Coverage Strategy by Funding Stage
Recommended Coverage:
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D&O (for investor protection and governance)
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Cyber (if data exposure is present)
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Tech E&O (if product is deployed in any live environment)
Notes:
Seed is often the first time external stakeholders appear. If a SAFE or convertible note is in place, you may have representational risk. If you're onboarding design partners or testing integrations, E&O and Cyber become real exposures - even without revenue.
Pre-Revenue
Recommended Coverage:
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D&O (full policy, including Side A)
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Tech E&O (with affirmative AI coverage or exclusions reviewed)
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Cyber Liability (enhanced for model/data risks)
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EPL (if headcount is growing or hiring automation is in use)
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Crime (if touching financial data or APIs)
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GL (if required in contracts or leases)
Notes:
This is the first inflection point where coverage must shift from reactive to strategic. Procurement will demand certificates. Investors will ask about limits. And contractual indemnity obligations will trigger liability the moment you sign your first enterprise customer.
Series A
Recommended Coverage:
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All of the above
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Excess layers across D&O, E&O, and Cyber
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Custom endorsements (AI carve-backs, media/IP riders, regulatory defense)
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Higher retentions and structuring flexibility
Notes:
At this stage, you're likely onboarding enterprise accounts, scaling infrastructure, and increasing your reliance on AI outputs in commercial contexts. This is also when regulators, competitors, and plaintiffs start paying attention. Coverage should anticipate these shifts.
Series B
Recommended Coverage:
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Updated D&O with M&A-specific language (Side A run-off, change-in-control triggers)
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E&O review for reps, warranties, and legacy liability
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Cyber and Crime coverage continuity for acquired systems or data
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Transactional liability coverage (if involved in deal structure)
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Coverage alignment for buyer and seller (especially in acquihires)
Notes:
Acquisition activity - whether you're the acquirer or the target - creates instant exposure. Legacy liability can attach to the board, platform, or prior decisions. If you're acquiring a company with poorly structured insurance, that exposure becomes yours. Conversely, if you're the target, you need run-off protection and clarity around pre- and post-close coverage triggers. This is especially critical for AI companies whose model training, data rights, or past representations may come under diligence or litigation after close.
M&A
Recommended Coverage:
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Public company D&O placement strategy
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Roadshow and prospectus liability protection
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Run-off for private company D&O policy
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Straddle claim coverage for pre- and post-IPO conduct
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Enhanced E&O with public-company thresholds (regulators, shareholders)
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Cyber tower review with incident response support
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Re-underwriting of EPL, particularly for executive risk and whistleblower exposure
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Claims-made policy alignment to minimize tail gaps
Notes:
The Pre-IPO stage invites litigation risk before the ticker symbol exists. Plaintiffs file as if you're public; underwriters scrutinize everything; regulators start watching disclosures. You'll need to navigate the shift from private to public D&O forms while preserving tail coverage for private-era decisions.
Straddle claim coverage is critical at this stage. Without it, claims that allege misconduct spanning both the private and public periods can fall between policies - especially if carriers dispute timing, continuity, or definition of "wrongful acts." Coverage should be coordinated to ensure no gaps exist during the transition window.
Investor expectations also change - coverage becomes a governance issue, not just a protection layer. The structure you put in place pre-IPO often dictates how resilient your risk transfer program will be post-listing.
Pre IPO
Recommended Coverage:
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ABC D&O tower with Side A DIC layers
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E&O/Cyber towers expanded and restructured for public scale
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EPL with executive-focused coverage and regulatory triggers
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Global policy coordination (if cross-border ops exist)
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Ongoing underwriter briefings for earnings, AI governance, ESG exposure
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Audit-aligned insurance structure with board oversight
Notes:
At the public stage, everything becomes discoverable. Misrepresentation, governance failure, insider risk, model claims - all are fair game. Coverage structure should support quarterly disclosure, board accountability, and market-facing risk. The litigation profile changes from product-centric to stock-price driven. Your D&O tower must be built accordingly, with catastrophic protection in place if indemnification fails.
Public
Common Mistakes AI Startups Make When Buying Insurance
In our opinion, misunderstanding what you actually bought is the biggest risk to a strategic approach to risk management.
Policy language may feel complete, but fails under any amount of pressure. Mistakes tend to happen based on ignored risk, or because coverage was structured generically, and without regard for how AI companies actually operate.
