🏥 Spectorn AI Security for Healthcare and Protected Health Information (PHI)

Target audience: Hospitals, telemedicine providers, digital-health platforms, medical-software vendors.

Integrating Large Language Models into healthcare (analyzing medical records, producing preliminary diagnoses, patient-facing chatbots) runs into the strictest constraints around handling personal medical data (PHI — Protected Health Information). Spectorn makes safe AI adoption possible.

🛑 Key risks and Spectorn solutions

1. Leakage of medical confidentiality (PHI Data Exfiltration)

Risk: While chatting with one patient, the model may "recall" (through hallucination or a direct attack) data from another patient's medical record. Spectorn defense:

• Engines: pii, hybrid_pii, exfiltration.
• Any national insurance number, health-insurance policy ID, full name, or diagnosis is detected before the response reaches the patient.

2. Life-threatening hallucinations (Hallucination Control)

Risk: An LLM delivers an incorrect diagnosis or drug dosage with very high confidence (sycophancy / hallucinations). Spectorn defense:

• Engines: output_scanner, false_completion.
• output_scanner inspects the model's response specifically (not the request), while false_completion catches fabricated "confirmations" and falsely confident conclusions. Through the Shield policy, Spectorn intercepts prescriptions and medical conclusions: if the model attempts to state a final diagnosis without an explicit disclaimer, or to prescribe a strictly prescription-only drug, Spectorn blocks the response or forces it to append the mandatory line: "This information is not a medical diagnosis. Please consult your physician."

3. RAG-corpus poisoning by an attacker (RAG Poisoning)

Risk: A hacker uploads a malicious file into the lab-results intake system that "trains" the model to recommend buying a particular company's supplements. Spectorn defense:

• Engines: rag_corpus_guard, injection.
• Every inbound document (PDF, Word) is pre-filtered by Spectorn before it enters the vector database: rag_corpus_guard controls source provenance and grounding, while injection catches instructions embedded in the document. Suspicious vectors are rejected.

🛠️ Policy settings

There is no configuration file to deploy: the platform operator sets your tenant's policy at your request, including the scan mode (enforce, or shadow to see what would be blocked without blocking it), allow and deny lists, the block threshold and category weights. The engines that matter most for this sector: pii, exfiltration, jailbreak, output_scanner. Start in shadow mode on your own traffic, review what would have been blocked, then enforce.

🚨 Specific to HIPAA / 152-FZ

The Spectorn instance itself (gomcp + sentinel-core) is designed as a stateless proxy (apart from the Decision Logger):

1. The platform does not log full model responses (text payloads), retaining only metadata (severity, confidence) for the dashboard.
2. Zero-Knowledge Proof deployment is supported — the ability to verify the safety of a response without decrypting the request itself.

📜 Telemedicine use cases

• Intake bot: the agent collects a patient's complaints. Spectorn monitors the conversation so a patient cannot "jokingly" (social engineering) trick the bot into issuing a prescription.
• Physician assistant: the agent summarizes a patient's chart. Spectorn ensures the RAG agent does not pull data from the chart of a patient in the next bed (cross-tenant isolation).

❓ FAQ

How does Spectorn prevent one patient's PHI from leaking to another patient?

The pii, hybrid_pii, and exfiltration engines detect protected health information — insurance numbers, policy IDs, names, and diagnoses — before any response reaches the patient. Who may read which record stays with your own access control; Spectorn keeps no copy of the records it checks.

Can Spectorn stop a medical AI from giving a dangerous hallucinated diagnosis or dosage?

It helps, without a guarantee. The output_scanner engine inspects the model's response and false_completion flags fabricated, overconfident conclusions; in gateway mode with enforcement a blocked response is withheld (HTTP 403 spectorn_output_blocked). Spectorn does not rewrite a response or add a "consult your physician" disclaimer: that, and a clinical review of what the assistant may say, stay with your application.

Is Spectorn HIPAA- and 152-FZ-compliant?

Spectorn (gomcp + sentinel-core) runs as a stateless proxy that does not log full model text payloads — only metadata such as severity and confidence. It also supports Zero-Knowledge Proof verification, helping providers meet HIPAA and 152-FZ (Russia's personal-data protection law).

How does Spectorn protect a healthcare RAG knowledge base from poisoning?

Every inbound document (PDF, Word) is pre-filtered before it reaches the vector database. The rag_corpus_guard engine validates source provenance and grounding, and the injection engine catches instructions embedded in the file, so poisoned documents and suspicious vectors are rejected before indexing.

Can doctors still see full patient records while patients do not?

That is your system's access control, not Spectorn's. Spectorn detects protected health information in requests and responses, and the tenant policy blocks or flags them; masking is not enabled in the cloud contours, so there is no masked copy for one role and a clear one for another.