Affirmative AI Insurance: Why It Matters, What It Covers, and What Boards Need to Know
- Steven Barge-Siever, Esq.
- May 29
- 8 min read
Updated: Jun 3
By Steven Barge-Siever, Esq.
Founder | Upward Risk Management
AI Risk Is Front and Center in 2025. So are New AI Insurance Solutions.
Bias, hallucinations, unauthorized training data, and autonomous decisions are already triggering real claims.
As AI transforms how companies operate, it’s also forcing insurers to rethink how coverage applies. New solutions are emerging, but clarity has not.
Some carriers are introducing Affirmative AI coverage - clear, written language that defines how Tech E&O and Cyber policies apply when artificial intelligence causes harm. Some rely on Silent AI coverage, and still others are introducing AI exclusions.
Focus on AI insurance coverage is a welcome movement. But it also raises real questions about scope, strategy, and buyer expectations.
In this article, we explore:
What affirmative AI insurance is
How it differs from “silent” coverage
What insurers are offering today
Why boards and brokers need to read the fine print
How to avoid the illusion of coverage
What Is Affirmative AI Insurance?
Affirmative AI insurance refers to explicit policy language - whether in the base form or added by endorsement - that clearly defines how insurance applies to risks arising from artificial intelligence.
Insurers offering Affirmative AI include direct references to algorithms, LLMs, machine learning models, or autonomous systems, often with accompanying definitions, conditions, or sublimits.
This is different from silent AI coverage, where the policy doesn’t mention AI at all, but the buyer assumes (or hopes) that AI-related incidents will be covered under more general policy terms - typically through the definition of “professional services.”
For example:
Silent AI Coverage: A Tech E&O policy may not mention AI, but if your company’s services include software development, analytics, or platform delivery - and AI tools are part of how you perform those services - coverage may still apply under the policy’s general definition of “professional services.”
Affirmative AI Coverage: The policy directly states that AI-related tools, models, or services are covered - often including specific definitions and terms like: computer-based services or systems that use AI algorithms, provided for others for a fee.
A Strategic Tradeoff: Affirmative AI vs. Silent Coverage
Silent coverage can offer broader protection when no exclusions apply - but it also creates uncertainty for buyers and risk managers.
Affirmative AI endorsements add clarity, but may limit coverage to only what’s listed - sometimes unintentionally.
There’s no universal answer for how to insure AI.
Carriers are testing different approaches. Brokers interpret them differently. Legal, underwriting, and operational teams rarely agree. That’s because AI risk isn’t one-size-fits-all, and neither is the coverage.
The comparison below highlights the core structural differences, helping companies align insurance strategy with how they build, deploy, or rely on AI:
Affirmative AI Coverage | Silent AI Coverage | |
Definition | Explicit language in the policy or endorsement covering AI-related risks | No mention of AI - coverage depends on how broadly the base policy is interpreted |
Clarity | Clear definitions, conditions, or sublimits tied to AI tools or systems | Ambiguous - may apply by default if no exclusion exists |
Defense Triggers | May limit defense to named perils or specified AI tools | Broader defense duty if policy is silent and ambiguous |
Coverage Scope | Defined - but potentially narrow (e.g., LLMs but not vision systems) | Flexible - can evolve with product without needing policy changes |
Underwriting Appeal | Some carriers prefer - allows pricing, sublimits, and risk containment | Seen as riskier by underwriters, harder to rate and contain evolving models |
Policyholder Risk | May deny claims outside the defined AI scope | Uncertainty during claims |
Legal Leverage | Less interpretive wiggle room if claim falls outside named use cases | Courts may side with insured if wording is vague (contra proferentem) |
Pros of Each Approach: Affirmative AI vs. Silent AI Coverage
Affirmative AI Coverage
Clear scope of coverage tied to specific AI tools or services
Helps meet enterprise client requirements for named AI protections
Demonstrates governance maturity to investors and auditors
Easier for carriers to rate, price, and underwrite
Silent AI Coverage
Flexibility as AI tools evolve without needing policy changes
Potential for broader defense duties due to ambiguity
May avoid restrictive sublimits tied to narrowly defined AI endorsements
Can be favorable when “professional services” are broadly defined
Takeaway: Affirmative coverage reduces ambiguity - but may limit scope. Silent coverage allows flexibility - but increases uncertainty.
