AI Security & Red Teaming for Insurance
Insurers are deploying AI where the money and the data are: claims intake, fraud scoring, underwriting assistants, and customer chatbots holding policyholder PII. Every one of those is an attack surface. A manipulated claims bot pays fraudulent claims, a leaky underwriting copilot exposes nonpublic personal information with state privacy and DOI consequences, and a gamed fraud model waves organized rings through. AI security red teaming for insurance is adversarial testing against exactly those failures: we attack your AI systems the way fraudsters and data thieves will, document what breaks, and help you fix it before a market conduct exam or breach notification makes it public.
AI Security & Red Teaming, built for insurance
We map the AI attack surface across claims, underwriting, servicing, and fraud systems, prioritizing anything handling PII or influencing payments.
We execute adversarial campaigns: prompt injection, social-engineering-style manipulation, data extraction, and systematic probing of fraud model blind spots.
Findings arrive as evidence, with reproduction steps, business impact in claims dollars and data exposure, and remediation guidance your engineers can execute.
We align results to your NAIC-driven AI governance program, giving your risk function documented testing evidence regulators increasingly expect.
Where it pays off in insurance
Claims automation abuse testing
Attempting to manipulate intake bots and straight-through processing into paying claims that should never clear.
Fraud model evasion
Probing whether crafted claim patterns slip past your fraud scoring, before organized rings find the same gaps.
PII leakage assessment
Testing whether chatbots and copilots can be induced to reveal policyholder data across sessions, users, or permission boundaries.
Underwriting assistant testing
Adversarial evaluation of AI underwriting support, including injection through broker-submitted documents.
Carriers typically uncover exploitable paths to improper payment or data exposure in the first assessment, close them within weeks, and gain a documented adversarial testing record that strengthens both security posture and regulatory standing.
Insurance AI, answered
Traditional pentests target infrastructure, and these failures live in model behavior. Prompt injection, fraud model evasion, and cross-boundary data leakage do not show up in a network scan. The attack surface is the AI itself, and it needs adversaries who specialize in it.
We simulate their methods: systematic probing of thresholds, document manipulation, and pattern variation at scale. The goal is finding which synthetic strategies your models miss, so your SIU hardens the model before real rings industrialize the same discovery.
No. Engagements run under strict rules with your security team, using test data and controlled environments wherever possible. When production-adjacent testing is necessary, scope and data handling are agreed in writing first, and everything we touch is logged.
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