AGENTS / DATA / GOVERNANCE / ACCOUNTABILITY

Legal AI is moving from tools to agents, and that shift changes the risk profile overnight.

As models become more autonomous, the key question is no longer “what can AI do?” but how do we control what it can access, what it can execute, and what it can return reliably, repeatably, and securely?

In this 90-minute webinar, Morgan Llewellyn (HIKE2) introduces the Model Context Protocol (MCP) as a practical architecture for governing agent-based AI: a structured layer that sits between your AI application and your firm’s data, tools, prompts, and workflows, enabling permissions, standardization, validation, and auditability by design.

Rather than theorizing about governance, this session focuses on operational control: how firms are beginning to use MCP-style patterns to reduce hallucinations, prevent data leakage, enforce “firm-approved” prompt and workflow standards, and create a scalable foundation where one controlled interface can serve multiple AI front-ends and use cases.

By the end of this session, attendees will:

• Build a clear mental model for how agents work and why “autonomy” increases governance requirements, not just capability.
• Understand what MCP is (and isn’t), and where it fits in a modern legal AI architecture.
• Learn how MCP-style design enables access control, prompt/workflow standardisation, and safer data/tool exposure across teams and use cases.
• Identify the highest-value MCP pilot patterns for law firms (documents, KM, matter data, contract review, prompt libraries) and where pilots commonly fail.
• Leave with a practical set of stakeholder questions and implementation next steps for IT, KM, Risk, and Innovation to move from experimentation to controlled operation.

Key Themes & Discussions

Agents Change the Governance Problem

Why agent-based AI isn’t just “better copilots” - it introduces new autonomy, new pathways into firm systems, and a bigger control challenge. We’ll map what must be governed as agents move from answering questions to taking action.

MCP as the Control Layer Between AI and Your Firm

What MCP is (and isn’t), and how MCP-style architectures sit between AI applications and firm resources to govern access to data, tools, and workflows. We’ll cover why this layer is becoming foundational to trust, safety, and reliability at scale.

From “Good Data” to Reliable Outcomes

Why clean data alone is not sufficient, and how mistakes still occur even with strong datasets. We’ll explore how MCP enables tighter grounding, structured access, and validation patterns that reduce variance and improve reliability.

Standardization: Prompt Libraries, Workflows, and Repeatable Analysis

How firms are beginning to move from ad hoc prompting to approved prompt libraries and standardized analysis workflows. We’ll discuss how this supports consistency, training, defensibility, and ultimately automation readiness.

Security, Permissions, and Interoperability in Practice

How MCP-style design supports SSO, permissions, and secure exposure of firm resources without “slurping” directly from data lakes or uncontrolled repositories. We’ll also examine how one MCP layer can serve multiple AI front-ends, reducing integration sprawl.

From Experiment to Operating Model

A pragmatic path to implementation: inventory your tools and data assets, define a safe sandbox boundary, and pilot high-repetition use cases first. We’ll cover how to bring IT, KM, Risk, and practice leadership into a shared model that can scale responsibly.

Who Should Attend

This webinar is designed for legal professionals responsible for AI strategy, governance, and implementation, including:

• CIOs, CTOs, and Heads of IT
• Innovation, AI, and Legal Tech leaders
• Knowledge Management and Information Governance professionals
• Risk, Compliance, and Data Protection leaders
• Practice leaders and partners overseeing AI adoption
• In-house legal operations teams exploring advanced AI use cases

Our Speaker