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Choose an AI security assistant by the job you need it to do, then test it against your own alerts and security gates. Triage, remediation advice, suggested patches, and an agent that edits a repository are different capabilities with different risks. No neutral head-to-head evaluation establishes one vendor as best; use a controlled pilot and verify current product availability, privacy terms, and costs before buying.

What do you need the assistant to do?

Start by naming the work the team wants to improve. “AI security assistant” can mean anything from an explanation of a scanner finding to an agent that changes code and opens a pull request. A tool that performs one of these tasks should not be assumed to perform the others.

Capability What it does What to verify
Finding triage Helps interpret a scanner alert, assess context, or investigate whether it may be a false positive. Whether it explains the relevant code and evidence, communicates uncertainty, and supports the scanners and finding types you use.
Code explanation or remediation advice Explains a vulnerability or suggests how a developer might address it. Whether the advice fits your codebase and preserves intended behavior; a chat response alone is not comprehensive security analysis.
Suggested patch Produces a proposed code change for a finding, usually for a developer to review. Whether the patch addresses the root cause, is minimal, and is clearly presented as a proposal requiring review.
Repository agent Can inspect code, make changes, run validations, and potentially open a pull request. Exactly which files, tools, commands, and network resources it can access, and which actions require human approval.

These categories can overlap, but their boundaries matter in procurement: an explanation is not a patch, and an opened pull request is not an accepted or verified fix.

Which capabilities are documented, and what do they establish?

Scanner-linked suggestions

GitHub documents Copilot Autofix for code-scanning alerts. It generates a proposed code change with a natural-language explanation. GitHub’s application card describes the inputs as CodeQL alert data in SARIF format, surrounding code, and query help text. It lists fix generation for a subset of queries in the default and security-extended suites across C#, C/C++, Go, Java/Kotlin, Swift, JavaScript/TypeScript, Python, Ruby, and Rust. These are documented capabilities, not proof of comprehensive coverage or superiority over another product.

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GitHub’s alert-resolution documentation also describes a separate workflow in which assigning an alert launches a Copilot cloud-agent session to explore a codebase, generate and validate a fix, and open a pull request. That documentation calls the feature a public preview, says it uses AI credits, and characterizes validation as best effort. Preview status, availability, and commercial details can change; confirm the current terms and eligibility directly with the vendor.

Examples in OWASP guidance

OWASP’s DevSecOps guideline names Semgrep Assistant, Snyk DeepCode AI, and GitHub Copilot Autofix as examples of tools that suggest scanner-finding remediations. It also describes possible defensive uses such as false-positive analysis, threat-modeling assistance, and security-focused pull-request review. These examples are not an independent ranking or a finding that every named product supports every use case.

Why chat is not a scanner replacement

GitHub cautions that Copilot Chat may help with some common vulnerabilities but should not be relied on for comprehensive security analysis, and points to code scanning for more thorough coverage. Treat a general-purpose chat assistant as supplementary help, not a substitute for your scanning, testing, or review process.

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How should you compare candidates?

Give each candidate the same tasks and record evidence rather than relying on a polished demonstration. Use these questions to shape the evaluation and procurement discussion.

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Area Questions to ask
Task fit Does the product support the specific work you need: triage, remediation advice, proposed patches, or repository actions such as opening a pull request?
Finding quality Does it identify affected code, explain its assumptions, express uncertainty, and show why a proposed change addresses the root cause? How does it handle false positives?
Coverage Which languages, repositories, scanner formats, finding types, and query suites are supported? What is explicitly out of scope?
Workflow Does it fit your source control, code scanning, pull-request, CI, ticketing, and review processes? Can security staff retain approval authority?
Safety controls Can you limit file and tool access, sandbox execution, restrict network egress, and inspect actions before merge? How does it handle untrusted issue and pull-request content?
Data and privacy What code, prompts, secrets, and telemetry are sent or retained? Is data used for training? Are enterprise or self-hosted deployment options and contractual privacy commitments available?
Verification Can each change pass your existing tests, code scanning, dependency review, and security review? Can you measure rework and reversals?
Cost and operations What license charges, usage credits, infrastructure, integration, review, and maintenance costs apply? Confirm current commercial terms rather than extrapolating from a product description.

How do you run a useful pilot?

