Guide to Insurance for AI Startups (2025)
- Steven Barge-Siever, Esq.
- Jul 31, 2025
- 5 min read
Updated: Aug 2, 2025
What to buy, when to buy it, and how to avoid common mistakes.
By Steven Barge-Siever, Esq.
Why Insurance Is Strategic for AI Startups
Insurance is often viewed as a checkbox, but for AI startups, insurance is a strategic, revenue generating tool.
Unlock enterprise deals: Enterprise buyers don’t just evaluate product viability - they assess risk. And they will not rely on your startup’s balance sheet to absorb liability. In these scenarios, Insurance acts as a contractual risk transfer mechanism. When something goes wrong, your carrier pays. For high-value customers, it’s not a nice to have - it can make or break a deal. Enterprise clients want to work with startups that look like stable vendors - and insurance is a tool to win their trust.
Protect board members and investors: D&O insurance shields your directors and officers from personal liability. If there’s a claim alleging mismanagement, breach of fiduciary duty, misleading investors, or even failing to supervise algorithmic risk, D&O covers legal defense and settlements. Without it, the board is personally exposed - and that’s a non-starter for seasoned investors/directors.
Mitigate legal exposure from LLM outputs, training data, and privacy concerns: If your AI model hallucinates, misuses data, or triggers regulatory action, you’ll need to defend your company. Insurance is designed to cover damages and legal defense costs, which can easily run into the millions. For startups facing ambiguous regulatory frameworks and fast-evolving model risk, insurance acts as the legal backstop.
Satisfy venture capital expectations and governance standards: If a VC is joining your board, they expect D&O in place as a requirement. More sophisticated funds will ask who your carrier is, what the limits are, and whether the policy includes side A protection. If you want to project maturity during a raise or sales cycle, don’t nickel-and-dime the insurance stack. Buy from strong carriers with recognizable names and real balance sheets.
It is worth noting that as your platform evolves, your liabilities do too. From product hallucinations to regulatory scrutiny, the coverage you buy should evolve with your architecture, clients, and scale.
When Should AI Startups Should Buy Insurance?
When AI Startups Should Buy Insurance
Trigger Events:
Seed Funding: This is often the first moment when a company takes on external stakeholders, which means real governance expectations. Even if your cap table is clean, your investors want to know their downside is covered. D&O protects founders and investors alike, while Cyber covers early product testing and PII handling.
Enterprise Go-to-Market (GTM): The moment you start selling to enterprise clients, insurance stops being optional. Procurement teams will mandate Cyber, E&O, GL, Crime along with minimum limit thresholds. Why? Because they can't rely on your startup’s balance sheet to absorb liability. Insurance gives them contractual risk transfer - and gives you a shot at closing the deal.
Model Commercialization: Whether you're releasing an LLM, building on top of one, or integrating AI into customer-facing workflows, you're assuming new liability. Outputs can cause harm. Training data can introduce legal risk. And any third-party reliance introduces expectations of performance. Tech E&O and Cyber should be reviewed or added here.
Hiring a C-suite or Board: No seasoned exec or outside board member will take a seat without D&O in place. It’s not just about optics - it’s about financial protection. If you're raising a round and bringing in strategic oversight, expect D&O to become a hard requirement during due diligence.
Don’t wait until you're asked for proof of insurance. Insurance is cheapest and most customizable before you’re under deadline pressure, and before you've signed big deals with real revenue.
What are the Core Insurance Products for AI Startups?
1. Directors & Officers (D&O) Insurance
Protects: Founders, board members, and officers from personal liability
AI-specific risks:
SEC scrutiny over AI marketing claims ("AI-washing")
Shareholder suits tied to misrepresentation of model capabilities or financials
Claims over data governance, privacy compliance, or algorithmic bias
2. Technology Errors & Omissions (Tech E&O)
Protects: Company liability from product or service failures
AI-specific risks:
Hallucinated output causes real-world harm
API or LLM integrations fail to perform
Legal exposure from reliance on flawed or incomplete results
Pro tip: Pair with Media Liability and Affirmative AI Endorsements to avoid gaps.
