Back to Whitepapers
Peer-Reviewed60+ SourcesWhitepaper

PHI Sprawl: Containing Protected Health Information in the AI-Enabled Health System

A White Paper by HealthSync AI

Learn how to contain the uncontrolled spread of Protected Health Information across APIs, clouds, AI pipelines, and partner systems using Zero Trust design, HIPAA-aligned governance, and modern implementation patterns.

AI Security Crisis

Organizations with AI Incidents

97%

lacked proper AI access controls

Share of orgs that reported AI-related security incident
IBM Report 2025

Healthcare Breach Crisis

2024 Total Impact

277M

records breached in 2024

U.S. Population
81%
YoY Increase
+64%
HIPAA Journal 2024

Table of Contents

Executive Summary

PHI sprawl is the uncontrolled spread of Protected Health Information across apps, clouds, logs, AI pipelines, partner systems, and devices. As U.S. providers adopt EHR APIs, analytics platforms, and AI agents, PHI now flows far beyond the core EHR—into CRMs, care navigation tools, RCM/billing stacks, cloud services, call recordings, web trackers, vector databases for RAG, and "shadow" spreadsheets.[1]

Left unmanaged, PHI sprawl raises breach risk, claim-denial exposure, and compliance liabilities, while slowing innovation due to fear of data misuse. Recent mega-incidents underscore the stakes and the systemic nature of the problem.[2]

This paper explains why PHI sprawl is accelerating, where it hides, and how to contain it using Zero Trust design, rigorous data-lifecycle controls, HIPAA-aligned governance ("minimum necessary"), and modern implementation patterns for FHIR/SMART, Bulk Data, and AI.[3][4]

This White Paper Covers:

  • What PHI sprawl is and why it's accelerating in AI-enabled systems
  • Common sprawl vectors: APIs, clouds, AI pipelines, partner systems, web tracking
  • Regulatory framework: HIPAA, NIST, CISA, FHIR security standards
  • A 7-point control blueprint to contain sprawl using Zero Trust principles
  • Three-phase implementation blueprint with measurable KPIs
  • How HealthSync AI operationalizes these controls in FHIR, AI, and billing workflows

1. What is "PHI Sprawl," and Why Now?

Definition

PHI sprawl occurs when individually identifiable health information—"PHI" under the HIPAA Privacy Rule—propagates into systems and locations beyond the intended or necessary scope for care, payment, or operations. This includes copies, caches, derived artifacts (embeddings, transcripts), backups, monitoring data, and vendor environments.[5]

Why It's Accelerating

Interoperability at Scale

FHIR and SMART-on-FHIR APIs (including Bulk Data/Flat FHIR) make cross-system movement of large data volumes routine—great for care coordination and analytics, but easy to overshare if scopes and exports aren't tightly controlled.[6][7][8]

Cloud Everything

Health clouds (AWS, Google Cloud, Azure) and EHR developer programs simplify integration—but multiply PHI endpoints (storage, queues, functions, logs). BAAs help, yet design choices still determine exposure.[9]

AI Adoption

Voice agents, copilots, and RAG stacks can copy PHI into prompts, logs, caches, and vector stores; LLM/embedding security is new terrain with unique leakage paths (prompt-injection, training-data exposure, membership inference).[10][11]

Tracking & Outreach

OCR has warned that web tracking technologies on patient-facing sites can impermissibly disclose PHI—an overlooked sprawl vector.[12]

Regulatory Expansion

Beyond HIPAA, rules like the FTC Health Breach Notification Rule (for health apps), state privacy laws (e.g., Washington's My Health My Data), and Cures Act information-blocking rules complicate data flows and liabilities.[13][14][15]

The Cost of Getting It Wrong

Healthcare breach costs remain the highest among all industries; independent analyses show outsized breach costs and downtime impacts year after year—averaging $408 per record and 2.3x higher than the cross-industry average.[16]

2. Where PHI Hides: Common Sprawl Vectors

PHI spreads through predictable but often overlooked pathways. Understanding these vectors is the first step to containment.

