AI Governance for the Modern Enterprise
AI adoption is accelerating, but governance has not always kept pace. This paper outlines a practical model for responsible AI use with transparency, accountability and robustness.
Governance pillars
- Transparency – visibility into data, models and reasoning.
- Accountability – clear ownership of outcomes.
- Robustness – protection against biased or harmful outputs.
- Security – protecting data used by AI systems.
Operating model
Organizations should define AI review boards, model lifecycle controls, risk tiers and audit checkpoints so that AI can be used confidently across business units.