Protect Underwriting and Claims AI From Discovery Through Agentic Runtime

Insurance carriers, brokers, and MGAs are adopting generative AI copilots, agents, and MCP-connected tools across underwriting, claims, fraud detection, customer service, and actuarial modeling faster than security teams can track. This exposes sensitive PII, claims, and health data to shadow AI, prompt injection, unauthorized agent actions, model vulnerabilities, operational disruption, and regulatory obligations guidance has not defined.

PointGuard AI provides a unified platform to discover, test, govern, and protect every AI application, agent, model, and MCP ecosystem insurers run, including vendor-connected tools flagged for weak authentication and audit gaps. Carriers gain a centralized AI inventory, continuous risk assessment, AI-native data protection, runtime guardrails, agent identity and access controls, behavioral drift monitoring, automated containment, and governance mapped to NAIC, state DOI, and GLBA. PointGuard AI helps insurers innovate confidently, reduce risk, simplify oversight as cybersecurity and third-party vendor risk top CRO priorities, backed by experience securing AI for carriers, brokers, and MGAs.
‍

Insurance Industry Solutions

Secure Vendor-Connected Claims and Policy Tools

Vendor-supplied agents and copilots connect to policy administration, claims, and rating systems through MCP servers that ship without enterprise authentication, letting a compromised integration read or alter claims data unnoticed.

PointGuard authenticates each agent via OAuth 2.0, enforces per-tool authorization, and inspects every MCP call.

Prove AI Governance to State Regulators

State insurance regulators now expect a documented accountability structure, validation records, and audit-ready evidence for every AI system, but insurers track that information in spreadsheets scattered across underwriting, claims, and IT teams.

Our unified AI inventory and compliance evidence map findings to OWASP, NIST AI RMF, and ISO 42001.

Close Gaps in AI Risk Frameworks

Underwriting models, chatbots, and MLOps environments proliferate across cloud and SaaS platforms with default settings, credential sprawl, and no consistent baseline, leaving carriers unable to confirm any system is hardened.

PointGuard AI applies zero-trust configuration baselines across Databricks, Azure, AWS, and Vertex, detecting misconfigurations and credential sprawl continuously.

Assess Risk in Third-Party AI Models

Many carriers build underwriting and claims tools on third-party AI components or fully outsourced models whose vendors refuse documentation, call outputs a black box, and update logic without notifying the insurer.

PointGuard AI scores every open-source model and MCP server on publisher trust, vulnerabilities, and license risk continuously.

Stop Claims and Health Data Leaks

Claims files, medical records, and policyholder PII now pass through chat prompts, model responses, and MCP tool arguments that traditional file-and-email DLP was never built to inspect, so sensitive data leaves unnoticed.

PointGuard AI delivers AI-native DLP across prompts and MCP calls, with detectors mapped to HIPAA, GLBA, and GDPR.

Prevent Chatbot Hallucinations and Injection Attacks

Customer-facing claims and policy chatbots retrieve documents, emails, and tool outputs that can hide injected instructions or trigger hallucinated answers, and static keyword filters cannot recognize adaptive, encoded, or multilingual attack techniques.

With PointGuard, prompts and responses are inspected by fine-tuned models, blocking injection and unsafe outputs at sub-100 ms.

Continuously Test Models for Adverse Outcomes

Underwriting and pricing models change every release while new manipulation techniques emerge weekly, so a one-time red-team review at launch says nothing about whether the model in production today still behaves safely.

PointGuard AI runs continuous, automated adversarial testing against models and agents, retriggered whenever prompts, tools, or models change.

Monitor Underwriting and Claims Agent Drift

Underwriting and claims agents make thousands of autonomous decisions, and a model that quietly drifts toward discriminatory outcomes or erratic behavior looks identical to a healthy one until the pattern surfaces.

PointGuard's cumulative trust score per agent tracks goal drift and scope violations with full telemetry.
‍

Test Underwriting Algorithms for Unfair Bias

Life, auto, and health underwriting algorithms must be quantitatively tested for discriminatory outcomes by race, with methodology, assumptions, and results documented and ready for examiner review under Colorado and New York rules.

PointGuard AI generates customer-specific bias and fairness probes, testing underwriting models continuously against your regulatory and business requirements.

Texas Mutual Insurance Improves AI Risk Governance with PointGuard AI

Life Insurance Leader Secures Autonomous AI