Not based on a specific customer deployment. The company, figures, decisions, and outcomes below are a constructed example that shows how Intended’s mechanism applies to this kind of problem. They are not measured results from a named customer. Where we publish a real, attributed customer outcome, we will say so explicitly.
HIPAA-Aware Governance for AI Patient-Data Operations
Consider a health system whose AI agents handle appointment scheduling, medical record access, billing automation, and clinical decision support — all touching PHI under HIPAA.
01 · the challenge
The kind of problem this addresses.
02 · how it works
See the difference.
AI agent requests patient records
Patient: J. Doe, MRN: 847291
System returns records
No authorization check, no logging
AI processes PHI
Full record access, no scope limits
Flat log entry (maybe)
No evidence chain, no tamper detection
03 · the solution
What they deployed.
- — Installed HR Ops domain pack plus custom Healthcare extension for PHI-specific intents
- — Configured CRITICAL risk level for all PHI access with mandatory dual authorization
- — Scoped authority tokens to individual patients with 60-second TTL and read-only permissions
- — Connected Epic EHR and internal scheduling systems via Intended connectors
- — Generated tamper-evident evidence bundles for every PHI access event, governed by the healthcare policy pack
04 · implementation
From zero to governed.
Phase 1
Assessment
Map AI agent PHI access patterns. Identify unprotected access paths across clinical systems.
Phase 2
Deploy
Install the Healthcare domain pack. Configure PHI access policies, dual-authorization workflows, and token scoping.
Phase 3
Validate
Run shadow mode in parallel with existing systems to verify complete PHI access capture before enforcing.
Phase 4
Enforce
Switch to enforcement. Every PHI access then carries a tamper-proof, independently verifiable evidence chain.
05 · illustrative outcomes
What this is designed to deliver.
Modeled figures for this scenario — what the workflow above is built to achieve, not measured results from a named customer.
0%
PHI access token-gated
Dual authorization for CRITICAL access
0%
PHI access logged
With tamper-proof evidence
0s
Token TTL for PHI access
Auto-expires, no lingering access
0-click
Auditor verification
Independent of self-reported logs
06 · decision replay
Example decisions, full trace.
Sample decision records that show the shape of the evidence Intended produces. Illustrative, not drawn from a live customer’s logs.
healthcare.phi.record-accessRISK: 72/100ALLOW28msAI scheduling agent requests patient demographics for appointment confirmation
Resolved by: Policy: demographics-only access auto-approved with single auth
healthcare.phi.clinical-recordsRISK: 95/100ESCALATE31msAI clinical support agent requests full medical history for treatment recommendation
Resolved by: Dr. Martinez (dual authorization in 1m 22s)
healthcare.phi.billing-dataRISK: 58/100ALLOW24msAI billing agent requests insurance information for claims processing
Resolved by: Policy: billing scope access with system auth
healthcare.phi.bulk-exportRISK: 99/100DENY19msAI analytics agent requests bulk patient data export for population health report
Resolved by: Policy: bulk PHI export requires IRB approval + CISO sign-off
the takeaway
The hard part of HIPAA for AI is independent verifiability: an auditor should be able to confirm every AI access to patient data without trusting self-reported logs. A tamper-proof cryptographic chain makes each access provable rather than asserted.
Why this pattern matters — not a customer quote.
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