Insider-threat monitoring
Compliance teams watch for snooping and over-broad use against behavioral baselines.
PHI Protection Agent
Healthcare Data Security AI
A security agent that watches how PHI is accessed and moved — flagging anomalous access, catching data leaving where it shouldn't, and de-identifying records — so a breach is caught by behavior instead of discovered in an audit months later.
Anomaly detectionDLPDe-identificationAudit Logged
Trusted by global innovators
Most PHI breaches are found too late
Insider snooping, over-broad access, and quiet exfiltration rarely trip a hard rule, so many PHI breaches surface only in a retrospective audit. This agent watches PHI behavior in near real time: it baselines normal access from the audit logs, flags anomalies, inspects data movement for PHI leaving where it shouldn't, and de-identifies records for safe downstream use. Every alert is scored, explained, and routed to an analyst — the agent prioritizes, people decide.
Detection architecture
Access logs and data flows feed anomaly detection, DLP, and de-identification — with scored, explainable alerts routed to human review.
Key Capabilities
PHI protection built on behavior, not just static rules — detection, prevention, and de-identification with human-reviewed alerts.
Anomaly detection on access logs
Baselines each user's normal access from EHR audit logs, then flags outliers — unusual patients, volume spikes, off-hours access, VIP-record snooping — with a risk score and reason.
Data loss prevention (DLP)
Inspects data in motion — email, uploads, exports, API responses — for PHI patterns (MRN, SSN) leaving sanctioned channels, and blocks or quarantines before it exits.
De-identification & masking
Removes or masks HIPAA Safe Harbor identifiers to produce de-identified datasets for analytics, testing, and model work — so teams operate on safe data, not raw PHI.
Explainable, scored alerts
Every alert carries a risk score and plain-language rationale — which behavior deviated from baseline and why — so analysts triage fast, correlating access, auth, and data-movement signals.
Who runs it
Where PHI-security detection matters
Exfiltration prevention
Security ops block PHI leaving over email, uploads, or exports before it exits.
Breach-readiness
Near-real-time detection and immutable trails support HIPAA disclosures and notification timelines.
Safe analytics enablement
Data teams get de-identified datasets so analytics never touches raw PHI.
PHI protection, expected impact
Expected impact, not guarantees — depends on log coverage and your response process.
Near real-time
Detection instead of retrospective audit
Every alert
Scored and explained for the analyst
Safe Harbor
De-identification for downstream data use
Compliance-First Healthcare App Development Services Backed by Global Standards
15-Minute Scoping Call01
HIPAA
Health Insurance Portability and Accountability Act
Protect PHI with privacy-first architecture, encrypted storage and transmission, strict access controls, and traceable audit logs.
02
GDPR
General Data Protection Regulation
Implement lawful consent flows, data minimization, retention controls, and secure processing for sensitive health data.
03
FHIR
Fast Healthcare Interoperability Resources
Enable standardized health data exchange across apps, care teams, and systems through robust FHIR-ready APIs.
04
HL7
Health Level Seven International
Support enterprise-grade interoperability with HL7-based integrations for records, events, and clinical messaging workflows.
05
HITRUST
Health Information Trust Alliance
Align security programs to healthcare-specific control and risk management practices trusted by providers and ecosystem partners.
06
HITECH
Health Information Technology for Economic and Clinical Health Act
Design with breach notification readiness, digital record safeguards, and operational controls that support regulated care programs.
07
SaMD
FDA Software as a Medical Device
Plan software quality, traceability, and documentation pathways for products that may require SaMD review and submission.
08
MDR (EU)
Medical Device Regulation (European Union)
Prepare EU market-ready processes for risk classification, evidence tracking, and lifecycle governance under MDR expectations.
09
SAMHSA
Substance Abuse and Mental Health Services Administration
Apply confidentiality controls and consent-aware sharing models for behavioral and mental health data experiences.
Standards we build against
Standards & data surfaces
HIPAAHITECHSOC 2HITRUSTFHIR
How It Works
From signal to reviewed alert
Capture the logs, detect against baseline, prevent leakage, and route scored alerts to a human — everything logged.
1.
Capture — logs & data flows
The agent ingests EHR access logs, IAM events, and data-movement telemetry, normalizing them into a common event stream.
Log ingestion
2.
Processing — baseline + detect
Models learn each user's normal behavior and score deviations; DLP inspects data in motion for PHI, and de-identification masks identifiers for downstream use.
Anomaly + DLP guardrails
3.
Action — scored, explainable alert
A risk-scored alert with a rationale is routed to a security analyst; high-severity leakage can auto-block, access anomalies escalate for human decision.
Human-in-the-loop
4.
System update — case & audit trail
The alert, its evidence, and the analyst's disposition write to a case record and an immutable audit log for incident response and compliance.
Audit logged
Related proof
Read Case StudyLera Health: compliant women's health platform
Related proof of compliant delivery — not this exact agent. For Lera Health we built a privacy-first data layer with strict PHI handling and access controls, the same foundation a PHI-security agent monitors.
Compliance & Guardrails
Security that's accountable, not autonomous
A PHI-security agent has to protect data without becoming a new way to over-access it.
PHI handling
The agent works on access metadata and de-identified data where possible; any PHI access is least-privilege, scoped, and itself logged.
Human-in-the-loop
The agent detects and prioritizes; an analyst investigates and decides. Auto-blocking is reserved for clear, high-severity leakage, always with a reviewable record.
Explainability
Every alert states which behavior deviated from baseline and why it scored as it did, so analysts and auditors can defend each decision.
Audit logging
Alerts, evidence, and dispositions write to an immutable log, supporting HIPAA accounting-of-disclosures and breach-notification readiness.
Frequently Asked Questions
Yes, and it's built to strengthen HIPAA posture. It operates on access metadata and de-identified data where possible; any PHI access is least-privilege, scoped, and logged. Immutable trails support accounting-of-disclosures and breach readiness.
Protect PHI by watching behavior
Tell us your log sources and data flows. We'll return an architecture for anomaly detection, DLP, and de-identification — with scored, human-reviewed alerts and a full audit trail.
contact@agnotic.com
Partnerships
contact@agnotic.com