ai-development-governance

name	ai-development-governance
description	AI-augmented development controls, GitHub Copilot governance, LLM security, AI-generated code review per Hack23 Secure Development Policy
license	MIT

AI Development Governance Skill

Context

This skill applies when:

• Using GitHub Copilot or other AI coding assistants
• Reviewing AI-generated code
• Implementing AI-augmented development workflows
• Creating custom Copilot agents and skills
• Securing AI/LLM integrations
• Documenting AI usage in development process

This skill enforces Hack23 Secure Development Policy Section 🤖 for responsible AI-assisted development.

Rules

1. AI as Proposal Generator, Not Authority (Policy Section 🤖.1)

1. Human Review Required: All AI-generated code MUST be reviewed by human developer
2. Understanding Required: Developer MUST understand what AI-generated code does
3. No Blind Acceptance: Never merge AI suggestions without verification
4. Test AI Code: All AI-generated code requires tests with 80%+ coverage
5. Security Review: AI-generated security code requires additional security review

2. PR Review Requirements (Policy Section 🤖.2)

6. AI Disclosure: PRs with significant AI-generated code MUST be labeled
7. Code Ownership: Developer submitting PR owns the code, regardless of AI generation
8. Review Standards: Same review standards apply to AI-generated and human-written code
9. Security Validation: Security-critical AI code requires security team approval
10. No Bypass: AI assistance does NOT reduce review requirements

3. Curator-Agent as Tooling Change (Policy Section 🤖.3)

11. Agent Purpose: Custom agents provide specialized expertise, not replace human judgment
12. Skill Integration: Skills teach patterns, developers validate appropriateness
13. Agent Transparency: Document which agents/skills were used
14. Agent Limitations: Understand agent limitations and potential biases
15. Agent Testing: Test agent-generated code as rigorously as any other code

4. Security Requirements (Policy Section 🤖.4)

16. No Secrets in Prompts: Never include secrets, API keys, or credentials in AI prompts
17. Data Classification: Don't share classified or sensitive data with AI
18. Code Review: AI-generated security controls require expert security review
19. Vulnerability Scanning: Run CodeQL/SAST on all AI-generated code
20. ISMS Compliance: AI-generated code MUST comply with all ISMS policies

Examples

✅ Good Pattern: AI-Generated Code with Proper Review

/**
 * Search MEPs by country
 * 
 * AI-GENERATED: GitHub Copilot assisted with initial implementation
 * REVIEWED-BY: [e-mail]
 * SECURITY-REVIEWED-BY: [e-mail]
 * TEST-COVERAGE: 95%
 * 
 * ISMS Policy: Secure Development Policy Section 🤖.1-🤖.4
 * Evidence: https://github.com/Hack23/ISMS-PUBLIC/blob/main/Secure_Development_Policy.md#ai-augmented-development-controls
 * 
 * @param country - ISO 3166-1 alpha-2 country code
 * @returns Array of MEPs from specified country
 */
export async function searchMEPsByCountry(country: string): Promise<MEP[]> {
  // AI suggested this validation - REVIEWED AND ENHANCED
  const CountrySchema = z.string()
    .length(2, 'Country code must be 2 characters')
    .regex(/^[A-Z]{2}$/, 'Country code must be uppercase')
    .refine(
      (code) => EU_MEMBER_STATES.has(code),
      { message: 'Not a valid EU member state' }
    );
  
  // Human added: Additional security validation
  const validatedCountry = CountrySchema.parse(country);
  
  // AI suggested this caching logic - REVIEWED AND APPROVED
  const cacheKey = `meps:country:${validatedCountry}`;
  const cached = mepCache.get(cacheKey);
  if (cached) {
    auditLog.record({
      eventType: AuditEventType.CACHE_HIT,
      subject: cacheKey,
      outcome: 'success',
    });
    return cached;
  }
  
  // Human added: Rate limiting (AI didn't suggest this)
  await rateLimiter.waitForSlot();
  
  // AI suggested this API call - REVIEWED
  const response = await fetch(
    `https://data.europarl.europa.eu/api/v2/meps?country=${validatedCountry}`,
    {
      headers: {
        'Accept': 'application/json',
        'User-Agent': 'European-Parliament-MCP-Server/1.0',
      },
    }
  );
  
  // Human added: Comprehensive error handling
  if (!response.ok) {
    auditLog.recordAPIError(response.status, validatedCountry);
    throw new APIError(`EP API error: ${response.status}`);
  }
  
  const meps = await response.json();
  
  // AI suggested caching - REVIEWED AND APPROVED
  mepCache.set(cacheKey, meps);
  