1. Buying Generic Tech E&O with AI Exclusions
Many early-stage companies assume Tech E&O is a commodity product. It’s not. Most off-the-shelf E&O policies include exclusions for:
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Algorithmic output
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Automated decision-making
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“Unintended consequences” of software behavior
These provisions are often buried in endorsements and override favorable base language.
Result: You’re denied coverage when your model’s output causes financial harm - even if the customer relied on it exactly as intended.
2. Relying on Cyber Insurance Alone
Cyber coverage is critical, but it’s designed for data breaches—not for model failures, hallucinated output, or third-party reliance. If your product generates decisions, predictions, or guidance, Tech E&O (not Cyber) is the policy that needs to respond.
Real-world example: A founder believed they had “AI insurance” because their $3M Cyber policy included data breach protection. When a customer relied on flawed model output and sued for damages, the carrier denied the claim. It wasn’t a breach - it was a product failure.
3. Letting Contract Requirements Drive the Coverage Structure
Most companies build their insurance program reactively - buying policies only when required by enterprise customers or investors. The result is a stack optimized for certificates, not actual protection.
Procurement clauses typically focus on limits, not language. Meeting those requirements doesn’t guarantee the policy will respond to real-world exposure.
Common outcome: Policies are in place, but contain exclusions, sublimits, or third-party carvebacks that invalidate claims.
4. Choosing Brokers Without Technical or Legal Fluency
AI insurance cannot be quoted accurately without a deep understanding of:
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How your model is trained, deployed, and fine-tuned
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Where liability flows through prompts, APIs, or user output
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How regulators (FTC, SEC, EU AI Act) are approaching AI risk
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How contract terms and indemnification obligations shift exposure
A broker who doesn’t understand inference, prompt injection, or embedded risk cannot structure your coverage to reflect it. And that disconnect often isn’t visible until a claim is filed.
What Are the Core Insurance Products for AI Startups?
The insurance stack for AI companies isn’t one-size-fits-all. Coverage must be structured around how your platform operates, where liability flows, and what stakeholders expect—across sales, legal, infrastructure, and governance.
These are the foundational policies most AI startups will need to evaluate by the time they reach commercialization.
1. Directors & Officers (D&O) Insurance
Protects: Founders, executives, and board members from personal liability tied to company decisions.
AI-specific exposures:
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Misrepresentation of AI capabilities (so-called AI-washing) in fundraising or customer negotiations
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Governance failures around model risk, training data, or safety controls
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Shareholder claims related to underperformance, regulatory action, or product harm
Why it matters: Most institutional investors require D&O before closing a round or joining the board. Claims often name the individual, not just the company.
2. Technology Errors & Omissions (Tech E&O)
Protects: The company from liability tied to performance failures of your technology or platform.
AI-specific exposures:
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Hallucinated output that causes commercial harm to a customer
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Model failure tied to prompt injection or improper fine-tuning
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API misfires, unreliable inference, or unexpected results in enterprise workflows
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Client reliance on model outputs that later prove flawed or non-compliant
Structuring Note: Off-the-shelf E&O policies often exclude “automated decision-making” or “algorithmic output.” Affirmative AI carve-backs or negotiated endorsements are often required.
3. Cyber Liability Insurance
Protects: Against data breaches, privacy violations, ransomware, and business interruption tied to system compromise.
AI-specific exposures:
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Misuse of regulated or restricted training data
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Inference attacks or reconstruction of private information
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Prompt injection or output manipulation
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Model access leading to downstream data exposure or reputational damage
Why it matters: Many AI companies trigger privacy exposure without ever experiencing a traditional breach.
4. Employment Practices Liability (EPL)
Protects: Against claims of discrimination, harassment, wrongful termination, and retaliation.
AI-specific exposures:
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Use of AI in candidate screening or performance reviews that leads to bias claims
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Disparate impact from algorithmic decision-making
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Compliance with new AI hiring laws (e.g., NYC Local Law 144)
For Vendors: If you build AI systems used in employment workflows, EPL claims may be redirected to you via indemnification demands or product liability allegations. Tech E&O must be structured to respond.
5. Media Liability / Intellectual Property
Protects: Against third-party claims for defamation, IP infringement, or reputational harm tied to content your system generates or hosts.
