GLOSSARYAI Governance
What is it and why it is important?
What is AI Governance?
AI (artificial intelligence) Governance is the framework of policies, processes, and controls that guide the development, deployment, and monitoring of artificial intelligence systems. It ensures AI is used responsibly, ethically, and in compliance with legal and regulatory standards.
Why is AI Governance important?
Without effective governance, AI can introduce risks such as bias, privacy violations, and a lack of transparency in decision-making. Governance establishes accountability, ensures that AI systems are explainable and trustworthy, and aligns AI use with business objectives, societal expectations, and regulatory requirements.
What are the benefits of AI Governance?
AI Governance enables organizations to build trust in their AI systems, mitigate regulatory and reputational risks, enhance transparency, and ensure that models continue to perform accurately and fairly over time. It also supports more efficient AI adoption by defining clear guardrails and responsibilities.
How Precisely can help
Precisely helps organizations succeed with AI governance by helping them build AI-ready data and providing tools to oversee and control AI responsibly. Precisely takes a comprehensive approach by combining robust oversight with intelligent automation—enabling organizations to adapt and scale. Capabilities such as auto-generated glossary terms, asset descriptions, and dynamic workflows reduce manual effort and accelerate onboarding. At the same time, governance scores, observability alerts, and lineage tracking provide real-time visibility into model and data integrity.
Cutting Through the Chaos: The Case for Comprehensive AI Governance
Whether you’re focused on reducing risk, ensuring compliance, or enabling AI innovation responsibly, this eBook provides a practical framework for building governance that is both agile and enterprise-ready.
Opening the Black Box: Building Transparent AI Governance Frameworks
Effective AI governance frameworks are essential for managing the lifecycle of AI models, addressing transparency gaps, monitoring bias and drift, and adapting to evolving regulatory demands.