Silent AI and the first mega-claim insurers didn't see coming

When AI quietly goes wrong: Why "silent AI" is the next big insurance shock

AI is being wired into underwriting, claims, and client service at record speed. The risk story isn't keeping up. That gap is where the first "silent AI" mega-claim is likely to hit - a loss triggered by AI that no one priced, underwrote, or excluded.

What "silent AI" actually means

Silent AI exposure shows up when policies never designed for AI are asked to respond to AI-driven losses. Think a PI wording that covers "professional services," written before generative tools existed, now facing a negligence suit after an employee relied on an AI tool that fabricated facts.

We've already seen the caution flag. In Mata v. Avianca, lawyers filed a brief with AI-invented case citations and were sanctioned - a clean example of AI-fueled professional error that could spill into PI claims in any market. Case docket (CourtListener)

Where the first big coverage fight could land

• Professional indemnity: AI-assisted advice, drafting, or research that's wrong or defamatory.
• Product liability: AI-enabled software in a product yields unsafe outputs or decisions.
• Tech E&O vs. PI overlap: Third-party AI vendors cause loss; insured relied on them.
• Cyber spillover: Prompt leaks, model misuse, or data exposure that don't fit classic "security failure."

The carrier risk: one test case crystallizes an entire class of unpriced exposure, forcing rushed exclusions and tense renewals.

Broker playbook: run an AI fact-find now

Start with the basics: how is the client actually using AI today - formally and informally? Shadow AI is everywhere. Your job is to make it visible and controllable.

• Usage map: Which teams use AI? For what tasks? Internal vs. vendor tools?
• Data flows: Any personal, health, payment, or confidential data going into prompts or training?
• Controls: Written policy on acceptable use, approvals, and human review for critical outputs?
• Vendor risk: Due diligence on providers, SLAs, indemnities, data residency, model updates?
• Audit trail: Logging of prompts, outputs, and decisions tied to case IDs or files?
• Testing: Red-team scenarios, hallucination checks, regression tests after updates.
• Access: Role-based permissions, API keys, and kill-switch for faulty models.
• Training: Staff guidance on safe prompts, privacy, and disclosure of AI assistance.

If a client isn't using AI, document that for the market. If they are, evidence discipline. That can support better pricing and fend off blunt exclusions.

Governance that improves insurability

• Clear AI use policy with approvals for high-impact tasks (advice, underwriting, pricing, claims decisions).
• Human-in-the-loop checkpoints for any output that could bind cover or affect outcomes.
• Data minimization: block sensitive data in public tools; use vetted enterprise versions.
• Model change control: track versions, update notes, and rollback plans.
• Bias and fairness tests where decisions affect customers or employees.
• Incident process for AI-caused errors, including notification and remediation.

For a common standard, the NIST AI Risk Management Framework is a practical reference for controls and evidence.

Coverage checkpoints to review now

• PI definitions: Do "professional services" and "advice" clearly capture AI-assisted work?
• Tech E&O triggers: Include failures in third-party models, APIs, and automation tools.
• Cyber vs. PI: Clarify "security failure" vs. bad AI output - avoid gaps and finger-pointing.
• Bodily injury/property damage: AI-driven systems (e.g., IoT) creating physical harm - which policy responds?
• Contractual liability: Vendor terms pushing AI risk onto the insured - check carve-backs.
• IP/defamation/privacy: AI-generated content risk; ensure the right insuring clauses exist.
• New exclusions: Watch for sweeping "AI exclusions" that nuke core cover unintentionally.
• Conditions: "Use of approved tools only," logging, and human review - if required, make it achievable.

Underwriting and pricing: questions to ask insureds

• Materiality: Which revenue-critical or high-exposure processes use AI?
• Decision rights: Can AI bind cover, deny claims, or issue final decisions?
• Testing rigor: Pre-deployment validation, scenario testing, and ongoing monitoring metrics.
• Update cadence: How often models change; who signs off; rollback readiness.
• Vendor stack: Concentration risk, indemnities, and evidence of provider security.
• Loss history: Near-misses or incidents tied to AI outputs or automations.
• Evidence pack: Policies, logs, training records, and sample review workflows.

Avoid blunt exclusions - use precision

• Prefer endorsements over blanket exclusions; tie requirements to real controls.
• Use sub-limits, higher deductibles, or co-insurance for high-uncertainty AI use cases.
• Consider warranties on specific safeguards (e.g., human review before binding or denial).

Scenarios to model before renewal

• Professional services: AI drafts with fabricated citations; client sues for negligence.
• Financial advice: Model error misallocates funds or mis-scores credit risk.
• Claims automation: AI wrongfully denies claims at scale; class action follows.
• Underwriting: Pricing model skews; adverse selection blows through a treaty.
• Chatbots: "Advice" interpreted as binding; regulatory action plus civil liability.
• Content risks: AI generates defamatory or infringing material.
• Privacy: Staff paste personal data into a public chatbot; data leak investigation and fines.

Accumulation and correlation

AI is concentrated risk. A single vendor update can degrade outputs across thousands of insureds on the same day. Carriers and reinsurers should stress-test correlated loss across PI, tech E&O, and cyber - triggered by one shared model or API.

What to do this quarter

• Brokers: add an AI fact-find to every renewal and new business submission.
• Insureds: issue an acceptable-use policy, enable logging, and require human review for key decisions.
• Underwriters: refresh PI/tech E&O/cyber wordings for AI clarity and avoid overlap gaps.
• Claims: prepare guidance for AI-related notifications; capture model/vendor details early.
• Risk engineers: publish a one-page AI control baseline clients can actually meet.
• Leadership: set a stance on exclusions vs. endorsements; align pricing signals with controls.

The first silent AI mega-claim isn't a thought experiment. AI use is surging, and risk discipline is lagging. Make AI usage visible, tighten controls, and tune wordings before a test case forces your hand.

If your clients need quick upskilling on safe AI use at work, point them to curated training libraries like Complete AI Training for practical courses teams can implement fast.