1API & Bulk Exports

SMART scopes that are too broad; periodic Bulk FHIR exports landing in general-purpose data lakes; CSVs in ad-hoc S3 buckets; service accounts with persistent tokens.[17][18]

2SaaS & Partner Ecosystems

CRMs, contact centers, help desks, forms/scheduling apps, and analytics tools that become business associates (or should)—often without explicit BAAs or minimum-necessary enforcement.[19]

3Cloud by Default

PHI in debug logs, object versions, unmanaged backups, and serverless invocations; misaligned key management or non-validated crypto modules.[20][21]

4AI Data Paths

Voice/chat transcripts, call recordings, STT/TTS logs, RAG document stores, embedding/vector DBs, and fine-tuning corpora—each can replicate PHI. LLM-specific risks include prompt-injection and training-data extraction.[22][23]

5Web & Mobile Properties

Pixels/cookies on appointment, portal, or symptom pages leaking identifiers to third parties without HIPAA-permitted disclosures or BAAs.[24]

6End-User Tooling

Exports to spreadsheets, screenshots, local notes, clinician "workarounds," or research sandboxes— classic shadow IT.

3. Regulatory and Standards Lens (What "Good" Looks Like)

Multiple frameworks define expectations for PHI governance, security, and interoperability. Aligning to these standards provides both compliance coverage and operational guardrails.

HIPAA Privacy/Security Rules

"Minimum necessary" use/disclosure standard; risk analysis/risk management; integrity, access control, audit controls. BAAs for all parties that create/receive/maintain/transmit PHI.[25][26][27]

De-identification

Two recognized methods—Expert Determination and Safe Harbor (18 identifiers)—with OCR guidance on residual risk.[28]

NIST

HIPAA mapping (SP 800-66r2), security controls catalog (SP 800-53r5), Zero Trust (SP 800-207), media sanitization (SP 800-88), key management (SP 800-57).[29][30][31][32]

CISA Zero Trust Maturity Model

Practical milestones for identity, devices, networks, apps, data.[33]

EHR Ecosystems

HL7 FHIR, SMART-on-FHIR, Bulk Data (Flat FHIR), FHIR Security Best Practices, and vendor programs (Epic, Oracle Health/Cerner, athenahealth, NextGen, eClinicalWorks).[34][35][36]

Epic on FHIROracle HealthathenahealthNextGeneClinicalWorks

Data Sharing Context

TEFCA/USCDI and Cures Act information-blocking expectations influence lawful, secure exchange.[37][38]

Beyond HIPAA

FTC Health Breach Notification Rule (for certain health apps), state laws (e.g., Washington MHMD) add obligations even when HIPAA doesn't apply.[39][40]

4. A Control Blueprint to Contain PHI Sprawl

Containing PHI sprawl requires a comprehensive approach across governance, architecture, and operations. This 7-point blueprint maps to HIPAA and NIST requirements while providing practical implementation patterns.

1Govern to "Minimum Necessary" with Data Maps & BAAs

  • Maintain a living data inventory (systems, data classes, flows, storage, retention).
  • Enforce "minimum necessary" access and disclosure policies across API scopes, exports, users, and apps.
  • Execute BAAs (and downstream BAAs) with every PHI-touching vendor; align permitted uses and retention/return-or-destroy clauses.[41]

Implementation hints: Use scoped SMART-on-FHIR permissions and time-boxed tokens; avoid blanket, long-lived service accounts. For Bulk FHIR, segment exports by cohort/use case and land into PHI-approved enclaves only.[42]

2Architect Zero Trust for Data Flows

Adopt Zero Trust patterns: continuous identity verification, least privilege, micro-segmentation, app-to-app auth, encrypted transit/storage with FIPS-validated modules, and per-purpose tokens.[43][44]

Apply NIST SP 800-53 controls for audit logging, access control, data integrity, and configuration management across every PHI store.[45]

3Harden the AI/LLM Pipeline (LLMSec)

  • Treat prompts, transcripts, embeddings, and RAG caches as PHI unless provably de-identified; segregate storage; set short TTLs; encrypt at rest.
  • Implement prompt-injection and data exfiltration mitigations; scan RAG corpora for PHI before indexing; consider "prompt firewalls."
  • Avoid training/fine-tuning on PHI unless covered by BAAs and purpose-limited; apply opt-out, dataset versioning, and deletion pipelines.
  • Recognize membership-inference and training-data extraction risks; limit model output exposure and log access.[46]

4De-identify Early; Re-identify Sparingly

Use Expert Determination where Safe Harbor is too destructive; monitor re-identification risk over time as external datasets evolve.[47]

Embed de-ID in ETL/ELT; prefer tokenization for operational joins; restrict re-ID keys to HSM/KMS with strong key-lifecycle governance.[48]

5Data Lifecycle: Retention, Deletion, and Media Sanitization

Define strict retention (by system/use) and automatic deletion; implement legal hold workflows.

