Insurance for AI Companies
What Founders and VCs Need to Know
1. Introduction: AI Risk Isn’t Hypothetical
2. Why AI Companies Are a Unique Insurance Risk
3. Core Insurance Coverages for AI Companies
4. The Problem with Standard Policies
5. AI-Specific Endorsements and Custom Language
6. Real-World AI Litigation and Enforcement
7. How Much Coverage Do You Need?
AI systems now drive hiring, lending, diagnostics, and decision-making across industries. But when models misfire - bias, privacy violations, enforcement - you can’t rely on boilerplate insurance.
Most policies don’t define “AI.” Most brokers don’t ask the right questions. And exclusions tied to discrimination, data misuse, or regulatory action often go unnoticed - until it’s too late.
This guide breaks down what insurance for AI companies should cover , and how to protect your company, your leadership, and your next funding round.
Unlike traditional SaaS, AI models can:
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Discriminate (even unintentionally)
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Trigger regulatory scrutiny (FTC, EEOC, EU AI Act)
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Misuse data (training sets, outputs, scraped content)
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Make errors you can’t always explain or trace
Why it matters for insurance:
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Policies must address model behavior, not just service delivery
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Bias, discrimination, or unfair outcomes are often excluded
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Regulators treat AI models as decision-makers, and that invites liability
AI Specific Insurance
AI companies need insurance built around outputs, not just operations. If your policy doesn’t define how “AI” fits into coverage, it may not fit at all.
Core Insurance Coverage AI Companies Need
Problems with Standard Corporate Insurance Policies
Common problems:
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“Professional services” excludes model development or deployment
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No coverage for algorithmic bias, discrimination, or unfair outcomes
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Government investigations (FTC, EEOC, CFPB) often excluded
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Policies protect inputs, not outputs - your model’s behavior isn’t covered
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No understanding as to how AI fits into the policy at all (Affirmative AI)
If your policy doesn’t define what your company actually does, it’s not built to protect it.
AI-Specific Endorsements: What They Actually Cover
Most Tech E&O policies are silent on artificial intelligence. They don’t define AI systems, model outputs, or algorithmic behavior - leaving coverage unclear. Affirmative AI endorsements remove that ambiguity.
They explicitly state which AI-related risks are covered, including algorithmic bias, hallucinations, IP issues, and regulatory investigations. If you're building or deploying AI, these endorsements/coverages make the difference between a denied claim and a defended one.
AI insurance isn’t just about what’s included - it’s about what’s clearly protected.
1. Algorithmic Bias & Discrimination
AI models may produce biased outcomes in hiring, lending, or other sensitive areas.
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Affirmative coverage ensures these claims are not excluded as “discrimination.”
2. AI Hallucinations
Your model generates inaccurate, harmful, or misleading outputs.
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Endorsements clarify this is a covered “error” under your Tech E&O.
3. Silent AI Risk (AI not Mentioned)
Most standard E&O policies don’t define “AI,” leaving coverage unclear.
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Affirmative endorsements make AI systems explicitly included - not excluded by silence.
4. Training Data & IP Risk
Using unlicensed or scraped content to train models can lead to infringement claims.
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Affirmative language covers IP issues tied to both inputs and outputs.
5. Privacy & Data Use Violations
AI systems process regulated data like biometrics, financials, or PII.
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Coverage extends beyond breach to include misuse via your model.
6. Regulatory Action (e.g. FTC, EEOC, AG Investigations)
Government agencies are already investigating AI systems.
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Endorsements can include legal defense and penalties (where allowed).
7. Model Drift or Retraining Risk
As models evolve, new outputs may introduce new risks.
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Endorsements account for ongoing learning and changing model behavior.
Real-World AI Litigation and Enforcement
Employment discrimination
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Tools used for screening or hiring are facing EEOC actions and class claims for biased outcomes. (Mobley v. Workday)
Privacy & biometric misuse
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AI models trained on scraped or regulated data have triggered lawsuits under laws like BIPA and GDPR.
Misleading marketing claims
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The FTC has warned (and sued) companies overstating what AI can do or how “fair” it really is. (AI Washing)
Investor claims tied to AI risk
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Shareholders and board members are questioning whether leadership understood or disclosed model risks. (Dual Fiduciary)
IP infringement
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Startups are being sued for using third-party data or code without licensing in their model training.
There’s no one-size-fits-all number. AI risk is nuanced - and your insurance limits should reflect your exposure, not just your funding round.
We benchmark AI insurance limits based on:
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Capital Raised
More funding = greater board expectations and liability pressure
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Model Risk Profile
Is your model making decisions (e.g., hiring, lending) or generating content? Risk varies.
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Jurisdictions & User Base
Operating in California, Illinois, the EU, or New York increases exposure to privacy and bias laws.
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Training & Output Data
If your model uses PII, health data, or biometric info, you need stronger Cyber + Tech E&O alignment.
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Contractual Requirements
Are customers or partners requiring specific E&O or Cyber limits? That sets the floor.
AI Insurance Checklist
Covers government investigations (e.g., FTC, EEOC) - not just investor lawsuits.
Protects against harm caused by your model - not just failure to deliver services.
Determine if you want explicit, "affirmative AI" coverage or prefer the logic of silent cover.
Cyber Coverage Beyond Breach
Includes misuse of data, training set issues, and prompt injection attacks.
IP Protection for Training and Output
Covers claims tied to scraped, unlicensed, or third-party content used in your model.
Model Drift + Retraining Language
Accounts for risk introduced as your AI system learns or evolves over time.
Coverage Limits Aligned to Risk
Based on your capital raised, data exposure, customer profile, and legal jurisdiction.
A Broker Who Redlines Policies
If your broker isn’t flagging exclusions and negotiating terms, they’re not protecting you.
URM AI Practice
At URM, we specialize in insurance solutions for AI companies - from early-stage startups to scaled enterprise vendors. Our team has negotiated affirmative AI endorsements, restructured D&O and Tech E&O policies to fit real model risk, and advised founders post-claim.
We understand the regulatory, legal, and operational exposures AI companies face - because we’ve seen them play out.
Whether you're deploying machine learning tools, generative AI models, or decision engines, we help ensure your insurance actually covers what your company does.