  // Human added: GDPR audit logging
  auditLog.recordPersonalDataAccess(
    'system',
    `meps:country:${validatedCountry}`,
    'Country-based MEP search'
  );
  
  return meps;
}

// AI-GENERATED TESTS - REVIEWED AND ENHANCED
describe('searchMEPsByCountry', () => {
  it('should validate country code format', async () => {
    // AI suggested this test
    await expect(searchMEPsByCountry('USA')).rejects.toThrow('Not a valid EU member state');
  });
  
  it('should reject lowercase country codes', async () => {
    // Human added this test (AI missed it)
    await expect(searchMEPsByCountry('de')).rejects.toThrow('Country code must be uppercase');
  });
  
  it('should use cache on subsequent requests', async () => {
    // AI suggested this test - ENHANCED
    vi.mock('./api', () => ({
      fetchMEPs: vi.fn().mockResolvedValue([{ id: 1, country: 'DE' }])
    }));
    
    await searchMEPsByCountry('DE');
    await searchMEPsByCountry('DE');
    
    const apiMock = await import('./api');
    expect(apiMock.fetchMEPs).toHaveBeenCalledTimes(1);
  });
  
  it('should log GDPR access', async () => {
    // Human added (security-critical test)
    const logSpy = vi.spyOn(auditLog, 'recordPersonalDataAccess');
    
    await searchMEPsByCountry('DE');
    
    expect(logSpy).toHaveBeenCalledWith(
      'system',
      'meps:country:DE',
      'Country-based MEP search'
    );
  });
});

Pull Request Description:

## AI-Generated Code Disclosure

**AI Tool Used**: GitHub Copilot  
**AI Contribution**: ~60% (initial implementation, test structure)  
**Human Review**: All code reviewed, enhanced security, added GDPR logging  
**Security Review**: ✅ Approved by [e-mail]  
**Test Coverage**: 95% (4 passing tests)

### Human Enhancements
- Added EU member state validation
- Added rate limiting
- Enhanced error handling
- Added GDPR audit logging
- Added security-focused test case

### Review Checklist
- [x] Code understood and validated
- [x] Security controls verified
- [x] Tests pass with 80%+ coverage
- [x] ISMS compliance validated
- [x] No secrets or sensitive data
- [x] Documentation complete

Policy Reference: Secure Development Policy Section 🤖

✅ Good Pattern: Custom Copilot Agent with Governance

---
name: example-agent
description: Example agent with proper governance
tools: ["view", "edit", "create"]
---

# Example Agent

**AI Governance Notice**: This agent uses AI to generate code suggestions. All suggestions MUST be reviewed by human developers per [Secure Development Policy Section 🤖](https://github.com/Hack23/ISMS-PUBLIC/blob/main/Secure_Development_Policy.md#ai-augmented-development-controls).

## Core Expertise

You specialize in X, Y, Z.

## Rules

1. **Security First**: All code must follow security-by-design principles
2. **ISMS Compliance**: Reference appropriate ISMS policies
3. **Test Coverage**: Generate tests achieving 80%+ coverage
4. **Human Review**: Remind users to review all generated code
5. **Evidence Links**: Provide evidence links to policy compliance

## Remember

**ALWAYS:**
- ✅ Generate code that follows ISMS policies
- ✅ Include comprehensive tests
- ✅ Document security considerations
- ✅ Reference evidence in Hack23 repos
- ✅ Remind users to review AI-generated code

**NEVER:**
- ❌ Include secrets or credentials
- ❌ Skip input validation
- ❌ Generate code without tests
- ❌ Bypass security controls
- ❌ Claim AI-generated code is perfect

---

**Governance**: This agent's output requires human review per AI Development Governance policy.

✅ Good Pattern: AI Security Validation Checklist

# AI-Generated Code Security Review Checklist

**Project**: European Parliament MCP Server  
**PR**: #123  
**AI Tool**: GitHub Copilot  
**Date**: 2026-02-16

## Pre-Merge Security Validation

### Code Understanding
- [ ] Developer understands all AI-generated code
- [ ] Code purpose and logic documented
- [ ] Edge cases identified and handled

### Security Controls
- [ ] Input validation with Zod schemas
- [ ] No hardcoded secrets or credentials
- [ ] Error handling doesn't expose sensitive data
- [ ] Audit logging for security events
- [ ] Rate limiting implemented where needed

### ISMS Compliance
- [ ] Follows security-by-design principles
- [ ] Aligns with threat model
- [ ] Implements defense-in-depth
- [ ] Complies with GDPR (if handling personal data)
- [ ] References appropriate ISMS policies

### Testing
- [ ] Unit tests achieve 80%+ coverage
- [ ] Security test cases included
- [ ] Edge cases tested
- [ ] Error paths tested
- [ ] Integration tests passing

### Code Quality
- [ ] TypeScript strict mode compliant
- [ ] ESLint passing with no warnings
- [ ] Code follows project conventions
- [ ] Documentation complete (JSDoc)
- [ ] No TODO or FIXME comments

### Vulnerability Scanning
- [ ] CodeQL scan passing
- [ ] npm audit shows zero vulnerabilities
- [ ] Dependency licenses approved
- [ ] SAST tools report no issues