AI-specific exposures:
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Model output that replicates copyrighted material
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Generated images, code, or text that results in takedown demands or litigation
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Claims over misinformation, impersonation, or reputational damage
Why it matters: If your platform produces content - text, visuals, audio, or code - media liability must be built into your E&O policy or structured separately.
6. Commercial General Liability (GL)
Protects: Against bodily injury or property damage arising out of company operations.
Relevance for AI companies: Often required in enterprise contracts, real estate leases, or vendor onboarding. For software companies, it's rarely the primary source of liability, but still contractually required.
When Should AI Startups Buy Insurance?
Insurance for AI startups is about anticipating the next stage of growth, and the liabilities that come with it.
Coverage needs to be aligned to company milestones, not just revenue or headcount. Delaying key policies until you’re asked for them may satisfy procurement, but it won’t reflect your actual exposure, or protect your board when it matters.
Key Trigger Events for Coverage
Seed Round
First external investors = first governance obligations.
Even with a clean cap table, Seed funding introduces risk: investor scrutiny, representations in a SAFE or convertible note, and the need for D&O coverage to protect founders. Cyber coverage may also be required if you’re testing with real data or onboarding design partners.
Enterprise Go-to-Market (GTM)
The moment you move from pilot to procurement, insurance shifts from optional to essential.
Enterprise customers will require:
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Cyber Liability
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Tech E&O
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Commercial General Liability
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Crime (for access to payment or financial systems)
Procurement teams aren’t relying on your balance sheet. They’re relying on your carrier.
Model Commercialization
If you're shipping an AI model or embedding one in customer workflows, you’re assuming new liabilities:
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Model outputs are relied on for business decisions
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Prompt injection or misuse creates third-party harm
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Your model’s architecture may introduce undisclosed risks
Tech E&O and Cyber coverage should be reviewed—and in most cases, rewritten - to reflect your actual architecture, data usage, and output behavior.
Board Formation or C-Suite Hires
No experienced board member or executive will join without D&O in place.
This isn’t a formality - it’s protection against personal liability. If you’re bringing on senior talent or raising institutional capital, D&O coverage becomes a hard requirement.
Timing Matters
Coverage is more customizable and cost-efficient before it’s required by a customer or investor. Once you’re under deadline pressure, the policy language is often secondary to getting a certificate issued - and that’s when risk creeps in.
The best time to buy insurance is before you sign a contract that assumes you already have it.
The second best time is before your board asks who’s on the hook if something goes wrong.
Insurance for AI Startups
AI Insurance: What it is, and Why It’s Not What You Think
To begin with the conclusion, "AI insurance” isn’t a standalone product. It is not one policy, and it’s not sitting on a shelf at Lloyd’s waiting for you to buy it.
So what is Insurance for an AI company? It is a strategy, and it encompasses enterprise sales, litigation cost mitigation, and a fundraising enablement tool.
In that regard, AI insurance is a portfolio of commercial coverages that include Tech E&O, Cyber, and D&O. The interlocking of these coverages should be structured to address the unique risks posed by AI models, data pipelines, and autonomous outputs.
These risks are not underwriting boxes where (revenue * client size) = risk broken down into $premium.
Hallucinated text isn’t, exactly, coding error. It can amount to an infringement, or it may interpret PII.
Prompt injection isn’t a hack in the conventional sense - it's a behavioral manipulation of an AI model.
Bias in model outputs isn’t just a PR problem - it’s an emerging source of regulatory and employment litigation.
And if you’re training on customer data, IP-restricted content, or third-party APIs, you’re assuming legal liability every time your model performs.
In this guide, we break down:
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Current insurance realities in 2025 for AI companies
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Insurance Strategy for AI Companies
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Core insurance products for AI companies
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Common mistakes AI Companies make with insurance
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How much and when AI Companies should by insurance
Guide to Insurance for AI Startups (2026)
What to buy, when to buy it, and how to avoid common mistakes.
Insurance for AI Companies
2025
The insurance industry is starting to catch up.
As of mid-2025, here’s the reality:
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There is no standalone “AI insurance policy.” What exists today is a complex patchwork of Tech E&O, Cyber, and D&O coverage - each written for a different era of technology.
