AI GRC FOR INSURANCE & CYBER POLICY PROVIDERS
Securely Underwrite Cyber Risk.
Automate Compliance. Prevent Model Failure.
TestSavant.ai is the unified GRC platform for MGAs, insurtechs, and program administrators. We provide autonomous Red Teaming for underwriting models, adaptive Guardrails for policy workflows, and audit-ready evidence for regulators.
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Reduction in Underwriting Errors
Prevent costly model failures and inaccurate risk assessments for Tech E&O and Cyber policies.
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Improvement in Model Fairness
Continuously test underwriting models for proxy discrimination to ensure equitable and compliant pricing.
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Acceleration of NAIC Audits
Automatically generate governance documentation mapped to the NAIC Model Bulletin and other key frameworks.
Solution Bundles for Policy Providers
Turnkey solutions to secure your most critical AI-driven insurance workflows.
Cyber Underwriting & Tech E&O
- Secure analysis of security reports (SOC 2, penetration tests).
- Prevent hallucinations of coverage for emerging cyber threats.
- Guardrails to ensure fair and unbiased risk classification.
Claims & SIU Automation
- Automated PII/PHI redaction from claims documentation.
- Prevent incorrect coverage determinations and bad-faith risks.
- Detect and flag sophisticated patterns of claims fraud (SIU).
Distribution & Broker Enablement
- Secure RAG over policy documents and underwriting manuals.
- Prevent assistants from providing unauthorized binding advice.
- Enforce brand safety and regulatory disclaimers in all outputs.
Actuarial & Model Risk (SR 11-7)
- Independent validation and testing for pricing models.
- Continuous monitoring for model drift and performance degradation.
- Audit-ready evidence for SR 11-7 and internal governance.
From Threat Model to Enforced Control
How our platform translates specific insurance AI risks into automated, auditable defenses.
| Threat / Failure Mode | Guardrail Decision (UGM + Nero) | Test Methodology (Coliseum) | Result |
|---|---|---|---|
| AI misinterprets a client's security report in cyber underwriting. | Require citations from source documents; route ambiguous interpretations to a human underwriter. | Test with deliberately confusing or contradictory security documentation. | Reduced underwriting errors; auditable review trail. |
| AI hallucinates coverage for a new, undefined cyber threat. | Strictly ground all responses in the official policy wording; block any speculative or non-document-based statements. | Prompt suites designed to elicit opinions on emerging, uncovered risks. | Lowered risk of policy misrepresentation and bad faith claims. |
| Underwriting model shows bias against certain industries. | Run continuous fairness diagnostics; require human review for decisions affecting protected classes. | Analyze model outputs against fairness metrics (e.g., disparate impact). | Provably fair and compliant underwriting; reduced regulatory risk. |
| Prompt injection via claims documentation leads to data exfiltration. | Multi-layer injection detection; sanitize and deny malicious inputs; quarantine suspicious documents. | Upload documents with hidden instructions (indirect injection attacks). | Blocked takeover attempts; documented security events. |
Evidence-Based Compliance for Insurance Regulators
Produce concrete artifacts mapped to the frameworks auditors and regulators scrutinize.
NAIC AI Model Bulletin
- ✓Fairness & Non-Discrimination: Automated bias and fairness testing for underwriting and claims models, with reports showing equitable outcomes.
- ✓Governance & Accountability: A complete, version-controlled AI Systems Program, including risk management policies, stakeholder roles (RACI), and an immutable audit trail of all model changes and decisions.
SR 11-7 (Model Risk Management)
- ✓Independent Validation: Automated red teaming and challenger model comparisons to provide robust, independent validation of pricing and underwriting models.
- ✓Ongoing Monitoring: Real-time dashboards to track model performance, data drift, and stability, with automated alerts for breaches of risk thresholds.
NIST AI RMF 1.0
- ✓Govern, Map, Measure, Manage: Comprehensive risk registers for all AI systems, detailed adversarial test results, continuous monitoring dashboards, and incident response workflows.
ISO 42001 (AIMS) & 23894 (Risk)
- ✓AI Management System: A full Plan-Do-Check-Act (PDCA) lifecycle management system for your AI, with documented policies, objectives, and management review outputs.
- ✓Risk Lifecycle Management: A structured process for identifying, analyzing, treating, and monitoring AI-specific risks like data privacy, fairness, and safety.
Frequently Asked Questions
How do you help underwrite the risk of AI companies themselves?
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Our platform is uniquely suited to analyze the AI-specific risks of potential clients. We can run our Red Teaming engine (Coliseum) against an applicant's AI products to identify vulnerabilities, providing your underwriters with a quantitative, evidence-based assessment of their actual tech E&O and cyber risk posture.
How do you stop hallucinated coverage language?
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Our UGM guardrails enforce strict "Retrieval-Augmented Generation" (RAG) that is grounded only in your authoritative policy documents. If the AI cannot find a direct citation for a coverage statement, the response is blocked or rewritten with a mandatory "cannot determine coverage" disclaimer and escalated to a human expert.
How do you protect PII in claims and underwriting workflows?
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We perform entity detection and field-level redaction of Personally Identifiable Information (PII) before any data is sent to an external model. We also provide options for zero-retention policies and data residency routing to meet specific compliance needs.
Move Faster Than the Risk
Schedule a confidential briefing to see how our platform provides the evidence and control needed to innovate safely, underwrite accurately, and satisfy insurance regulators.
