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Advising Businesses on Autonomous AI Liability: Rogue Agents & MCP Risks

Credits Available
Credit Status Total
Alaska CLE Reciprocity 1 Total
Alabama CLE Approved 1 Total
Arkansas CLE Approved 1 Total
Arizona CLE Approved 1 Total
California CLE Approved 1 Total
Colorado CLE Approved 1 Total
Connecticut CLE Approved 1 Total
Florida CLE Approved 1 Total
Georgia CLE Approved 1 Total
Hawaii CLE Approved 1 Total
Iowa CLE Approved 1 Total
Illinois CLE Approved 1 Total
Indiana CLE Approved 1 Total
Kansas CLE Approved 1 Total
Kentucky CLE Approved 1 Total
Maine CLE Approved 1 Total
Minnesota CLE Approved 1 Total
Missouri CLE Approved 1.2 Total
Northern Mariana Islands CLE Approved 1 Total
Montana CLE Approved 1 Total
North Carolina CLE Approved 1 Total
North Dakota CLE Approved 1 Total
New Hampshire CLE Approved 1 Total
New Jersey CLE Approved 1.2 Total
New Mexico CLE Approved 1 Total
Nevada CLE Approved 1 Total
New York CLE Approved 1 Total
Ohio CLE Approved 1 Total
Oklahoma CLE Approved 1 Total
Oregon CLE Approved 1 Total
Pennsylvania CLE Approved 1 Total
Rhode Island CLE Approved 1 Total
Tennessee CLE Approved 1 Total
Texas CLE Approved 1 Total
Utah CLE Approved 1 Total
Virginia CLE Approved 1 Total
Vermont CLE Approved 1 Total
Washington CLE Approved 1 Total
Wisconsin CLE Approved 1 Total
West Virginia CLE Approved 1.2 Total
Wyoming CLE Approved 1 Total
OnDemand
57 minutes
Andrew Eichen
With Andrew Eichen from ZwillGen PLLC
Recorded May 12, 2026.
Product ID 102366

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Overview

Know the Risks Before Your Client Deploys

AI agents can now browse the web, query internal systems, send messages, and take actions on a user's behalf - often with little human review. The business appeal is obvious. The legal risks are easy to miss. This course gives you the tools to spot liability before a client's AI deployment becomes a problem - covering how these systems work, what can go wrong, where existing agreements fall short, and how to build a practical review process. Register today!

  • Understand how AI agents work and where disclosure risks hide.
  • Map data governance, access, and adversarial threats.
  • Identify contract and privacy framework gaps.
  • Evaluate and govern MCP deployments with confidence.

*Any mention of specific products in this program is intended as part of a general overview and does not constitute NBI's endorsement or recommendation of any specific product or provider. This program is not sponsored by any technology or electronics provider.

Abbreviated Agenda

  1. What Are Autonomous AI Agents and MCP?
    1. What Model Context Protocol (MCP) Is and How It Works
    2. Internal Queries as Third-Party Disclosures
  2. Mapping the Risk Landscape
    1. Loss of Governance Over Data (Retention, Secondary Flows, and Unintended Inferences)
    2. Expanded Access and the Democratization Problems
    3. Adversarial Risks and Reliability
    4. The Risks of Write Access
  3. Legal and Compliance Exposure
    1. Contractual Restrictions: Partner Data, Vendor Agreements, and Sub-Processor Limitations
    2. Privacy and Data Protection: Policy Conflicts, Purpose Limitation, and Data Subject Rights
  4. Evaluating and Managing MCP Deployments
    1. Data Inventory and Classification
    2. Contractual and Policy Review
    3. Technical Mitigations: Access Controls, Middleware, and Logging
    4. Ongoing Governance