Apply NIST SP 800-88 media sanitization for disks, snapshots, and portable media; verify with audit evidence.[49]

6Cloud Practice: Logs, Backups, and Serverless Hygiene

  • Exclude PHI from debug logs; mask in app logs; restrict object versioning for PHI buckets; encrypt snapshots; centralize key management per SP 800-57.
  • Review cloud provider HIPAA guidance; ensure BAAs in place and services used are within HIPAA eligibility lists.[50]
Azure Health Data ServicesGoogle Cloud Healthcare APIAWS HealthLake

7Web Properties and Patient Apps

Remove or strictly control tracking technologies on patient-facing pages; treat events as PHI if they can identify a person + health context; align with OCR's bulletin.[51]

For non-HIPAA DTC apps that collect health data, evaluate FTC HBNR applicability.[52]

5. Case Signal: Large-Scale Operational Impact

The Change Healthcare Cyberattack (2024)

The 2024 Change Healthcare cyberattack illustrated how interdependent, distributed PHI and transaction data can paralyze operations across the U.S. health system, disrupt revenue cycles, and expose massive volumes of sensitive data—an emblematic consequence of system-wide data sprawl and connectivity.[53]

This incident demonstrates that PHI sprawl is not merely a compliance concern—it's an operational resilience issue that can affect patient care delivery, financial stability, and trust across the entire healthcare ecosystem.

6. Implementation Blueprint (HealthSync AI Reference Approach)

A three-phase rollout balances urgency with operational reality. This approach prioritizes high-risk vectors first while building sustainable governance.

1

Phase One: Discover & Contain

  • Rapid PHI data map (systems, apps, exports, AI stores, logs)
  • Access review on SMART scopes, Bulk FHIR jobs, S3/Blob buckets, vector DBs
  • Kill or quarantine shadow exports; enable guardrails on EHR app registrations; rotate long-lived tokens[54]
2

Phase Two: Redesign Data Flows

  • Introduce a PHI Gateway: policy-based brokering for all inbound/outbound PHI (scope filtering, masking, de-ID, DLP)
  • Segment AI data paths: separate stores for prompts, transcripts, and embeddings with short TTLs; implement PHI scrubbing before indexing
  • Standardize de-ID and tokenization in ETL; move analytical workloads to de-identified datasets by default[55]
3

Phase Three: Operationalize

  • Zero Trust enforcement (micro-segmented networks; per-app auth; continuous posture checks)[56]
  • Update BAAs; codify retention/deletion; adopt NIST SP 800-88 for media; configure audit trails to SP 800-53
  • Run tabletop exercises: Bulk-export misroute, vector-DB leak, and web-tracker exposure; verify FTC/HBNR and state-law playbooks[57]

HealthSync AI Platform Integration

HealthSync AI's platform operationalizes these controls across Atrium (Healthcare SLM), Pulse3 (AI Billing), and Voice & Chat Agents—with built-in PHI Gateway, FHIR/HL7 connectors, and Zero Trust architecture.

