AI Security Auditor

Overview

The AI Security Auditor is an automated workflow for performing comprehensive security scans and vulnerability assessments on codebases and infrastructure. It combines static analysis, dependency checking, configuration auditing, and secret detection to identify security risks.

Security audits are critical but time-consuming. This workflow automates the initial scan, prioritizes findings, and provides actionable remediation suggestions, enabling security teams to focus on high-impact issues.

Difficulty

Hard — Requires security expertise to interpret results and validate findings.

Tools Required

Tool	Purpose
LangGraph	Multi-agent orchestration with state management
Claude	Security analysis and code review
GitHub	Repository access and issue creation
Sentry MCP	Error tracking and vulnerability correlation
Filesystem MCP	Local file scanning
Git MCP	Git history analysis for security changes

Workflow Steps

Step 1: Scanner Agent

The Scanner Agent performs static code analysis:

• Run SAST (Static Application Security Testing) tools
• Check for common vulnerability patterns (OWASP Top 10)
• Analyze input validation and sanitization
• Check for insecure coding practices
• Identify potential injection vulnerabilities

# Example: Run static analysis
scanner_result = scanner_agent.run("""
Perform a comprehensive security scan of this codebase:

1. Check for SQL injection vulnerabilities
2. Identify XSS vulnerabilities
3. Find insecure deserialization
4. Check for broken authentication
5. Identify sensitive data exposure
6. Check for security misconfigurations

Report all findings with file, line number, and severity.
""")

Step 2: Dependency Agent

The Dependency Agent checks for vulnerable packages:

• Scan package.json, requirements.txt, etc.
• Check against vulnerability databases (CVE, Snyk, GitHub Advisory)
• Identify outdated packages with known vulnerabilities
• Check for transitive dependencies
• Provide upgrade recommendations

# Example: Check dependencies
dependency_report = dependency_agent.run("""
Analyze all dependencies in this project:

1. List all direct and transitive dependencies
2. Check each against known vulnerability databases
3. Identify outdated packages
4. Provide safe upgrade paths
5. Flag any packages with critical vulnerabilities

Include CVE IDs and severity for each vulnerability.
""")

Step 3: Configuration Agent

The Configuration Agent audits infrastructure and config files:

• Check cloud configuration (AWS, GCP, Azure)
• Audit Dockerfiles and container configs
• Review CI/CD pipeline security
• Check environment variable handling
• Identify insecure defaults

# Example: Audit configurations
config_report = config_agent.run("""
Audit all configuration files for security issues:

1. Cloud IAM policies and permissions
2. Docker container security
3. CI/CD pipeline secrets handling
4. Environment configuration
5. Network and firewall rules
6. SSL/TLS configuration

Flag any overly permissive settings or exposed secrets.
""")

Step 4: Secret Detector Agent

The Secret Detector Agent scans for exposed credentials:

• Scan for API keys, tokens, passwords
• Check for private keys and certificates
• Identify database connection strings
• Find AWS/cloud credentials
• Check git history for leaked secrets

# Example: Detect secrets
secrets_report = secret_detector_agent.run("""
Scan for exposed secrets and credentials:

1. API keys and tokens (hardcoded and in env files)
2. Private keys and certificates
3. Database connection strings
4. Cloud provider credentials
5. Check git history for previously committed secrets

Use pattern matching and entropy analysis to detect secrets.
""")

Step 5: Risk Analyzer Agent

The Risk Analyzer Agent prioritizes findings:

• Assign severity scores (Critical, High, Medium, Low)
• Consider exploitability and impact
• Factor in business context
• Prioritize by risk level
• Create remediation timeline

# Example: Prioritize findings
risk_report = risk_agent.run("""
Prioritize all security findings by risk:

1. Assign severity (Critical/High/Medium/Low)
2. Consider exploitability and business impact
3. Identify which findings are actionable now
4. Create a remediation priority list
5. Estimate effort for each fix

Use CVSS scoring where applicable.
""")

Step 6: Report Generator Agent

The Report Generator Agent creates a comprehensive report:

• Executive summary for leadership
• Technical details for developers
• Remediation steps for each finding
• References to security best practices
• Actionable next steps

# Example: Generate report
report = report_agent.run(f"""
Create a comprehensive security audit report:

Findings: {all_findings}

Include:
1. Executive summary (1 page max)
2. Detailed findings with severity
3. Remediation steps for each issue
4. References to OWASP/CWE
5. Recommended timeline for fixes
6. Risk score and trend analysis
""")

Step 7: Remediation Agent

The Remediation Agent suggests fixes:

• Generate code patches for vulnerabilities
• Provide configuration fixes
• Suggest dependency upgrades
• Create PR-ready fixes
• Document the fix rationale

# Example: Generate remediation
remediation = remediation_agent.run(f"""
Generate remediation suggestions for each finding:

For each vulnerability:
1. Explain the issue
2. Provide a code fix or configuration change
3. Reference security best practices
4. Note any breaking changes
5. Suggest testing approach

Focus on Critical and High severity items first.
""")

Example Usage

Full Security Audit

# Initialize auditor
auditor = SecurityAuditorWorkflow(
    repo_path="./my-app",
    cloud_config_path="./terraform/",
    cve_database="snyk",
)

# Run full audit
report = auditor.run(
    include_secrets=True,
    include_dependencies=True,
    include_config=True,
    severity_threshold="medium",
)

# Save report
report.save("security-audit-report.md")
report.save_json("security-audit-findings.json")

CI/CD Integration

# Run as part of CI pipeline
def run_security_check():
    auditor = SecurityAuditorWorkflow(repo_path=".")
    report = auditor.run(severity_threshold="high")

    # Fail CI if critical issues found
    if report.critical_count > 0:
        raise SecurityAuditFailedError(
            f"Found {report.critical_count} critical vulnerabilities"
        )

    return report

Pros

• ✅ Comprehensive Coverage: Multiple analysis types
• ✅ Automated Scanning: Reduces manual effort
• ✅ Prioritized Findings: Focus on what matters
• ✅ Actionable Remediation: Ready-to-use fixes
• ✅ CI/CD Integration: Automated security checks
• ✅ Historical Tracking: Track security over time

Cons

• ❌ False Positives: May report non-issues
• ❌ Complex Setup: Requires multiple tools
• ❌ Expert Review Needed: Human validation required
• ❌ Performance: Large codebases take time
• ❌ Coverage Gaps: May miss novel vulnerabilities

When to Use

Use this workflow when:

• Performing regular security audits
• Onboarding new codebases
• Before major releases
• After security incidents
• For compliance requirements

Avoid this workflow when:

• You need real-time protection (use WAF/SIEM)
• The codebase is very small and well-understood
• You lack security expertise to interpret results
• You need 100% accuracy (no automated tool does)

Resources

• OWASP Top 10
• CWE/SANS Top 25
• Snyk Vulnerability Database
• GitHub Security Advisories
• SAST Tools Comparison