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Most policies were not built to handle probabilistic outputs, inference-based systems, or machine-generated decisions. Yet these are the risks AI startups carry every day.
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As litigation, regulation, and enterprise contracting evolve, how insurers define - or exclude - AI has become one of the most material variables in any insurance program.
Some carriers are adapting. Others are quietly excluding coverage.
The result? Two AI startups with similar models, clients, and architectures can carry insurance programs with completely different outcomes in a claim.
One will trigger. The other will collapse.
This is where expert structuring matters.
The value of a broker isn't access to markets - it's the ability to translate technical risk into policy language that holds up when it’s tested.
That’s what we do at Upward Risk Management.
Why Insurance Is Strategic for AI Startups
Unlock Enterprise Deals
Enterprise buyers/clients don’t just evaluate your tech - they evaluate your risk profile.
If your model fails, hallucinates, or causes downstream harm, they’re not suing a 20-person startup with a short runway. They want a path to recovery. Insurance creates that path.
In these deals, insurance functions as contractual risk transfer:
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When something breaks, your carrier pays
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Procurement boxes are checked faster
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Your platform looks like a safe bet - not a litigation trap
Bottom line: Insurance is often the difference between closing a deal and getting stuck in procurement purgatory.
Recruit and Protect Your Board and Officers
D&O insurance protects your leadership from personal liability. That includes:
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Misrepresentation of AI capabilities
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AI-washing in fundraising, investor decks, or public claims
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Governance failures around training data, user risk, or auditability
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Shareholder suits over compliance, performance, or valuation volatility
AI-washing isn’t just a marketing problem - it’s a liability trap.
Regulators are already cracking down on companies that overstate their use of AI, especially when those claims influence investor behavior or enterprise sales.
If you’re raising capital or signing enterprise contracts on the back of
AI functionality, you’re taking on representational risk - and your board becomes a vector for litigation. No seasoned investor or independent director will sit on your board without D&O. And in today’s regulatory environment, AI-native governance failures are no longer theoretical.
If your model can hallucinate, your board can be sued for failing to anticipate it.
If your pitch deck overstates AI capabilities, your board can be sued for signing off.
Cyber - With AI in the Loop
The traditional Cyber framework - ransomware, phishing, perimeter breaches - still matters. But for AI companies, the attack vector has shifted inward.
Today, companies are liable for what their model sees, stores, and says - not just what’s stolen.
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If your training data includes regulated or restricted content, you may trigger privacy violations without ever experiencing a breach.
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If your model can be manipulated through prompt injection or chaining, you’ve lost control of its output - creating exposure to downstream contractual or reputational claims.
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If your infrastructure includes open-source LLMs, unsecured plugins, or third-party models, you're inheriting systemic risk that may not be documented - or covered.
You are responsible for protecting sensitive information and ensuring the system behaves as intended.
Most Cyber policies were written for data breaches - not for models that make decisions, generate content, or act on probabilistic logic. Without modification, many Cyber forms exclude liability from model behavior, output misuse, or unintentional data disclosure.
Building AI? The Liability Flows Through You
If your company builds AI systems, models, or tooling that others rely on, your exposure doesn’t end with your own data. You're responsible for:
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The security posture of your hosted model or API
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The way your system handles user prompts or injected content
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The privacy, safety, and auditability of any downstream data processing
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And often, for the behavior of your model when embedded in someone else’s product
When a customer suffers a data incident tied to your model’s behavior, they will look to your company for indemnification. If your Cyber policy isn't structured for AI, the claim may be denied on the basis that no breach occurred, or that automated model output isn’t covered.
Employment Practices Liability (EPL, EPLI)
AI is increasingly embedded in employment workflows - resume screening, candidate scoring, performance evaluations, compensation decisions. This introduces legal exposure for employers, but also for the vendors building the technology behind these decisions.
If your company builds or licenses AI models that influence employment outcomes, the exposure is not secondhand—it’s direct.
Regulators and plaintiffs are now scrutinizing:
Bias in model design or training data
Lack of auditability or transparency in decision-making
Failure to disclose automated evaluation tools
Vendor representations about “bias mitigation” or “fairness”
When an employer is sued over discrimination tied to an automated hiring tool, their next move is often to bring in the vendor - alleging product failure, negligent design, or misleading marketing.
These claims typically target the AI company’s Tech E&O policy, not EPL.
