AI Governance refers to the overarching framework of policies, ethical standards, and operational procedures that an organization uses to ensure its artificial intelligence systems are developed, deployed, and managed responsibly throughout their entire lifecycle.

Unlike "AI Management," which focuses on the technical performance and efficiency of models, AI Governance focuses on accountability and risk control. It bridges the gap between ethical principles (like "do no harm") and practical engineering reality. Unless an organization establishes clear governance, it risks deploying "shadow AI" tools that expose the company to legal liability, data breaches, and reputational damage.

To implement effective AI governance, an organization must address the following core pillars:

• Accountability & Human Oversight: Clearly defining who owns the risk for an AI system's decisions. This ensures there is always a "human in the loop" or "human on the loop" for critical high-stakes decisions.
• Fairness & Bias Mitigation: Establishing protocols to test models for discriminatory patterns against protected groups (e.g., ensuring a hiring algorithm does not favor one gender over another) before deployment.
• Transparency & Explainability: Mandating that AI systems be documented and that their decision-making logic be interpretable, allowing stakeholders to understand why a specific outcome was reached.
• Security & Privacy: Ensuring models are robust against adversarial attacks and that the data used to train them complies with privacy laws like GDPR and CCPA.

Strategic Impact: AI Governance is no longer optional; it is a business imperative driven by regulations like the EU AI Act and the ISO 42001 standard. A strong governance framework transforms AI from a "black box" liability into a trusted asset, allowing organizations to innovate faster because they understand the guardrails within which they can safely operate.