• videocam Live Webinar with Live Q&A
  • calendar_month July 15, 2026 @ 1:00 PM ET/10:00 AM PT
  • signal_cellular_alt Intermediate
  • card_travel Cybersecurity & Data Privacy
  • schedule 90 minutes

AI Risk and Cyber Insurance: Assessing Risk and Coverage Portfolios; Improving Audit and Incident Response Efforts

About the Course

Introduction

This CLE webinar will explore the emerging cybersecurity and data privacy risks arising from AI use and how cyber liability insurance coverage is adapting to address them, through both coverage exclusions and new product offerings. The panel will highlight important case law developments addressing AI losses, as well as how data privacy and cybersecurity practitioners can help business clients adjust data privacy and cybersecurity audit efforts to include AI-related risks. 

Description

Organizations of all sizes face cybersecurity and data privacy risks that traditional insurance policies may not fully cover, and cyber insurance helps mitigate those exposures and costs. Coverage can include forensics, litigation expenses, ransomware payments, customer credit monitoring, investigative responses, fines, and more. At the same time, the rapid adoption of generative artificial intelligence (GenAI) and advanced machine learning in core business operations has introduced distinct risks, including algorithmic bias, unpredictable outcomes, hallucinations, and "black box" errors, all of which can create uninsured cyber exposure if not addressed in a company's policy.

Businesses must understand how they use AI in daily operations and which products or outcomes depend on it to evaluate heightened cybersecurity risks and available coverage. First-party cyber coverage may address direct financial losses from AI-related events such as data recovery and business interruption costs. Third-party coverage for external claims, including those by affected customers, may also help offset AI-related exposure. Policy review can reveal AI coverage gaps and help businesses adjust their portfolios as needed, and is especially important as exclusions tighten and AI-related coverage case law evolves.

To meet these challenges, the insurance industry is developing more sophisticated products tailored to AI-related cyber liability, some of which may offer broader protection. However, whether those products provide meaningful coverage at a reasonable cost remains a business-specific question for clients, brokers, and their cybersecurity and data privacy counsel. During this webcast, our faculty will survey emerging AI cyber and privacy threats, discuss best practices for mitigating AI-related risks through audits and incident response planning, and explain how counsel can assess AI risks against existing coverage portfolios and evolving exclusions and case law.

Listen as our authoritative panel provides a practical overview of the latest trends and developments in cyber liability insurance and offers guidance on assisting clients in selecting the right coverage for the new AI risks developing within their business operations.

Credit Information
  • This 90-minute webinar is eligible in most states for 1.5 CLE credits.

  • An excellent opportunity to earn Ethics CLE credits. Note: BARBRI cannot guarantee that this course will be approved for ethics credits in all states. To confirm, please contact our CLE department at pdservice@barbri.com.


  • Live Online


    On Demand

Date + Time

  • event

    Wednesday, July 15, 2026

  • schedule

    1:00 PM ET/10:00 AM PT

I. Analysis of typical AI cybersecurity and data privacy risks

II. Understanding legal impacts

A. M&A and deal risk

B. Unique liability theories arising out of the plaintiff's bar

C. Key regulatory considerations, especially for consumer-facing activities

III. Cybersecurity and data privacy audits: how to weave AI-related risk

IV. Incident response: examining response efficiencies for AI-related events and how to improve them

V. Cyber insurance coverage questions

A. AI-generated failures and errors

B. Unauthorized access of machine learning models (manipulation, poisoning, etc.)

C. Third-party claims for biases, inaccurate or infringing AI-generated outputs

D. Assessing the cost of losses vs. policy limits vs. the policy costs    

E. Unique issues related to vendors

VI. Recent case law addressing AI insurance coverage

VII. Emerging AI insurance products: how to assess their utility in a risk management portfolio

The panel will discuss these and other key considerations:

  • How does cyber liability insurance work with other types of business coverage to address cyber risks?
  • What are the latest trends and recent coverage components of AI-related losses under cyber liability insurance?
  • When does AI-specific coverage become a viable option for business clients?
  • How can businesses adjust cybersecurity audits to meaningfully account for AI-related risks?
  • Should incident response plans look different for losses caused by AI, and if so, how?