HEALTHCARE
Secure AI At The Point Of Care
PromptShield™ delivers real-time protection, audit visibility, and governance controls across clinical, pharmacy, and patient-facing AI systems.
AI Protection For Modern Healthcare
Secure healthcare AI—from clinical copilots to patient-facing systems—without compromising safety, privacy, or compliance.
Patient-Facing AI, Protected
Stop prompt injection and unsafe outputs—before they impact care.
PromptShield™ blocks PHI leakage, prevents harmful recommendations, and neutralizes adversarial inputs while keeping patient communication fast and accessible.
Clinicians Using AI—Safely
Enable documentation, summarization, and decision support without exposing patient data.
PromptShield™ enforces real-time PHI detection, redaction, and output validation so AI tools enhance productivity—not introduce malpractice risk.
Protecting Clinical Systems
AI in Care Delivery, With Guardrails
Allow AI to assist in triage, dosing, and workflow automation—without letting it overstep.
PromptShield™ governs tool calls, validates actions, and applies human-in-the-loop controls only where clinical risk demands it.
Protecting Institutional Integrity
Proprietary Data, Clinical Logic, Secured
Safeguard treatment protocols, referral systems, billing logic, and research data from misuse or extraction.
PromptShield™ prevents over-disclosure, corrupted inputs, and manipulation—while preserving operational efficiency.
BUILT FOR COMPLIANCE
Support AI Adoption While Meeting Regulatory And Security Standards
Common AI Risk Scenarios In Healthcare
As AI adoption accelerates, healthcare organizations must manage new clinical and data risk surfaces while
maintaining patient safety, regulatory compliance, and institutional trust.
Leakage In PHI Prompts
AI-generated summaries or reports can fabricate allergies, overstate diagnoses, or suggest incorrect dosages. Without safeguards, these errors can directly impact patient safety.
Prompt Injection Defense Via Clinical Text: EHR Notes, Messages, Referrals
Prompt injection can cancel appointments, misprioritize symptoms, generate improper billing codes, or approve controlled substances outside protocol. Without guardrails, AI actions can create operational and regulatory risk.
Safe Clinical Summarization And Documentation Drafting
Clinicians are using AI to summarize notes, draft communications, and handle data. Without safeguards, these workflows can lead to cross-patient exposure, failed de-identification, or unintended PHI disclosure.
Control Tool-Calls For Operational Automation: Scheduling, Billing, Triage
Malicious portal messages, poisoned notes, or fake referrals can manipulate AI systems. Without controls, they can distort triage decisions or trigger unsafe actions.
Common AI Risk Scenarios In Healthcare
Traditional security tools rely on static rules and keyword filters. That fails in healthcare AI, where attacks hide in notes, messages, or workflows. Clinical risk appears as altered documentation or manipulated triage. Security must understand intent—not just keywords.
Frequently Asked Questions
Can We Ever Fully Eliminate Hallucinations?
No. LLMs are probabilistic by nature. PromptShield™ reduces hallucination HARM by detecting likely hallucinations and enforcing human review. Goal is <5% hallucination rate in safety-critical fields (meds, allergies) with 100% detection.
Will Extensive Constraints Make AI Outputs Too Generic/Useless?
Properly tuned constraints maintain utility. AI can still elaborate narratives, format professionally, save time—just not fabricate clinical facts. If AI output becomes too constrained, it signals input data is insufficient (clinician should provide more details).
What If Clinician Blindly Accepts AI Output Despite Warnings?
Forcing functions: Require explicit acknowledgment, create audit trail. If clinician habitually ignores warnings, flag for quality review/retraining. Ultimately, professional responsibility rests with clinician (standard of care).
How Do We Handle AI that Learns From Our Institutional Data (Fine-Tuning)?
HIPAA-compliant fine-tuning (BAA with AI vendor, de-identified training data) can reduce hallucinations by aligning AI with institution-specific practices. PromptShield still required—fine-tuning improves but doesn’t eliminate hallucination risk.
Are Disclaimers Sufficient For Liability Protection?
Disclaimers help but aren’t bulletproof. Courts may still find institution negligent if AI is known to produce frequent errors. PromptShield’s™ value: Demonstrates active risk mitigation (not just warnings, but detection + prevention controls).
Won't HITL Requirements Slow Down Operations And Negate Automation Benefits?
Properly tuned policies trigger HITL only for high-risk actions (5-10% of total). Routine actions (90-95%) proceed automatically. Net result: Significant efficiency gain while maintaining safety.
What If A Clinician Approves A Bad AI Recommendation (HITL Fails)?
Liability shifts to clinician (professional responsibility). PromptShield™ documents: AI made recommendation, human had opportunity to review, human approved. Clinician held to standard of care (should have caught error).
Can This Prevent All Billing Fraud?
No. Sophisticated fraud (e.g., fabricating entire patient encounters) requires different controls (EHR audit, utilization review). PromptShield™ prevents AI-generated coding errors and upcoding based on insufficient documentation.
How Do We Balance Patient Access (24/7 AI) With Safety (HITL Delays)?
Tiered approach:
- Low-Risk: Full automation (routine scheduling, appointment reminders).
- Medium-Risk: AI proposes, human reviews next business day (routine refills).
- High-Risk: Immediate human review required (emergency triage, controlled substances).
What If Attackers Develop Novel Injection Techniques We Haven't Seen?
Layered defense: Signature detection catches known patterns, parameter validation catches illogical actions (e.g., bulk cancellations), rate limiting prevents mass abuse, audit logs enable rapid incident response. No single control is perfect; defense-in-depth mitigates unknown attacks.