As organizations rapidly adopt AI and Large Language Models (LLMs), establishing proper governance is critical. AI governance ensures responsible development, deployment, and use of AI systems while managing risks related to security, privacy, bias, and regulatory compliance.
Why AI Governance?
Data Privacy Risks: LLMs may inadvertently expose sensitive data through prompts or training data leakage
Security Concerns: Prompt injection, data poisoning, and model theft are emerging attack vectors
Regulatory Pressure: EU AI Act, NIST AI RMF, and industry guidelines require documented governance
Liability Issues: AI-generated outputs may create legal or reputational risks
Bias and Fairness: AI systems can perpetuate or amplify discriminatory patterns
Key Principles
Transparency: Document AI use cases, training data sources, and decision-making processes
Accountability: Assign clear ownership for AI systems and their outputs
Privacy by Design: Implement data minimization and anonymization in AI workflows
Human Oversight: Maintain human-in-the-loop for critical decisions
Continuous Monitoring: Track model performance, bias metrics, and security events
AI Risk Categories
Technical Risks: Model hallucinations, accuracy degradation, adversarial attacks
Data Risks: Training data leakage, PII exposure, intellectual property concerns
Operational Risks: Shadow AI usage, vendor lock-in, integration failures
Ethical Risks: Bias in outputs, lack of explainability, inappropriate use cases
Compliance Risks: Regulatory violations, audit failures, contractual breaches
Policy Framework
Establish comprehensive policies covering:
- Acceptable Use Policy: What AI tools are approved, prohibited use cases
- Data Classification: What data can be used with AI systems
- Vendor Assessment: Due diligence requirements for AI providers
- Model Inventory: Registry of all AI models in use with risk ratings
- Incident Response: Procedures for AI-related security events
Implementation Steps
Step 1: Conduct an AI inventory—identify all current and planned AI use cases
Step 2: Perform risk assessments for each use case using the EU AI Act risk tiers
Step 3: Develop policies and controls proportional to risk levels
Step 4: Implement technical controls (access management, logging, DLP)
Step 5: Train employees and establish governance committees
Ongoing Monitoring
Usage Analytics: Track who uses AI tools, what data is shared, and output patterns
Performance Metrics: Monitor accuracy, response quality, and error rates
Security Monitoring: Alert on suspicious prompts, data exfiltration attempts
Bias Audits: Regularly test outputs for discriminatory patterns