Arun Pamulapati

Security data is among the most sensitive in the enterprise, yet many SIEM deployments lack consistent, fine-grained governance. Learn how Lakewatch leverages Unity Catalog to provide centralized governance across your entire security ecosystem. We will dive deep into the Lakewatch RBAC model, demonstrating how to manage Principals (Users, Groups, and Service Principals) across protected resources like Datasources, Detection Rules, and Notables. You’ll learn how to implement Lakewatch’s unique inherited permission model — where cases automatically inherit context from parent resources — to streamline investigations while maintaining strict "need-to-know" access. Takeaways include: implementing table, row, and column-level security for telemetry; defining Viewer, Creator, and Manager scopes at Workspace and Resource levels; practical patterns for SOC, cloud, and data teams to collaborate safely; sharing data with partners via Delta Sharing without compromising auditability or lineage.

Agentic AI systems—where LLM-powered agents plan, reason, and act across data and tools—are rapidly moving from proof-of-concept to mission-critical production. However, this shift introduces a new security surface: autonomous decision-making, dynamic tool use, and data access at scale. How do you innovate without compromising security, governance, or compliance? In this session, we explore how to build secure, enterprise-ready agentic systems on Databricks. Designed for practitioners and architects, we break down the unique risks of agentic workflows and map them to concrete controls. We will introduce the Databricks Agentic AI Security Framework developed with top researchers from OWASP, NIST, and Fortune 100 companies. Key Takeaways: - Reference architectures for deploying secure agentic AI at scale. - A framework to operationalize and explain your security posture to cyber teams. - Actionable tools, including a worksheet and live demo, to test your deployments and MCP security.

This session is repeated. AI is transforming industries, enhancing customer experiences and automating decisions. As organizations integrate AI into core operations, robust security is essential. The Databricks Security team collaborated with top cybersecurity researchers from OWASP, Gartner, NIST, HITRUST and Fortune 100 companies to evolve the Databricks AI Security Framework (DASF) to version 2.0. In this session, we’ll cover an AI security architecture using Unity Catalog, MLflow, egress controls, and AI gateway. Learn how security teams, AI practitioners and data engineers can secure AI applications on Databricks. Walk away with:• A reference architecture for securing AI applications• A worksheet with AI risks and controls mapped to industry standards like MITRE, OWASP, NIST and HITRUST• A DASF AI assistant tool to test your AI security

The Databricks Security team led a broad working group that significantly evolved the Databricks AI Security Framework (DASF) to its 2.0 version since its first release by closely collaborating with the top cyber security researchers at industry organizations such as OWASP, Gartner, NIST, HITRUST, FAIR Institute and several Fortune 100 companies to address the evolving risks and associated controls of AI systems in enterprises. Join us to to learn how The CLEVER GenAI pipeline, an AI-driven innovation in healthcare, processes over 1.5 million clinical notes daily to classify social determinants impacting veteran care while adhering to robust security measures like NIST 800-53 controls and by leveraging Databricks AI Security Framework. We will discuss robust AI security guidelines to help data and AI teams understand how to deploy their AI applications securely. This session will give a security framework for security teams, AI practitioners, data engineers and governance teams.