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Directories / Use Cases / Build a Security Audit Agent

Use case

Build a Security Audit Agent

Scan codebases for vulnerabilities, exposed secrets, and insecure patterns, producing a prioritized findings report with remediation guidance.

Static analysis tools catch known patterns but miss context-dependent issues: an API key that is technically in an environment variable but logged to stdout, or an auth check that is present on nine of ten endpoints. A security audit agent combines static scanning with reasoning over the full codebase to find those gaps. It produces a findings report ranked by exploitability, not just severity score. Security engineers and CTOs at early-stage companies use this to run structured audits before funding rounds or enterprise customer onboarding.

The stack

Each pick is a real entry on the index. Click any one for the full detail page.

  1. 1
    A Agents Analysis Engine

    Claude Code

    by Anthropic

    Why this: Claude Code can read entire directory trees, trace data flows across files, and reason about whether a pattern is actually exploitable in context. It produces findings with file paths, line numbers, and remediation steps.

    Full entry
  2. 2
    S Skills Rule Pack

    Security Review Skill

    by Anthropic

    Why this: The security review skill provides a structured set of vulnerability categories and prompt templates. It keeps the agent focused on what matters: injection, auth bypass, secret exposure, insecure deserialization.

    Full entry
  3. 3
    M MCP Code Access

    GitHub MCP Server

    by GitHub

    Why this: The GitHub MCP server lets the agent browse repository file trees and fetch file contents without cloning locally. This makes it practical to audit repositories on demand without provisioning compute.

    Full entry
  4. 4
    M MCP Dynamic Testing

    Playwright MCP

    by Microsoft

    Why this: For web applications, Playwright lets the agent probe live endpoints for common issues like missing auth headers or open redirects. This covers what static analysis cannot see.

    Full entry
  5. 5
    M MCP Findings Store

    Postgres MCP Server

    by Model Context Protocol (reference)

    Why this: Persisting findings across runs lets the agent track which issues were remediated and which regressed. This turns one-off audits into a continuous compliance record.

    Full entry
Why we picked this stack

Get this running with Enterprise DNA.

Enterprise DNA gives the audit agent its scope: which repositories to include, which severity thresholds trigger a Slack alert, and which teams own each finding. Secrets like GitHub tokens and Supabase credentials are pulled from EDNA's secrets store at runtime so they never touch the agent's codebase.

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Alternative stacks

Different angles on the same outcome.