AI Governance

AI governance is the framework of policies, processes, and controls that ensure AI systems are developed and used responsibly, ethically, and in compliance with regulations.

AI governance addresses the unique risks and responsibilities of deploying artificial intelligence systems in organizations.

Key AI governance areas: - Accountability: Clear ownership for AI systems - Transparency: Explainability of AI decisions - Fairness: Bias detection and mitigation - Privacy: Data protection in AI training/inference - Security: Protecting AI systems from attacks - Compliance: Meeting regulatory requirements

Emerging regulations: - EU AI Act (risk-based classification) - ISO 42001 (AI management system) - NIST AI RMF (risk management framework) - SEC guidance on AI disclosures

Risk categories (EU AI Act): - Unacceptable risk (banned uses) - High risk (strict requirements) - Limited risk (transparency) - Minimal risk (no restrictions)

Why It Matters

The EU AI Act introduces fines up to €35 million or 7% of global revenue for non-compliant AI systems. Even outside Europe, enterprise customers and regulators increasingly demand evidence of responsible AI practices. Organizations without AI governance frameworks risk deploying biased or opaque systems that erode customer trust, trigger regulatory action, and create legal liability.

Key Points

ISO 42001 provides certifiable AI management system

EU AI Act creates tiered requirements by risk level

Must address bias, transparency, and accountability

AI risk assessments required for high-risk uses

Governance framework should cover full AI lifecycle

Applicable Compliance Frameworks

ISO 27001

Related Terms

ISO 42001

ISO/IEC 42001 is the international standard for AI management systems, providing a framework for organizations to responsibly develop and deploy artificial intelligence.

LLM Security

LLM security addresses the unique risks of deploying Large Language Models, including prompt injection, data leakage, and adversarial attacks on AI systems.

Model Risk Management

Model risk management is the oversight of risks arising from models making decisions, including AI/ML models, ensuring they perform as intended.