Related Topics

Product ID 102366

Credit Details

Credits Available
Credit Status Total
Alaska CLE Reciprocity 1 Total
Alabama CLE Approved 1 Total
Arkansas CLE Approved 1 Total
Arizona CLE Approved 1 Total
California CLE Approved 1 Total
Colorado CLE Approved 1 Total
Connecticut CLE Approved 1 Total
Florida CLE Approved 1 Total
Georgia CLE Approved 1 Total
Hawaii CLE Approved 1 Total
Iowa CLE Approved 1 Total
Illinois CLE Approved 1 Total
Indiana CLE Approved 1 Total
Kansas CLE Approved 1 Total
Kentucky CLE Approved 1 Total
Maine CLE Approved 1 Total
Minnesota CLE Approved 1 Total
Missouri CLE Approved 1.2 Total
Northern Mariana Islands CLE Approved 1 Total
Montana CLE Approved 1 Total
North Carolina CLE Approved 1 Total
North Dakota CLE Approved 1 Total
New Hampshire CLE Approved 1 Total
New Jersey CLE Approved 1.2 Total
New Mexico CLE Approved 1 Total
Nevada CLE Approved 1 Total
New York CLE Approved 1 Total
Ohio CLE Approved 1 Total
Oklahoma CLE Approved 1 Total
Oregon CLE Approved 1 Total
Pennsylvania CLE Approved 1 Total
Rhode Island CLE Approved 1 Total
Tennessee CLE Approved 1 Total
Texas CLE Approved 1 Total
Utah CLE Approved 1 Total
Virginia CLE Approved 1 Total
Vermont CLE Approved 1 Total
Washington CLE Approved 1 Total
Wisconsin CLE Approved 1 Total
West Virginia CLE Approved 1.2 Total
Wyoming CLE Approved 1 Total
Select Jurisdiction
CLE

Agenda

All times are shown in

  1. Advising Businesses on Autonomous AI Liability: Rogue Agents & MCP Risks

    1. What Are Autonomous AI Agents and MCP?
      1. What Model Context Protocol (MCP) Is and How It Works
      2. Internal Queries as Third-Party Disclosures
    2. Mapping the Risk Landscape
      1. Loss of Governance Over Data (Retention, Secondary Flows, and Unintended Inferences)
      2. Expanded Access and the Democratization Problems
      3. Adversarial Risks and Reliability
      4. The Risks of Write Access
    3. Legal and Compliance Exposure
      1. Contractual Restrictions: Partner Data, Vendor Agreements, and Sub-Processor Limitations
      2. Privacy and Data Protection: Policy Conflicts, Purpose Limitation, and Data Subject Rights
    4. Evaluating and Managing MCP Deployments
      1. Data Inventory and Classification
      2. Contractual and Policy Review
      3. Technical Mitigations: Access Controls, Middleware, and Logging
      4. Ongoing Governance

Who Should Attend

This legal update is designed for attorneys. CTOs and directors will also benefit.

Speakers

Andrew Eichen
Andrew
Eichen
ZwillGen PLLC
Speaker bio
Andrew Eichen

Andrew Eichen

ZwillGen PLLC
Andrew Eichen

is an attorney at ZwillGen PLLC. He helps clients navigate the regulatory and strategic challenges of artificial intelligence. With a background in law, finance, and public policy, he advises clients on responsible AI deployment and compliance under emerging frameworks including the EU AI Act, Colorado AI Act, and NYC Local Law 144. His experience includes drafting internal policies aligned with the NIST AI Risk Management Framework, developing vendor assessment frameworks, and building AI-specific incident response plans. He specializes in evaluations and testing of AI systems, including adversarial testing (red teaming) for GenAI and accuracy/bias audits for predictive models. Andrew also advises clients on system-specific risks and AI-related contract terms for SaaS and enterprise applications. Prior to joining ZwillGen, he was an associate at Venable LLP, where he advised financial institutions and fintech companies on banking regulations and payment processing. He earned his J.D. degree, magna cum laude, from the University of Pennsylvania Law School, where he was inducted into the Order of the Coif and received the graduating prizes for Excellence in Business Law, Best Paper in Law and Economics, and Outstanding Trial Advocacy Abilities. During law school, he focused his studies on technology law, serving as a research assistant to Professor Christopher S. Yoo, a leading authority on internet law, and as a senior editor of the Journal of Law and Innovation. He also holds a Master of Public Policy from Georgetown University's McCourt School of Public Policy, where he was named a McCourt Scholar, the school's most prestigious merit scholarship. His AI-focused coursework included studies with Professor Paul Ohm, and his research on AI in higher education earned the award for Most Outstanding Thesis. He earned his Bachelor of Science in Business Administration, magna cum laude, from Washington University's Olin Business School, with double majors in finance and entrepreneurship.

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