Join MuleSoft and Informatica solution experts for a comprehensive hands-on workshop focused on the risk, security and governance pillars of enterprise AI strategy. Learn how to create an AI-ready data layer to power distributed Agent networks running on Agent Fabric, a trusted and robust Enterprise AI control plane that delivers confident AI business outcomes. 

As organizations transition to autonomous agentic workflows, the need for trusted context  through AI-ready data is critical. This workshop will help participants move beyond theoretical design and through hands-on and instructor-led labs, attendees will configure and deploy data, model and agent policies, enforce operational guardrails and create end-to-end lineage visualizations for auditability and compliance. You will gain practical experience in establishing an improved trust posture, based on a unified AI governance strategy that spans your growing agentic ecosystem.

• Agent Fabric: Runtime AI control plan for trusted operations of Agents, MCP tools, and Models.
• Informatica Cloud Data and Governance Catalog: Discover, classify, and govern data, context, and models.

You'll leave knowing how the controls fit together, how trusted enterprise context drives safe and compliant outcomes for scalable AI deployments.

Core Learning Objectives

• Data Governance: Discover, profile, and classify enterprise data, apply quality rules, ownership, and security policies for sensitive data.
• Model Governance: Discover and catalog models with training-data lineage, model dependency visualization, versioning and model lifecycle governance.

• Centralized Agent & Tool Governance: Establish robust lifecycles for discovering, registering, and governing autonomous agents and Model Context Protocol (MCP) tools across the enterprise.

• Zero-Trust Agent Identity & Access Control: Implement dynamic, Attribute-Based Access Control (ABAC) and On-Behalf-Of (OBO) policies to ensure agents operate with verifiable identities and strictly scoped permissions.
  
• AI Gateway Policy & Cost Optimization: Deploy effective guardrails to enforce security policies, manage payload optimization, and optimize LLM token consumption costs.
  
• End-to-End Lineage & Explainability: Build transparent, visually traceable execution lineage graphs using a Context Catalog. This will ensure regulatory compliance, simplified debugging techniques, and drive more accurate and auditable AI outcomes.

Who should attend

• AI Enterprise Architects, Devlopers, and AI Engineers
• Governance, Risk, and Security Leads
• Data Engineers, Stewards, and Architects
• Platform and System Integration Engineers