AI Security for Insurance

AI Security for insurance refers to the practices and technologies that protect generative and analytical AI systems, used in underwriting, claims, fraud detection and customer service, from unique risks such as data leakage, prompt injection, hallucinations, model poisoning and shadow AI. Unlike traditional cybersecurity, which defends deterministic systems, AI security must monitor model behavior, prompts and outputs in real time and enforce data‑handling policies to safeguard sensitive PII/PHI.

AI dramatically accelerates underwriting accuracy, streamlines claims processing and strengthens fraud detection. However, these systems process sensitive policyholder data and make high‑impact decisions; misconfigurations or attacks can expose personal information, hallucinate non‑existent policy clauses or let adversaries manipulate outcomes. Securing the AI layer ensures you reap the efficiency gains without incurring customer harm or regulatory backlash.

Key risks include:
Data leaks: Prompts or model outputs inadvertently exposing policyholder PII/PHI


Hallucinations and bias: LLMs fabricating reasons for claim denials or producing discriminatory pricing


Prompt injection and adversarial inputs: attackers hiding malicious instructions in claims documents or emails, causing data exfiltration or unauthorized policy changes


Model leakage, poisoning and drift: stealing model weights, corrupting training data or letting performance degrade over time


Shadow AI and vendor risk: unsanctioned use of third‑party AI tools or insecure vendors that bypass corporate controls

Classic insurance cybersecurity tools (WAFs, DLP, IAM) protect networks and endpoints but can’t inspect dynamic prompts, vector embeddings or model outputs. Generative and agentic AI introduce a novel attack surface: prompts act as code, models retain training data, and outputs can hallucinate sensitive information. AI security platforms provide runtime visibility, prompt filtering, data masking and model‑behavior monitoring that legacy tools lack.

Regulators such as the NAIC, New York Department of Financial Services and state privacy laws require insurers to safeguard sensitive data, ensure model fairness and maintain detailed audit trails. AI security platforms enforce policies (e.g., no PII in training data), monitor model decisions for bias, record every AI action and align with standards like HIPAA, GDPR and CCPA. This reduces the risk of penalties, litigation and reputational damage.

AI itself cuts costs and boosts revenue by improving fraud detection, speeding claims and enhancing underwriting accuracy. AI security protects those gains: it prevents data breaches and regulatory fines, reduces false positives/negatives in fraud models, preserves customer trust and accelerates AI approvals. Securing AI allows insurers to innovate faster while maintaining a competitive loss ratio and meeting compliance obligations.

Traditional insurance cybersecurity protects networks, applications and data, but cannot interpret prompts, model outputs, embeddings or AI-driven decisions.
 AI Security for Insurance adds model-aware controls that detect prompt injection, stop policyholder data leakage, validate AI-generated decisions and secure underwriting, claims and fraud models in real time.

Underwriting AI processes extremely sensitive PII/PHI, medical histories, financial data, telematics inputs and third-party risk signals. Without AI-layer security, errors, bias, hallucinated explanations or adversarial manipulation can lead to unfair pricing, regulatory reviews and higher loss ratios.

Insurers need runtime monitoring, prompt/output filtering, redaction and policy-as-code controls that block PII/PHI from entering prompts, embeddings or responses. Levo’s AI Threat Protection enforces these controls automatically across claims AI, underwriting models and customer-facing LLMs.

AI security platforms generate the audit trails, model-level visibility, access logs and decision explanations regulators increasingly expect. Insurers use Levo to demonstrate responsible AI use, enforce guardrails and prevent non-compliant data handling across underwriting, claims, fraud and support workflows.

Fraud rings increasingly use adversarial inputs, deepfake documents, synthetic IDs and prompt injection to bypass AI-based fraud models. Levo detects abnormal behavior, poisoned signals, suspicious document patterns and model drift that could weaken fraud detection accuracy over time.

Customer-facing LLMs are vulnerable to prompt injection, misinformation, hallucinated policy guidance and data exposure. Levo applies real-time monitoring and guardrails to ensure AI agents provide accurate, compliant and safe coverage advice across every interaction.

AI security should be implemented before models go into production, during experimentation, tuning and integration phases. However, Levo can also secure systems already in production by providing visibility, enforcement and red-teaming without rebuilding your stack.

AI security reduces regulatory risk, prevents costly data-leak incidents, lowers fraud losses and accelerates approval for new AI initiatives. Insurers with Levo ship AI faster, reduce manual oversight costs and maintain strong policyholder trust as they scale automation.

Many carriers rely on external AI vendors for OCR, fraud scoring, claims automation, or chatbots, but lack visibility into what these systems actually do. Levo provides centralized monitoring, access governance and policy enforcement across third-party AI tools, reducing supply chain exposure.

Begin with model inventory and visibility, set policies for PII/PHI handling, implement runtime guardrails, continuously monitor for drift and conduct ongoing AI red-teaming. Levo consolidates all of these requirements into one platform so insurers can scale AI responsibly without slowing innovation.