7. KPIs to Track

Measure progress with concrete metrics aligned to HIPAA/NIST control families:

Data Governance

  • % of PHI stores inventoried & classified
  • # of shadow exports eliminated
  • % workloads using de-ID datasets

Access Control

  • Mean token lifetime (target: <24 hours)
  • # of vendors with current BAAs
  • Time-to-revoke access (target: <1 hour)

Lifecycle Management

  • Retention compliance rate (%)
  • # of deletion requests completed on time
  • Media sanitization audit pass rate

Audit & Monitoring

  • % of PHI access events logged
  • Mean time to detect anomalous access
  • Compliance dashboard uptime

Align to NIST SP 800-53 Control Families: AC (Access Control), AU (Audit and Accountability), CM (Configuration Management), IA (Identification and Authentication), SC (System and Communications Protection).[58]

8. Procurement Checklist (Essentials)

When evaluating vendors and platforms for PHI-touching capabilities, ensure these fundamentals are in place:

✓ BAA & Sub-processors

BAA in place for each vendor + list of all sub-processors with downstream BAAs.[59]

✓ Data Maps & Retention

Data maps and retention schedules; right-to-return or destroy PHI on termination (contract language).[60]

✓ Cryptography

TLS 1.2/1.3; FIPS 140-2/140-3 validated modules; documented key management to SP 800-57.[61][62]

✓ EHR Connectivity

Named SMART scopes, Bulk FHIR controls, audit logs; vendor documentation for Epic/Oracle/athena/ NextGen/ECW.

✓ AI Pipeline

PHI scrubbing, cache TTLs, vector-store isolation, prompt-injection defenses; documented model data policies addressing extraction risks.

✓ Web Tracking

Attestation of tracking-tech usage and HIPAA-compliant configuration per OCR bulletin.

9. Conclusion

AI-enabled care and modern interoperability demand fast, lawful, and safe data movement. Without deliberate design, PHI sprawl is inevitable—and costly. The path forward is practical: govern to minimum necessary, architect for Zero Trust, de-identify early, isolate AI data paths, and operationalize deletion and auditing.

With these patterns, health systems can accelerate analytics and AI while reducing breach likelihood, denials, and compliance risk.

HealthSync AI Platform

HealthSync AI's platform and implementation playbooks are built on these principles—spanning EHR integration (FHIR/SMART/Bulk Data), AI orchestration, billing, and safety controls—so your teams ship value without letting PHI leak into the long tail of systems.

Explore PlatformSchedule Demo

References (Selected)

Citations in the text link to the sources below. This whitepaper draws on 60+ credentialed sources including HHS OCR, NIST, CISA, HL7, major EHR vendors, and cloud providers.

Regulatory & Compliance

[14] [40]

Washington My Health My Data Act

https://www.atg.wa.gov/my-health-my-data-act

[15]

Information Blocking Rules - ONC

https://www.healthit.gov/topic/information-blocking

NIST Standards

[29]

NIST SP 800-66r2 (HIPAA Security Rule)

https://csrc.nist.gov/pubs/sp/800/66/r2/final

[30] [45] [58]

NIST SP 800-53r5 Security Controls

https://csrc.nist.gov/pubs/sp/800/53/r5/final

[4] [31] [43] [56]

NIST SP 800-207 Zero Trust Architecture

https://csrc.nist.gov/pubs/sp/800/207/final

[32] [49]

NIST SP 800-88 Media Sanitization

https://csrc.nist.gov/pubs/sp/800/88/r1/final

[61]

FIPS 140-3 Standard - NIST

https://csrc.nist.gov/pubs/fips/140/3/final

[11]

NIST AI Risk Management Framework

https://www.nist.gov/itl/ai-risk-management-framework

FHIR & Interoperability

[35]

FHIR Security Best Practices

https://build.fhir.org/security.html

[7] [17] [36] [42] [54]

SMART on FHIR / SMART Health IT

https://smarthealthit.org/

[6]

CMS Interoperability & Prior Authorization Rule

https://www.cms.gov/priorities/burden-reduction/overview/interoperability

AI & LLM Security

[23]

Prompt Injection & Data Extraction Risks

Carlini et al., "Extracting Training Data from LLMs" (arXiv)

Industry Data & Case Studies

[16]

IBM Cost of a Data Breach Report 2025

https://www.ibm.com/reports/data-breach

[2] [53]

Change Healthcare Cyberattack 2024

Reuters: U.S. House Committee on Change Healthcare Breach

About HealthSync AI

HealthSync AI helps provider organizations "break the silo" with secure EHR integrations (FHIR/SMART/Bulk Data), AI orchestration, billing automation, and AI safety controls that contain PHI sprawl while enabling modern analytics and patient experiences.

To discuss an assessment or demo, visit healthsync.tech.