But most Tech E&O policies contain carveouts or vague definitions that leave automated decision-making, data-driven scoring, or third-party reliance in a grey area. If not structured properly, your carrier may argue:
The claim involves employment decisions (and belongs under EPL)
The model’s output was informational, not actionable
The harm was caused by the employer, not the software
This finger-pointing is common, and it creates a coverage vacuum unless addressed in advance.
Structuring Coverage for AI Employment Tech Vendors
To mitigate this risk, AI startups building employment tools must ensure:
Tech E&O explicitly covers liability tied to algorithmic scoring, decision support, and model outputs used in employment contexts
EPL exclusions in E&O are either removed or clarified not to apply to third-party use cases
Affirmative AI endorsements include automated decision support and bias-related claims, not just general “technology services”
Without this structuring, AI vendors are exposed on both ends:
Claims from customers alleging product failure
Claims from regulators alleging unfair practices under local laws (e.g., NYC Local Law 144, Illinois AIPA, California CPRA)
AI Insurance Coverage / Language
AI insurance has taken a few forms - silent, affirmative, tech E&O, Cyber. There is not a consensus in the market, and that relates to two issues -
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The risk is not understood
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The fact that AI is a diverse concept/practice
CFC (Lloyds of London)
"Technology services" means the supply by you or on your behalf of technology products or technology services, including but not limited to software development, software installation and maintenance, hardware design, hardware installation and maintenance, artificial intelligence development, artificial intelligence services, data processing, internet services, data and application hosting, computer systems analysis, consulting, training, programming, systems integration, IT support and network management
Vouch (State National)
"Technology Services means any of the following services that You perform, including services You perform through any computer-based system that uses artificial intelligence algorithms, for others for a fee, service or other remuneration: analyzing, designing, developing, supporting, repairing, implementing, selling, operating, programming, installing, or advising on, any computer or electronic system, network, hardware, software or component or wireless application; managing, operating, administering or hosting any technology, cloud computing, computer system, database or network; storing, warehousing, mining, processing, collecting, compiling or analyzing data; or website hosting or development."
Coalition (Cyber)
The following is added to the definition of “security failure” DEFINITIONS: Security failure includes an AI security event, which results in:
1. acquisition, access, theft, or disclosure of personally identifiable information or third party corporate information in your care, custody, or control and for which you are legally liable;
2. loss, alteration, corruption, or damage to software, applications, or electronic data existing in computer systems;
3. transmission of malicious code from computer systems to third party computer systems that are not owned, operated, or controlled by the named insured or subsidiary;
4. a denial of service attack on the named insured's or subsidiary's computer systems; or
5. access to or use of computer systems in a manner that is not authorized by you, including when resulting from the theft of a password
2. The following definition is added to Definitions:
AI security event means the failure of security of computer systems caused by any artificial intelligence technology, including through the use of machine learning or prompt injection exploits.
3. The following is added to the definition of “data breach” to Definitions:
Data breach includes the acquisition, access, theft, or disclosure of personally identifiable information or third party corporate information, that is unauthorized by you, resulting from an AI security event.
Coalition (Cyber)
The following is added to the definition of “security failure” DEFINITIONS: Security failure includes an AI security event, which results in:
1. acquisition, access, theft, or disclosure of personally identifiable information or third party corporate information in your care, custody, or control and for which you are legally liable;
2. loss, alteration, corruption, or damage to software, applications, or electronic data existing in computer systems;
3. transmission of malicious code from computer systems to third party computer systems that are not owned, operated, or controlled by the named insured or subsidiary;
4. a denial of service attack on the named insured's or subsidiary's computer systems; or
5. access to or use of computer systems in a manner that is not authorized by you, including when resulting from the theft of a password
2. The following definition is added to Definitions:
AI security event means the failure of security of computer systems caused by any artificial intelligence technology, including through the use of machine learning or prompt injection exploits.
3. The following is added to the definition of “data breach” to Definitions:
Data breach includes the acquisition, access, theft, or disclosure of personally identifiable information or third party corporate information, that is unauthorized by you, resulting from an AI security event.
URM (Attorneys and Brokers)
While it is possible for clients to explore all solutions, one insurer at a time, we access all markets. We have relationships with RT Specialty, AmWins, and Citadel to access teh coverage from all carriers.