Why Affirmative Coverage May Be Safer
While silent coverage can be broader in theory, affirmative AI language often provides better real-world protection - especially when stakes are high or contractual clarity is required.
Affirmative coverage can be preferable when:
The company sells to enterprise clients who require proof of AI-specific insurance
There’s a history of claims or potential AI-driven loss scenarios (e.g., bias, hallucinations, system failure) - coverage is expensive and carriers limited in this scenario.
Investors, boards, or regulators are conducting due diligence and want defined protections
The company is using AI tools with defined risk classes (e.g., autonomous systems, LLMs, vision models)
In these cases, vagueness creates friction. Affirmative endorsements - when drafted well - can satisfy procurement teams, speed up deal cycles, and prevent coverage denials.
Silence can be broad, but affirmative language is what underwriters, legal teams, and enterprise partners can rely on.
The Other View: Silence Can Be Stronger
While affirmative endorsements clarify intent, some argue they come at a cost:
“When you name a peril, you define a peril.”
This legal principle suggests that naming specific risks may create an interpretive ceiling. If the claim doesn’t neatly fit within the listed AI perils, the carrier may deny coverage - arguing it falls outside the defined scope.
How Silent AI Insurance Coverage Functions
In most Tech E&O policies, coverage applies to “professional services” - typically defined as technology-related services provided to others for a fee. If AI outputs (e.g., decisions, analysis, recommendations) are part of that service, and there’s no specific exclusion, coverage may apply even without naming AI directly.
Key Trigger: Definition of Professional Services - Most Tech E&O policies define “professional services” broadly - covering software development, data analytics, system design, and advisory work provided for a fee. If AI tools are embedded in what your company delivers to clients, they may be covered under this umbrella - even if the policy never mentions AI explicitly.
When well-structured, silent coverage can:
Trigger broader duty to defend - Courts often interpret ambiguity in favor of the insured.
The problem here, of course, is the possibility of having to fight for coverage in court, but this is also something that both carriers and insured look to avoid.
Avoid narrowing protection to predefined AI use cases.
Bypass sublimits - Full policy limits may apply if there’s no carve-out.
Preserve flexibility - Ideal for evolving AI products, use cases and risk profiles.
This flexibility can be a feature, and not a flaw, when the policy is silent but well-constructed.
Risks of Silent AI Coverage
Insurance coverage lives or dies by the wording. When AI isn't addressed, brokers, courts and carriers are left to interpret intent.
We’re already seeing AI-related claims surface across multiple domains:
Bias in automated decisions (e.g., hiring, lending, healthcare) (More here)
Privacy violations tied to training data
Copyright disputes involving generated content
Regulatory probes into algorithmic harm
Model performance failures leading to financial or reputational loss
Ambiguity creates room for denial. Especially when exclusions - for biometrics, product liability, or third-party platforms - quietly cut across coverage.
Legal and Regulatory Pressure Is Forcing the AI Insurance Issue to the Forefront
AI is no longer a tech issue. It’s a liability issue.
The SEC has signaled that AI-related misstatements in fundraising or IPO materials may constitute securities fraud. (See here)
The FTC has launched actions involving AI-enabled discrimination and deceptive design.
In Mobley v. Workday, the court allowed claims to proceed against the AI vendor - not the employer for algorithmic hiring bias. (See here)
Regulators, courts, and claimants are treating AI systems as insurable actors. That means insurance has to catch up - or risk being irrelevant when it counts.
