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Industries/Insurance
Insurance  ·  AI Governance Policies

AI underwriting and claims AI need more than guardrails. They need policy enforcement.

Actuarial models, claims adjudication agents, and fraud detection systems access the most sensitive data in your organization. AutoPIL enforces access boundaries at the retrieval layer and produces the audit trail that state insurance regulators require.

AI Agent Policies — Insurance

10 policies across underwriting, claims management, and fraud & compliance — designed for the strict data segregation requirements of P&C, life, and specialty carriers operating under state DOI and Solvency II frameworks.

underwriting.yaml4 roles
claims_management.yaml3 roles
fraud_compliance.yaml3 roles
All 10 agent roles
underwriting_analystrisk_assessoractuarial_agentpricing_agentclaims_adjusterclaims_investigatorsubrogation_agentfraud_detection_agentcompliance_officeraudit_trail_agent
policies/insurance/underwriting.yaml
policies:
  # Underwriting analyst — risk profile and applicant data; blocked from claims history and competitor pricing
  - name: underwriting_analyst_policy
    agent_role: underwriting_analyst
    allowed_sources:
      - risk_profile_data
      - applicant_records
      - actuarial_tables
      - property_valuations
      - credit_reports
    denied_sources:
      - claims_history
      - competitor_pricing
      - internal_risk_models
    allowed_tasks:
      - risk_assessment
      - policy_quoting
      - eligibility_determination
    denied_tasks:
      - claims_settlement
      - fraud_investigation
    max_sensitivity: high
Applicable Regulations — Insurance
Where AI governance breaks down in Insurance
Actuarial data isolation
Underwriting AI accessing claims history from other lines of business creates adverse selection risk and regulatory exposure. AutoPIL enforces the boundary at the source.
Claims data segregation
Fraud detection agents need claims data — but not policyholder PII. AutoPIL enforces that distinction automatically, at the retrieval layer, before data reaches any model.
State regulator audit readiness
Insurance departments in NY, CA, and FL require demonstrable AI governance. AutoPIL's cryptographic audit chain is designed to survive regulatory review.
How to use

One path.
Your industry loaded.

Point policy_path at your industry directory. AutoPIL loads every YAML file recursively — roles, sensitivity rules, and process groups wired up automatically from the directory structure. No additional config needed.

Policies are hot-reloaded at runtime. Extend or override any pre-built policy via the REST API without redeploying your agents or restarting services.

setup.py 
from autopil import ContextGuard

# Point at your industry — only those policies load
guard = ContextGuard(
    policy_path="policies/insurance/",
    audit_db="autopil.db",
)

# policies/insurance/ — loads recursively
# Switch verticals by changing the path — nothing else changes.

Start with Insurance.
Extend from there.

Pre-built policies for insurance are included in every AutoPIL trial. Extend or override any rule via the REST API without redeploying.

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