AI Governance: Simplifying Compliance with GenAI & IBM watsonx.governance
Discover how leaders embed AI governance to ensure compliance, ethics & value. Learn key strategies in our expert-led webinar.
Date and time
Location
Online
About this event
- Event lasts 30 minutes
AI Governance: A Board-Level Priority
In today’s rapidly evolving AI landscape, ensuring responsible, compliant, and transparent use of artificial intelligence is now a board-level priority. Join us for a focused webinar that unpacks how enterprise leaders can embed AI governance frameworks that safeguard their organizations while driving significant business value.
You’ll gain valuable insights into the real-world challenges and opportunities of operationalizing AI governance across complex data ecosystems. We will explore how leading organizations are balancing regulatory compliance, ethical standards, and business agility, backed by the Six Principles of AI-Ready Data.Register for the Webinar!
Key Takeways
- Embedding AI Governance for Business Agility - Learn how to navigate the complexities of AI governance while ensuring regulatory compliance and business growth.
- The Six Principles of AI-Ready Data - Discover how to build and operationalize a solid data foundation that supports transparent and ethical AI decisions.
- Leveraging IBM Watsonx.governance for Responsible AI - See how platforms like IBM watsonx.governance automate governance processes, mitigate AI risks, and accelerate innovation.
Frequently asked questions
AI governance ensures generative AI is used ethically, transparently, and in compliance with regulations.
It’s IBM’s AI governance platform designed to monitor, manage, and enforce responsible AI across the lifecycle.
It automates tracking, documentation, risk detection, and policy enforcement for AI models to meet compliance needs.
GenAI models are less predictable and more opaque, requiring extra safeguards around content, bias, and explainability.
Automation reduces manual errors, scales compliance efforts, and speeds up responsible AI adoption.
Yes, it helps identify and mitigate bias and drift using built-in model monitoring and evaluation tools.
Highly regulated sectors like finance, healthcare, and government gain the most from governance automation.
It logs decisions, tracks data lineage, and offers explainability tools to understand how models function.
No — it also ensures ethical use, trust, accountability, and long-term business sustainability.
Yes, watsonx.governance can monitor both internal and third-party model usage, risks, and outputs.
Through continuous risk scoring, usage monitoring, and compliance controls across AI lifecycles.
No — when done right, it enables safer and faster AI deployment by removing uncertainty.
It supports frameworks like GDPR, EU AI Act, and industry-specific regulations (e.g., HIPAA, Basel).
Model provenance tracks a model’s origin, changes, and use — crucial for auditing and accountability.
Yes, it’s designed to integrate with MLOps, data platforms, and lifecycle management tools.
By assessing reduced risk exposure, faster model deployment, compliance savings, and increased trust.
Some onboarding is needed, but it’s built with user-friendly interfaces and guided workflows.
Transparency, accountability, fairness, security, privacy, and explainability — foundational for responsible AI.
Regularly — ideally continuously, especially after updates or major output changes.
You’ll learn how to simplify AI compliance using GenAI and IBM watsonx.governance, with real-world use cases and frameworks.