Identity Fabric: Insurers' Bridge to Trustworthy Agentic AI

Could an Identity Fabric Edge Insurers Closer to Adopting Agentic AI?

Insurers see the upside of agentic AI, but most haven't crossed the line. Recent industry polling shows the intent is high, yet full rollouts are rare. The blockers are familiar: regulatory exposure, explainability of AI-only decisions, bias, and ethics.

The fastest path forward isn't another tool. It's unifying identity. If agents (human and AI) can't be verified, authorized, and audited with confidence, adoption stalls.

The identity bottleneck behind agentic AI

AI agents behave like users. They need access to core systems, carry entitlements, and trigger actions that must be logged and explained. That means they need the same level of governance, least privilege, and oversight as employees and partners.

The catch: many insurers still run a patchwork of identity tools, teams, and policies. That fragmentation slows approvals, creates blind spots, and makes audits harder than they need to be.

The patchwork problem

• Multiple point products: IAM, IGA, PAM, AD management, ITDR, and more.
• Separate teams running overlapping tools and processes.
• Integrations that break as versions change or vendors consolidate.
• User friction that invites risky workarounds and unattended sessions.

This is why a unified approach matters. One policy framework, one event model, one place to automate approvals and revoke access-especially for machine and AI identities.

From sprawl to unified identity

Identity has moved through four phases: sprawl, point solutions, stitched integrations, and now unified platforms. Analysts describe this next phase as converged identity or identity fabrics. The goal is simple: consistent zero-trust access for people, devices, machine identities, and AI agents-without stitching together 12 tools by hand.

AI as the abstraction layer

AI doesn't just create new identity needs; it can help solve them. An AI-driven abstraction layer can sit across your identity stack to broker access, verify context, and enforce policy-without forcing every app to learn every identity system.

• Context-aware access: query, verify, grant or deny based on user, device, location, and session risk.
• Behavior signals: flag anomalies and insider threats; trigger step-up checks or auto-remediation.
• Process enforcement: automate joiner-mover-leaver rules across all directories and apps.
• Explainability: produce decision logs that auditors and regulators can follow.

Stop rip-and-replace

The best part of an abstraction layer: you can extend what you already own. Let AI read across IAM, IGA, PAM, AD, and ITDR to unify decisions and workflows. Keep your investments, reduce integration toil, and reserve replacement for true gaps-not every new use case.

What an identity fabric should cover for insurers

• All identities: employees, agents, brokers, partners, customers, devices, machine identities, and AI agents.
• Core controls: single policy engine for least privilege, step-up auth, and session oversight.
• Audit-grade logs: consistent, queryable events across tools and directories.
• Out-of-the-box integrations: major directories, cloud apps, policy stores, and ticketing.
• Zero-trust by default: verify every request, every time, with time-bound access.

A practical rollout plan (60-90 days)

• Week 1-2: Inventory identities. People, service accounts, API keys, AI agents. Map high-risk entitlements and crown-jewel apps.
• Week 3-4: Stand up the abstraction layer. Connect to IAM, IGA, PAM, and AD. Normalize events into one log.
• Week 5-6: Automate JML. Auto-provision least privilege; add time-boxed access and auto-recertification.
• Week 7-8: Pilot agentic AI use cases. Claims triage, document intake, underwriting assistance-each with explainable decision logs.
• Week 9-12: Expand policies. Add context checks (device, geolocation), step-up flows, and break-glass with mandatory post-facto review.

Controls auditors expect

• Traceable decisions: who/what requested access, policy applied, outcome, and reason codes.
• Separation of duties: enforced across human, machine, and AI identities.
• Bias and fairness checks: documented review of AI-only decisions in claims and underwriting.
• Periodic recertification: automated reminders, bulk actions, and evidence capture.
• Human override: clear escalation paths and kill-switch for agents.

High-value starter use cases

• Claims document intake and data extraction with automatic least-privilege temp access.
• Underwriting pre-checks with explainable rule alignment and audit logs.
• Agent and broker onboarding with auto-provisioned, time-bound entitlements.
• Access reviews triggered by policy or behavior anomalies, not calendar dates alone.

Metrics that prove progress

• Time to approve access requests: before vs. after policy automation.
• Percentage of JML actions fully automated and completed within SLA.
• Coverage: share of apps behind zero-trust policy and session oversight.
• Explainability: percentage of AI-only decisions with reviewer-ready logs.
• Tool count: integrations managed centrally vs. bespoke point-to-point links.

Bottom line

Agentic AI won't stick in insurance without unified identity. An identity fabric-augmented by an AI abstraction layer-gives you explainability, least privilege, and audit-grade evidence without ripping out your stack. Start with identity, and AI adoption follows.

If your teams need skills for AI governance, auditability, and agent operations, explore focused learning by role at Complete AI Training.