3. Cyber Liability Insurance
Protects: Breach of data, ransomware, business interruption, and third-party claims
AI-specific risks:
Model training on unlicensed or sensitive data
Inference attacks, prompt injection, or API abuse
Use of AI-generated code that introduces vulnerabilities
4. Employment Practices Liability (EPL)
Protects: Claims of discrimination, harassment, wrongful termination
AI-specific twist:
Use of AI tools in hiring, firing, or compensation can trigger bias claims
Emerging focus of plaintiff’s bar: AI-influenced employment decisions
5. Commercial General Liability (GL)
Protects: Bodily injury or property damage (least relevant for SaaS AI, but required in contracts)
6. Media Liability / Intellectual Property
Often built into Tech E&O or sold separately
AI-specific risks:
Model generates infringing content
False statements, deepfakes, or reputational harm
Media coverage is essential if your platform outputs text, code, images, or video.
Common Mistakes AI Startups Make
Buying generic E&O without affirming AI coverage
Many startups assume Tech E&O is a commodity product. But if you’re deploying AI, most off-the-shelf policies exclude “algorithmic decisions,” “automated output,” or undefined “data processing errors.”
Example: A Series A AI company discovered mid-lawsuit that their E&O carrier was denying defense because the claim involved a hallucinated answer from their LLM - an excluded risk under their generic policy. The right broker could have flagged this and negotiated carve-backs.
Assuming Cyber is enough
Cyber insurance is designed for data breaches and ransomware, and not for model output failures, LLM bias, or API breakdowns.
Example: A founder thought they had “AI insurance” because they purchased a $3M cyber policy. But when a partner relied on bad model output and sued for contractual damages, the carrier denied the claim - it wasn’t a data breach. That’s Tech E&O territory.
Letting contracts dictate structure
Many founders buy coverage reactively - only when procurement demands it. But those requirements don’t reflect your actual risk or policy language, and generally only dictate the amount to purchase based on a legal template.
Example: A startup added $5M of GL and Cyber coverage to close a contract, but had no Tech E&O. Two months later, their AI feature failed, and a customer sued for bad advice. No coverage. The contract drove the purchase, but not the protection.
Choosing brokers without tech fluency
If your broker doesn’t understand how how a RAG system with guardrails works, or the methods for training through HRL (or other fine tuning methods), they can’t translate your risk into insurance.
Example: An early-stage AI company used their friend's generalist broker. Their policy had an AI exclusion, no media carve-backs, and missing language on regulatory coverage. No one caught it, until they needed it. Tech fluency isn’t optional.
If your broker doesn’t know what RAG, inference, or LLM means, they can’t structure proper coverage.
Coverage Strategy by Funding Stage
Stage | Recommended Coverages | Notes |
Pre-Seed | None or Cyber only (if working with PII) | Wait unless triggered by client or compliance need |
Seed | D&O, Cyber, Tech E&O (if GTM) | Begin building risk infrastructure |
Series A | D&O, EPL, Cyber, Tech E&O, Crime, GL | Purchase an expandable, full insurance stack |
Series B+ | All above + layered limits, endorsements | Enterprise risk, deal size, and exposure justify higher coverage |
How to Vet Your Broker
Do they understand LLM risks, hallucinations, or prompt injection?
Have they worked with AI-native companies or regulated GTM use cases?
Can they negotiate endorsements that carve back AI coverage into E&O and Cyber?
Do they treat insurance as a strategic asset - or a commodity?
Final Thought: Insure What You Actually Built
AI is different. Your insurance should be too.
At Upward Risk Management, we help AI startups build intelligent coverage that aligns with how your platform actually works, and how risk shows up across your pipeline, contracts, and boardroom.
Want a quick review of your current policy stack?
Talk to us.
Upward Risk Management LLC
