ai-governance

# AI Governance Skill ## 🔴 AI FIRST Quality Principle > **Apply the AI FIRST principle: never accept first-pass quality. Minimum 2 iterations. Read all output, improve every section. No shortcuts.** ## Purpose This skill provides governance guidelines for AI usage in the Riksdagsmonitor platform, including GitHub Copilot agent security, EU AI Act compliance, and responsible AI practices. It ensures AI-assisted development follows Hack23 ISMS policies and regulatory requirements. ## When to Use This Skill Apply this skill when: - ✅ Configuring or updating GitHub Copilot agent workflows - ✅ Integrating AI/ML models for political data analysis - ✅ Reviewing AI-generated code before merge - ✅ Assessing AI risk classification under EU AI Act - ✅ Implementing prompt engineering for data analysis - ✅ Auditing AI agent outputs for bias or accuracy Do NOT use for: - ❌ Standard code reviews without AI involvement - ❌ Manual data analysis without AI components - ❌ Infrastructure changes unrelated to AI services ## EU AI Act Classification ### Risk Assessment for CIA Platform ``` CIA Platform AI Usage Assessment │ ├─→ Political Data Analysis (NLP, trend detection) │ ├─ Risk Level: LIMITED RISK (Article 52) │ ├─ Requirement: Transparency obligations │ └─ Action: Disclose AI-generated analysis to users │ ├─→ GitHub Copilot Code Generation │ ├─ Risk Level: MINIMAL RISK │ ├─ Requirement: Voluntary codes of conduct │ └─ Action: Code review before merge, security scanning │ ├─→ Political Risk Scoring │ ├─ Risk Level: HIGH RISK (Annex III, Category 8) │ ├─ Requirement: Conformity assessment, human oversight │ └─ Action: Human review of all risk scores, audit trail │ └─→ Voter Behavior Prediction ├─ Risk Level: HIGH RISK ├─ Requirement: Transparency, fairness, accountability └─ Action: Bias testing, explainability, regular audits ``` ### Compliance Checklist - ✅ Document AI system purpose and intended use - ✅ Classify AI risk level per EU AI Act categories - ✅ Implement human oversight for high-risk AI outputs - ✅ Maintain audit trail of AI-generated decisions - ✅ Conduct bias and fairness assessments - ✅ Provide transparency notices for AI-generated content - ✅ Implement data governance for training datasets ## OWASP LLM Top 10 for CIA Platform ### LLM01: Prompt Injection **Risk:** Malicious input manipulating Copilot agent behavior. **Mitigation:** ```yaml # .github/copilot-instructions.md safeguards - Validate all agent outputs before committing - Never allow agents to modify security configurations - Restrict agent file access to source code only - Review agent-generated code with CodeQL scanning ``` ### LLM02: Insecure Output Handling **Risk:** AI-generated code containing vulnerabilities. **Mitigation:** - Run CodeQL on all AI-generated code changes - Apply OWASP secure code review checklist - Validate AI outputs against coding standards - Never trust AI-generated SQL or security logic without review ### LLM06: Sensitive Information Disclosure **Risk:** AI agents leaking secrets or sensitive political data. **Mitigation:** ```java // Never pass sensitive data to AI prompts // ✅ SECURE: Generic analysis request String prompt = "Analyze voting patterns for committee " + committeeId; // ❌ INSECURE: Including PII in prompts String prompt = "Analyze voting for " + politicianName + " SSN: " + ssn; ``` ### LLM09: Overreliance **Risk:** Blindly trusting AI-generated political analysis. **Mitigation:** - All AI analysis must include confidence scores - Human analyst review required for published insights - Cross-validate AI outputs with official data sources - Label AI-generated content clearly in the UI ## GitHub Copilot Agent Security ### Agent Configuration Best Practices ```yaml # Secure agent workflow permissions permissions: contents: read # Read-only by default pull-requests: write # Only for PR creation issues: write # Only for issue management actions: read # Read workflow status # Never grant: # - admin permissions # - security_events write # - secrets access ``` ### Agent Output Validation ``` Agent Output Validation Pipeline │ ├─ Step 1: Syntax validation (compile check) ├─ Step 2: Security scan (CodeQL, OWASP) ├─ Step 3: Test execution (unit + integration) ├─ Step 4: Code review (human or Copilot review) └─ Step 5: Merge approval (maintainer sign-off) ``` ## Responsible AI Practices ### Bias Prevention in Political Analysis - Test analysis algorithms across all 8 Swedish parties equally - Validate data representation for minority viewpoints - Audit sentiment analysis for political neutrality - Document model limitations and known biases ### Transparency Requirements - Label all AI-generated content in the Riksdagsmonitor platform UI - Provide methodology documentation for AI analysis - Enable users to access raw data behind AI insights - Maintain changelog of AI model updates ## ISMS Alignment | Control | Requirement | Implementation | |---------|------------|----------------| | ISO 27001 A.5.1 | Information security policies | AI governance policy | | ISO 27001 A.8.1 | Asset management | AI model inventory | | NIST CSF GV.OC | Organizational context | AI risk assessment | | CIS Control 16 | Application security | AI code review gates | | GDPR Art. 22 | Automated decision-making | Human oversight for scoring | ## References - [EU AI Act](https://artificialintelligenceact.eu/) - [OWASP LLM Top 10](https://owasp.org/www-project-top-10-for-large-language-model-applications/) - [Hack23 ISMS Secure Development Policy](https://github.com/Hack23/ISMS-PUBLIC/blob/main/Secure_Development_Policy.md) - [GitHub Copilot Trust Center](https://resources.github.com/copilot-trust-center/)