Use historical findings or safely reproducible alerts that reflect the team’s important languages and vulnerability classes. The pilot is an evaluation method, not a benchmark unless you publish a controlled, comparable study.

  1. Choose representative alerts. Include different severity levels, vulnerability classes, and code contexts across important repositories. Use the same alert set and task instructions for every candidate.
  2. Define the expected work product. Decide whether the candidate should explain a finding, assess a false positive, suggest a patch, or make a repository change. Avoid judging a triage-only tool as if it were a patch agent.
  3. Record evidence for each result. Assess whether the finding is understood correctly, uncertainty is handled honestly, the proposed patch is effective and minimal, and intended behavior is preserved.
  4. Run the normal gates. Apply existing tests and security tools to proposed changes. Require reviewer assessment of root-cause correction and regression risk; a model’s own validation does not replace these checks.
  5. Measure team impact and cost. Track false-positive disposition, reviewer time, rework, reversals, coverage, and usage costs. Compare like with like across candidates.
  6. Test the intended operating boundary. If the deployment will read issue or pull-request content, include a safe test with untrusted content and verify that the agent’s permissions and sandbox behave as intended.

GitHub describes operational measures such as resolution rate, token efficiency, latency, reliability, and spot-checking successful suggestions for its own system. These can inform what you measure in a pilot, but vendor-reported measures are not a neutral cross-product comparison.

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How do you let an agent work safely on security alerts?

An agent that reads repository files, issue bodies, pull-request comments, documentation, or tool output may encounter indirect prompt injection: untrusted content designed to influence its behavior. OWASP’s guidance supports treating that content as untrusted and controlling the agent’s context, tools, and runtime.

  • Limit context. Give the agent only the files and information needed for the task; inspect unexpected changes, especially after it has consumed external or user-submitted content.
  • Apply least privilege. Restrict repository permissions, commands, and connected tools to what the task requires. Keep approval gates for consequential actions.
  • Audit integrations. Review connected tools and MCP servers; allowlist approved servers and commands, pin definitions where feasible, and validate tool arguments.
  • Sandbox execution. Use restricted shells or ephemeral workspaces. Block access to credential stores and sensitive directories, and limit network egress where it is not needed.
  • Keep human review and security gates. Review the code before acceptance and run normal tests and security tooling. OWASP Top 10:2025 advises thorough review of AI-assisted code, including with security tools such as static analysis.

OWASP Top 10:2025 puts the review obligation plainly: “You should be able to read and fully understand all code you submit, even if it is written by an AI or copied from an online forum.” It also advises reviewing AI-assisted code for vulnerabilities, ideally both directly and with security tooling.

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What should procurement establish about data and suppliers?

Get written, current answers from each vendor rather than inferring privacy or security terms from a product feature page. OWASP recommends defining data categories and approved tools; its guidance does not establish the contractual terms of the named vendors.

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  • Identify which source code, prompts, secrets, personal information, and telemetry may leave your environment, and what must never be shared with a third party.
  • Confirm retention, deletion, access, training use, and the handling of data in logs and support workflows.
  • Establish whether enterprise terms, contractual privacy commitments, or a self-hosted deployment are available for the specific product and plan under consideration.
  • Document approved tools, permitted use cases, review requirements, and the people responsible for approving changes.

For supplier assessment, NIST’s software supply-chain acquisition guidance recommends considering vulnerability disclosure and coordinated disclosure processes, SBOM and vulnerability-database integration, and whether suppliers have defined product security incident-response or research teams. These are supplier questions, not an AI-assistant certification.

For structured verification requirements, OWASP AISVS can support procurement as well as design, assessment, and testing. OWASP reports that AISVS 1.0 was released in June 2026 and contains 191 requirements across 12 chapters; that is a description of the standard’s size, not a measure of a product’s effectiveness.

How do you make the final decision?

Select the candidate that performs the specific task your team needs within acceptable security and operational boundaries, based on your pilot evidence. Do not infer a universal winner from vendor feature descriptions or vendor-reported operational metrics. Before contracting, confirm the current feature availability for your plan, privacy and retention commitments, deployment choices, and full cost. Keep code review and security testing in the acceptance path: an AI response or passing agent validation is not proof that a vulnerability has been fixed.

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