configuration and data flow decide the outcome.
Review AI-generated fixes as untrusted patches
Autofix systems generate a candidate change; they do not certify that the change is safe. GitHub documents that Copilot Autofix supports only a subset of CodeQL queries. A query without Autofix support may still have a valid remediation, but the developer must write it.
Inspect the proposed diff
- Does it remove the vulnerable path, or merely suppress the alert?
- Does validation occur before the dangerous operation and cover every entry point?
- Does the change preserve authorization, error handling, performance and compatibility?
- Did it alter a dependency, lockfile, configuration file or generated source?
- Could an attacker bypass the new check through another encoding, route or API?
Test before merging
Run the project’s unit, integration and security tests. Add a regression test that demonstrates the original exploit path and fails without the fix. Rerun the relevant scanner and confirm that the alert is gone for the right reason. GitHub’s responsible-use guidance recommends testing in CI before enabling Autofix and using dependency review to detect risky package changes introduced by a proposed remediation.
Keep human ownership
Require a maintainer who understands the affected component to approve the patch. Store the AI-generated diff in the normal code-review record, including the reviewer’s reasoning and test results. If the fix is uncertain, revert the candidate and open a focused issue rather than merging a speculative change.
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Understand the limits of AI-specific features
GitHub AI Scan for pull requests
GitHub documents AI Scan as a public-preview, advisory feature that analyzes pull requests. The documented implementation does not provide a full-repository scan, does not create the same repository backlog alerts as ordinary code scanning, and cannot be used in rulesets to enforce merge requirements during that preview. It also does not run on fork or Dependabot pull requests.
The preview requires the applicable GitHub Advanced Security and Copilot licenses and consumes AI credits. Those requirements and the preview status are product terms that can change; verify them in the current documentation before budgeting or promising enforcement.
“As with any AI-based tool, AI Scan findings may include false positives.” — GitHub Docs, “AI Scan for pull requests.”
Semgrep Agentic Workflows
Semgrep labels Agentic Workflows as beta. Its workflow can investigate and explain issues, but coverage depends on the selected workflow, language, framework and context available to the agent. Semgrep explicitly warns that a workflow might not analyze every file or detect every instance of a vulnerability class. Continue running deterministic, rule-based scans even when an agentic workflow is enabled.
Vendor descriptions of coverage, including counts of supported weakness classes, are changing product scope rather than independent evidence of detection effectiveness. Use them to check whether your stack is eligible, not to estimate the probability that the repository is secure.
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Compare scanners using the questions that affect coverage
| Decision factor | Questions to answer | Why it changes the result |
|---|---|---|
| Language and framework support | Are all production languages supported? Are framework-specific rules available? Is a successful compiled build required? | Unsupported or unbuilt code cannot contribute findings. |
| Risk categories | Does the service cover source flaws, secrets and dependencies, or only one category? | Different scanners see different failure modes. |
| Execution model | Can it run locally, in GitHub Actions or another CI system? Can it upload SARIF? | Developers need fast feedback while security teams need centralized history. |
| Finding workflow | Are pull-request annotations, severity controls, data-flow paths and suppressions available? | Good presentation reduces triage time and makes ownership explicit. |
| AI boundary | Does AI find issues, explain alerts, generate fixes or triage results? Does it scan a pull request or the full repository? | “AI-powered” does not specify what is actually analyzed or changed. |
| Enforcement | Can findings fail a check or participate in branch rules? Are preview findings advisory only? | An advisory signal cannot substitute for a required security gate. |
| Cost and access | What Actions minutes, private-repository licenses, AI credits and service tiers apply? | Usage and licensing can determine whether a design works at your scale. |
Semgrep documents both a Community Edition and a hosted platform with free and paid tiers. GitHub code scanning consumes Actions minutes, and the current documentation states that private-repository use requires a GitHub Code Security license. Confirm current terms for your organization and region before adoption.
A practical end-to-end review procedure
- Inventory the repository. Write down languages, frameworks, build commands, manifests, lockfiles, generated directories and CI entry points.
- Define scope. Decide whether examples, tests, vendored code and history are included. Document exclusions and their rationale.
- Select complementary tools. Choose a source analyzer with stack coverage, a secret scanner and dependency analysis. Add an AI feature only after its scan boundary and licensing are understood.
- Make the build reproducible. Ensure dependencies install and compiled targets build in CI. Treat extraction errors as failures.
- Run a baseline scan. Record existing findings, tool versions, rulesets and scan date. Do not block all development until old findings are triaged; establish a plan for reducing the backlog.
- Enable pull-request checks. Annotate changed lines, set severity thresholds and require successful analysis. Keep a scheduled default-branch scan for issues outside current pull requests.
- Triage new alerts. Inspect rule logic, data flow, reachability and deployment context. Assign an owner and due date or document a narrowly scoped suppression.
- Review AI proposals. Read every changed line, inspect dependency modifications and add or update regression tests.
- Rerun all affected checks. Confirm source analysis, secret scanning, dependency analysis, unit tests and integration tests pass after the change.
- Audit coverage periodically. Revisit language support, build failures, excluded paths, preview limitations and license terms whenever the repository or scanner changes.
Troubleshoot the results readers commonly misinterpret
| Symptom | Likely cause | Correct response |
|---|---|---|
| No alerts in a large compiled project | Extraction observed no successful compiler activity or only a subset of targets | Fix the build, inspect extraction logs and rerun; do not interpret an empty database as safety. |
| A pull request is green but a full-branch scan later finds issues | The issue is outside changed lines or the PR feature has narrower coverage | Keep scheduled repository scans and triage the alert independently. |
| An AI explanation says “false positive” | The model inferred context incorrectly or lacks configuration details | Trace the data flow yourself and document evidence before suppressing. |
| An Autofix removes an alert but tests fail | The candidate patch changed behavior or handled only one path | Revert or rewrite the patch, add a regression test and rerun analysis. |
| A dependency fix adds an unexpected package | The proposed remediation changed the dependency graph | Review the manifest and lockfile, run dependency analysis and approve only an intentional change. |
| Fork or Dependabot pull requests show no AI findings | Documented AI Scan preview exclusions | Run ordinary code, secret and dependency checks for those contributions. |
| Agentic workflow reports partial coverage | Language, framework, workflow or context is outside the agent’s effective scope | Use deterministic rules and targeted manual review for the uncovered areas. |
Operate scanners as part of security governance
Keep scan configuration, query packs and workflow definitions under version control. Record the tool and ruleset version with each report so that a changed result can be explained. Assign ownership for alerts by component rather than sending every finding to a generic security queue.
Separate preventive gates from discovery. A pull-request check can prevent a newly introduced high-severity issue, while a scheduled scan discovers vulnerabilities in unchanged code or newly disclosed dependency advisories. Neither replaces incident response for exposed secrets.
Review preview and beta features quarterly. Public-preview behavior, supported languages, AI-credit consumption, licensing and enforcement options can change without matching the assumptions in an old workflow. Reconfirm those details before renewing a service or making a compliance claim.
What a “clean” result means
A clean result is bounded evidence: the selected tools completed, the configured rules ran, and no issue matched within the analyzed coverage. It says nothing about unsupported files, failed builds, unrecognized secrets, undiscovered dependency vulnerabilities, logic flaws outside the rules, or vulnerabilities introduced after the scan. Semgrep’s documentation states that a workflow might not analyze every file or every instance of a vulnerability class; GitHub likewise warns that AI findings can include false positives.
Use AI to accelerate investigation and propose changes, then apply engineering judgment, tests and repeatable scans. The strongest review process is therefore layered, pull-request aware and explicit about what it did not analyze.
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