Building Guardrails for Enterprise AI: Exploring the Databricks AI Governance Framework and Beyond

As enterprises, governments, and society writ large race to unlock the transformational value of AI, the risks, ranging from bias and security vulnerabilities to regulatory noncompliance, are growing just as quickly. Frameworks like the Databricks AI Governance Framework (DAGF) and standards like ISO 42001 are emerging to provide structure and uniformity of assessment, manage risk, and ultimately, build trust in AI systems. 

But how can enterprises translate frameworks and standards into practical, actionable guardrails and reduce friction between governance practitioners and AI stakeholders?

Join experts from Databricks, Schellman, and Trustible for an exploration of the Databricks AI Governance Framework, including its principles, architecture, and role in helping enterprises scale AI responsibly. We’ll examine how it aligns with emerging regulations and standards (like ISO 42001, NIST AI RMF, and the EU AI Act), and discuss practical considerations for implementing governance controls and assurance programs across the AI lifecycle, while creating a feedback loop between governance and compliance teams and AI internal stakeholders that can increase and accelerate safe AI adoption.

This 45-minute session is designed to equip technology, risk, and compliance leaders with insights they can apply to operationalize AI governance in their organizations.

Key Highlights:

• An In-Depth Look at the Databricks AI Governance Framework and Databricks AI Governance tools: Explore its components, objectives, and how it addresses the unique risks of enterprise AI adoption. Focus on how tools like Unity Catalog and MLFlow can help on the highly technical aspects of AI governance.
• Bridging Frameworks to Practice: How organizations can align the Databricks framework, and other standards such as ISO 42001, with other emerging global standards and regulatory obligations.
• Operational and Assurance Considerations: Practical insights into implementing governance controls, testing for compliance, and providing assurance over AI systems.
• Real-World Perspectives: Lessons from industry practitioners, auditors, and governance experts on avoiding common pitfalls and building resilient AI governance programs.

Learning Objectives: 

• Describe the Databricks AI Governance Framework and its approach to managing AI risk across the enterprise.
• Recognize how emerging standards and regulations intersect with framework-based governance strategies.
• Apply practical methods for operationalizing governance controls and assurance practices to mitigate AI risks effectively.