What Insurers Are Doing Today
The market is moving - but not in sync. Several carriers are taking bold steps, while others remain cautious. Here’s how some of the leading approaches are unfolding:
Armilla AI: A new entrant backed by A-rated carriers offering both model risk evaluation and AI-specific liability coverage. Their dual approach combines underwriting with technical diligence - aiming to set a new benchmark for insuring algorithmic systems. (See here)
Coalition: One of the first to roll out a Cyber endorsement explicitly covering algorithmic bias, model failure, and other AI-related risks. (See here)
Relm Insurance: Launched a dedicated “AI Liability Solution” targeting gaps in traditional Tech E&O for AI-native companies. (See here)
Lloyd’s Syndicates: Some syndicates are experimenting with AI carve-backs and tailored language, but offerings remain limited - particularly for U.S.-based risks.
What’s available in the market today is fragmented, narrow, and in many cases, misunderstood by buyers and brokers.
A New Warning Sign: Affirmative Exclusions
While some insurers are rolling out endorsements that affirmatively cover AI, others are adding exclusions that affirmatively deny it.
This shift is telling.
If carriers are carving out AI from coverage, it’s because they know they’re exposed.
These exclusions are not subtle - they specifically call out AI tools, models, or outputs and state they’re not covered. This signals that insurers are seeing real risk (or real claims) and don’t want ambiguity.
For buyers, that means one thing: Read the AI clause closely - and/or ask your broker why there isn’t one.
The Biggest Risk? Thinking You're Covered When You’re Not.
What we are observing (and addressing):
Companies assuming Cyber covers AI failure - even though many Cyber forms exclude or limit third-party liability
Tech E&O policies that define “professional services” so narrowly that autonomous decisions don’t qualify
AI language that names LLM use but excludes computer vision or biometrics
Sublimits that silently reduce coverage to 10–25% of the policy limit
The issue isn’t that coverage doesn’t exist. It’s that most of it doesn’t do what buyers think it does.
What Boards Should Do Right Now
If your company builds, sells, or embeds AI - or even contracts with vendors who do - here’s what needs to happen:
Review your policy language. Not the summary. The actual form and all amendments.
Don’t assume silence means protection. Silence is a risk.
Push for clarity. Whether it’s an endorsement, carve-back, or revised definition - affirmative language, when done right, helps.
Work with someone who’s seen the market. Most brokers haven’t read these clauses. We’ve written some - and read all - of them.
Parallel Lessons from Cyber Insurance
This is not the first time we’ve seen this story.
In the early 2000s, companies assumed general liability or E&O covered cyber breaches. Then came the lawsuits. Then came exclusions. Then came sublimits. And finally, standalone Cyber policies.
Affirmative AI insurance is following the same trajectory.
Cyber (2000s) | AI (2020s) |
Silent coverage → patchwork endorsements | Silent coverage → AI clauses, sublimits, carve-outs |
Undefined terms like “breach” | Undefined terms like “hallucination” or “algorithmic output” |
Fragmented response | Fragmented AI underwriting now |
Market-wide standardization | Still years away for AI |
Those who got ahead of cyber are now prepared. The same applies here.
Our Take / Summary
We’ve reviewed the language. We’ve helped carriers write it. And we’ve negotiated custom AI coverage for startups and global platforms embedding LLMs, autonomous systems, and proprietary decision layers.
What we know today:
Affirmative AI insurance is here - in different forms, with different levels of value.
Most off-the-shelf coverage is partial, conditional, or misleading.
The real danger isn’t being uncovered. It’s thinking you’re covered - and finding out too late that you’re not.
Make sure to discuss in depth with your broker, whether you are an AI company, a company using AI, or developing AI in house - there is a different solution for each.
What Should You Do?
Ask These 5 Questions:
Does my current E&O policy define “professional services” to include automated tools?
Is there any AI insurance exclusion in the base policy?
Does the AI endorsement list specific tools, models, or activities - or does it stay broad?
If affirmatively added, is the AI endorsement subject to a sublimit?
Could the endorsement limit coverage by naming too narrowly?
Comments