AI risk is rising. Your legacy insurance still carries the load.
Artificial intelligence is changing operations and decision-making across the enterprise, and with it, claim profiles. Recent cases, including Raine, et al. v. OpenAI, Inc., Docket No. CGC25628528 (Cal. Super. Ct. Aug 26, 2025), show how quickly exposure can surface and why established insurance lines remain a first line of defense.
Here's the key: the tech is new, but the liabilities look familiar. Commercial general liability still addresses bodily injury. In A.F. et al. v. Character Technologies, Inc. et al., No. 2:24-cv-01014 (E.D. Tex.), plaintiffs allege harms such as risks of self-harm and inadequate warnings-classic CGL territory, depending on policy terms and exclusions.
Directors and officers coverage is also squarely in play. In Sarria v. Telus Int'l (Cda) Inc. et al., No. 1:25-cv-00889 (S.D.N.Y.), plaintiffs claim leadership failed to properly disclose AI impacts-misrepresentation theories D&O policies have long addressed. Cyber, employment practices, and IP lines can likewise respond when the basic elements of liability are triggered.
Legal researchers have already tracked 200+ AI-focused cases, and that count will grow. Do not sideline your legacy insurance. The labels may be new, but the claims machinery runs on familiar causes of action-bodily injury, misrepresentation, breach of duty, and more.
10 essential tips for AI risk
- Review legacy coverage. Do not assume AI is excluded. Map where potential AI-related claims could fit across CGL, D&O, cyber, EPL, IP, and excess layers.
- Check all relevant policies. Treat each incident like a multi-policy puzzle. A single AI event can implicate bodily injury, privacy, product liability, professional services, and securities exposures.
- Leverage third-party insurance. Vendors, data providers, integrators, and platform partners may carry insurance that extends to your operations. Secure additional insured status and tight indemnities.
- Watch for AI-specific exclusions. New endorsements are entering the market to carve out algorithmic error, model bias, or automated decisioning. Scrutinize language at renewal and midterm.
- Take AI underwriting seriously. Answer carrier questionnaires precisely. Understating or misstating AI use can trigger rescission, narrower terms, or claim disputes.
- Consider AI-focused products. Where gaps exist, look at specialty coverage for algorithmic errors, wrongful automated decisions, training-data issues, and evolving regulatory exposure.
- Engage all stakeholders. AI risk is cross-functional. Involve IT, data science, legal, compliance, HR, operations, product, and claims to surface how models are built, deployed, and monitored.
- Review coverage regularly. Calendar quarterly reviews for material changes in AI use, contracts, or vendors. Involve coverage counsel early to avoid unpleasant surprises.
- Appoint a chief AI officer. Centralize oversight for strategy, governance, procurement, and incident response. This role aligns model risk, documentation, audits, and insurance disclosures.
- Invest in AI training and education. Raise fluency for the board, executives, and front-line teams. Better questions lead to better controls and cleaner claims.
Claims and governance: keep your footing
Set a claims playbook for AI incidents: notice all potentially responsive carriers, track tender deadlines, and preserve logs, data, prompts, and model versions. Document human oversight and testing-these artifacts matter in coverage disputes and defense.
Align controls to recognized frameworks. The NIST AI Risk Management Framework offers a practical baseline for mapping functions, risks, and safeguards. For marketing and product representations, review the FTC's guidance on AI claims to reduce misrepresentation exposure.
If your teams need structured AI upskilling to support governance and underwriting disclosures, see role-based training options at Complete AI Training.
The takeaway for insurance teams
AI changes how losses arise, not the core legal theories that drive coverage. Keep legacy policies at the center, close gaps with targeted endorsements or specialty products, and run a tight governance loop across stakeholders. Anticipate issues, act early, and treat disclosure quality as policy performance in disguise.
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