## Security Review Sign-Off

- [ ] Reviewed by: ___________________
- [ ] Date: ___________________
- [ ] Security concerns: None / [Document concerns]
- [ ] Approval: ✅ Approved / ❌ Requires changes

## Post-Merge Monitoring

- [ ] Monitor for runtime errors
- [ ] Track performance metrics
- [ ] Review audit logs for anomalies
- [ ] Schedule follow-up review in 30 days

Policy Reference: Secure Development Policy Section 🤖.2

✅ Good Pattern: AI-Generated Code Documentation

/**
 * AI-GENERATED CODE DOCUMENTATION
 * 
 * Generation Details:
 * - Tool: GitHub Copilot
 * - Generation Date: 2026-02-16
 * - Prompt: "Create a rate limiter for European Parliament API"
 * - AI Contribution: 70% (algorithm, structure)
 * - Human Contribution: 30% (security enhancements, tests)
 * 
 * Review Details:
 * - Reviewed By: [e-mail]
 * - Security Review: [e-mail]
 * - Review Date: 2026-02-16
 * - Review Notes: Enhanced with audit logging, added comprehensive tests
 * 
 * ISMS Compliance:
 * - Policy: Secure Development Policy Section 🤖
 * - Controls: Rate limiting (DoS prevention), Audit logging
 * - Evidence: https://github.com/Hack23/ISMS-PUBLIC/blob/main/Secure_Development_Policy.md
 * 
 * Test Coverage: 95%
 * Security Tests: 3 test cases
 * Performance Tests: 2 test cases
 */
export class EPAPIRateLimiter {
  // Implementation...
}

Anti-Patterns

❌ Bad: Blindly Accepting AI Code

// GitHub Copilot suggested this - looks good, merging!
export async function handleUserData(data: any) {
  return await processData(data);  // No validation!
}

Why: Violates AI Development Governance - no review, no validation, no tests

❌ Bad: Including Secrets in AI Prompts

Prompt to Copilot: "Create API client with API key abc123xyz456"

Why: Violates Policy Section 🤖.4 - Never share secrets with AI

❌ Bad: No AI Disclosure

# Pull Request

Changed the search function.

Why: Violates transparency - significant AI-generated code must be disclosed

Evidence Portfolio

Reference Implementations

1. European Parliament MCP Server

   • Custom Agents: https://github.com/Hack23/European-Parliament-MCP-Server/tree/main/.github/agents
   • AI Skills: https://github.com/Hack23/European-Parliament-MCP-Server/tree/main/.github/skills
   • Agent Guide: https://github.com/Hack23/European-Parliament-MCP-Server/blob/main/AGENTS_AND_SKILLS_GUIDE.md

2. Citizen Intelligence Agency (CIA)

   • AI Governance: https://github.com/Hack23/cia/blob/master/AI_DEVELOPMENT_GOVERNANCE.md

3. Black Trigram Game

   • Copilot Agents: https://github.com/Hack23/blacktrigram/tree/main/.github/agents

Policy Documents

• Secure Development Policy Section 🤖: https://github.com/Hack23/ISMS-PUBLIC/blob/main/Secure_Development_Policy.md#ai-augmented-development-controls
• AI Development Guidelines: Best practices for AI-assisted development

GitHub Copilot Integration

Custom Agents Best Practices

1. Clear Purpose: Each agent has specific expertise
2. Security Focus: Security controls in all agent instructions
3. Evidence Links: Reference Hack23 repos for patterns
4. Human Review Reminder: Always remind users to review
5. ISMS Alignment: All agents reference ISMS policies

Skills Best Practices

1. Pattern-Based: Teach patterns, not specific solutions
2. Auto-Activation: Skills activate based on context
3. Evidence-Backed: Include working examples from real repos
4. Compliance-Focused: Map patterns to ISMS policies
5. Test-Driven: Include testing patterns

AI Development Checklist

• AI tool disclosed in PR description
• AI-generated code percentage estimated
• All AI code reviewed and understood
• Security controls validated
• Tests achieve 80%+ coverage
• ISMS compliance verified
• No secrets in code or prompts
• Documentation includes AI disclosure
• Security review completed (if security-critical)
• CodeQL/SAST passing
• Appropriate ISMS policies referenced

ISMS Compliance

This skill enforces:

• AI-001: AI as proposal generator principle (human accepts or rejects)
• AI-002: PR-review requirements for AI-generated code
• AI-003: Custom-agent governance (definition, scope, minimal privilege)
• AI-004: AI security requirements (prompt-injection resistance, output filtering)
• AI-005: Provenance — every AI-assisted change traceable to prompt + reviewer

Policy References

Primary:

• AI Policy
• OWASP LLM Security Policy
• Secure Development Policy (Section 🤖 AI-Augmented Development Controls)

Related:

• Information Security Policy
• Change Management — AI-assisted change review
• Access Control Policy — Agent-token least privilege
• Privacy Policy — No personal data in training / prompt context
• Open Source Policy — Licence-clean AI code output
• Incident Response Plan — AI-driven incident triage