Solutions · Insurance
Audit evidence in minutes,
not a quarterly cycle.
Claims and underwriting AI is already writing decisions in production. Internal audit reviews a sample long after the fact. Citizen-developer AI tools spread faster than the review cycle can track them, and lines of business that must stay on separate regulated data planes still need one unified oversight view. Quadrazene governs every AI action as it happens and hands the auditor a re-verifiable evidence pack instead of a reconstruction project.
Today
What we see in the field.
AI already writes claims decisions, unaudited
Agents and copilots evaluate and route claims in production today. Internal audit finds out what happened through a sample review, weeks or months after the action.
Citizen-developer AI spreads faster than review cycles
Business users stand up AI-assisted tools outside the formal inventory. The audit function learns about them at the next scheduled review, if at all.
Evidence packs take weeks to assemble
A regulator or examiner request means pulling logs from several AI platforms and stitching a defensible timeline together by hand.
Lines of business must stay isolated, but oversight can't
Property & casualty and other regulated lines run on separate data planes by requirement. Audit leadership still needs one view across all of them.
Process mining finds the deviation; nothing governs the action
Flow-level anomalies get flagged by process-mining tools, but the individual write that caused the deviation isn't controlled or consistently logged.
Segregation of duties is checked quarterly
The same person who tunes a model can also approve what it recommends, and no one notices until the next scheduled review finds it.
Regulatory context
The frameworks you already operate against.
Quadrazene's policy engine and audit ledger are framework-shaped. Specific control mappings available under NDA.
NAIC AI governance model bulletin
Third-party and internally built AI systems require a documented governance program. Continuous in-band monitoring maps directly onto the expected control set.
State insurance AI circular letters
A growing set of states expect AI use disclosures and testing for disparate impact. Evidence collects as the systems run, not at filing time.
GIAS-aligned internal audit standards
Global Internal Audit Standards for AI processes expect continuous risk assessment, not point-in-time sampling.
SOX (public insurers)
Financial-reporting-relevant AI actions carry the same audit-chain requirement as any other financially material system change.
HIPAA (health lines)
Where health-adjacent lines are in scope, PHI handling routes through the same classification cap and Trust Layer as any other restricted System.
State privacy statutes
Policyholder and claimant data access is scoped by policy at the data layer, consistent across every line of business.
How the four Engines compose
Governance covers every AI action. Insights unifies the view across isolated lines.
Audit leadership asks plain-English questions across AI action logs from every platform and line of business, in one cited view — without the underlying data planes ever merging.
Example Skills: AI-action coverage by platform, citizen-developer tool inventory drift, SoD-conflict trend, evidence-pack turnaround time.
Risk-scored recommendations on which AI actions warrant the auditor's attention first, with the policy match and model trace attached so the finding is defensible on its own.
Example Skills: AI-action risk scoring, control-gap mapping, citizen-developer risk classification, cross-line SoD analysis.
Four enforcement modes: in-band on every governed action, detective scanning over external AI platforms, preventive-by-configuration on grants and guardrails, and preventive-by-interposition at the model-routing layer.
Example Skills: Continuous SoD monitoring, citizen-developer scanning, cross-line data-plane isolation, before→after policy-change diff.
Findings escalate into tracked, owned action items with the evidence attached. Access revocations, control-gap tickets, and evidence-pack exports all write to the audit-management system of record.
Example Skills: Escalate finding to audit-management system, revoke access on SoD conflict, export evidence pack, notify control owner.
See it on real surfaces
Two walkthroughs that show the insurance-audit shape.
Continuous evidence pack (access-review shape)
The quarterly-access-review walkthrough shows drift surfacing between cycles and an auditor-ready evidence pack assembling automatically. The same shape reuses for continuous AI-action audit coverage.
Start walkthrough →SoD correlation → revoke (claims governance shape)
The failed-login-correlation walkthrough shows two related actions by the same actor triggering an automatic HITL-gated revocation. The same pattern reuses for claims-side segregation-of-duties conflicts.
Start walkthrough →Platform surfaces that matter most
Where the insurance-audit work actually happens.
Connections
Register each AI platform and each line-of-business data plane with its classification. Isolation enforced everywhere.
Governance
Continuous SoD monitoring and citizen-developer scanning, editable inline with before→after audit diffs.
Risk
Composite risk score on every AI action; the auditor sees why a finding is high-risk, not just that it is.
Inbox · HITL
Control-owner sign-off and revocation approvals route here, SLA-bound.
Records
Hash-chained audit trail. Evidence pack exports as re-verifiable NDJSON for the regulator or examiner.
Models · routing
Restricted-line data pins to on-prem or approved models; the routing decision itself is a governed, logged action.
Security & compliance posture
The questions audit leadership and compliance will ask.
Cross-line isolation enforced at the data layer
Each regulated line of business stays on its own data plane. Audit leadership gets one dashboard without the underlying data ever merging.
Customer-installable
Run on your VM, your Kubernetes, or air-gapped. AI platform credentials never leave your perimeter.
Hash-chained audit for every governed AI action
Every finding, revocation, and evidence-pack export is one immutable row. The regulator can re-verify offline.
BYOK with FIPS-validated KMS
Per-tenant DEKs wrapped by a CMK in your KMS. FIPS 140-3 path supported.
IdP and SSO with per-line roles
Your Entra or AD FS is the source of truth. SCIM 2.0 provisioning. Role assignments respect the line-of-business boundary automatically.
Detective monitoring with zero changes to monitored tools
External AI platforms get scanned continuously without requiring an integration project or a change to how the platform team already works.
What changes
Our gut feel for where the wins land.
Qualitative reads from the demos we've run. The shape of the change, not the size. We won't quote customer numbers we haven't measured.
Every AI action carries an audit-chain entry
Coverage starts from day one instead of building up sample by sample across review cycles.
Citizen-developer tools get scanned continuously
New AI tools surface as they appear instead of at the next scheduled inventory review.
Evidence packs assemble in minutes
A regulator or examiner request pulls from the ledger instead of triggering a multi-week reconstruction project.
Lines of business stay isolated under one view
Audit leadership sees across every line without the underlying regulated data planes ever merging.
SoD conflicts surface continuously
The gap between a quarterly review and the violation it would have caught closes to hours.
Process-mining deviations get a governed action
A flow-level anomaly and the write that caused it show up in the same audit row instead of two disconnected systems.
Where to start
Our recommended first phase for an insurance-audit pilot.
- 1.Connect one AI platform's action log, read-only. Tag its classification and line of business.
- 2.Run an AI-action coverage question through Insights. Show audit leadership the first cited answer.
- 3.Bond Governance. Turn on continuous SoD monitoring for one control; watch the first finding land.
- 4.Run the evidence-pack walkthrough. Hand the exported NDJSON to a reviewer and have them re-verify it offline.
- 5.Add a second line of business on its own data plane; confirm the dashboard unifies without the data merging.
Bring your internal audit team.
We’ll walk continuous SoD monitoring, citizen-developer scanning, and the evidence-pack export on a sample of your AI action logs.