AI is becoming part of clinical documentation, medical information retrieval, research, safety workflows and regulated operations. These systems can process PHI, clinical knowledge and GxP data, making security and governance essential throughout the AI lifecycle.
Cygeniq helps healthcare and life sciences organizations discover AI assets, test AI systems against real-world threats, enforce runtime guardrails, govern AI risk and detect AI-native attacks through one Runtime AI Trust Platform.
One platform across clinical and regulated AI
AI systems can change behavior when a model is updated, a prompt changes or new information enters a retrieval corpus.
Sensitive information can also leave through prompts, logs and model outputs. Manipulated or outdated retrieval content can influence AI-generated information without appearing as a conventional cybersecurity incident.
AI sits alongside clinical and research workflows, so existing healthcare security and compliance controls cover it. Model and prompt changes look like ordinary software updates.
AI-generated content and recommendations reach clinicians, researchers and patients directly, and a manipulated input or instruction can move through the AI layer without triggering conventional security alarms.
This creates risks including:
Healthcare AI security needs to protect sensitive data and AI behavior as models, prompts and knowledge sources evolve.
PHI can move through prompts, transcripts, model outputs and logs, while generated content may enter medical records.
Poisoned or outdated retrieval content can cause an AI system to present superseded information as current.
AI used to support regulated submissions requires appropriate context, governance and supporting evidence.
AI-assisted safety workflows require visibility into how information is processed and prioritized.
AI components may change through model, prompt or corpus updates, creating additional requirements for monitoring and change control.
One connected sequence, from discovery through defense, mapped to the modules that deliver each step.
Create an AI bill of materials across clinical, R&D and GxP models, agents, retrieval corpora and APIs with ownership, data classification and risk tiering.
Test healthcare AI against prompt injection, PHI extraction, retrieval poisoning and unsafe role manipulation.
Apply runtime monitoring and guardrails across prompts and outputs, with renewed testing when models, prompts or knowledge sources change.
Assess AI risk based on factors such as PHI access and potential clinical consequence so different AI systems can be prioritized appropriately.
Connect AI assets, risks, controls and evidence to support healthcare and life sciences AI governance.
Detect AI-layer threats such as PHI exfiltration, agent misuse and poisoning of clinical retrieval sources.
AI phishing and BEC against clinical, finance and procurement staff, deepfake impersonation of clinicians and executives, AI-authored ransomware, and credential attacks on clinician and patient portals, detected, blocked and contained under human control.
Book a walkthrough of discovery, adversarial testing, runtime protection and AI-native detection for healthcare AI.
| AI Use Case | Key AI Security Risk |
|---|---|
| Ambient Documentation | PHI exposure and unsafe generated documentation |
| Clinical RAG | Retrieval poisoning and outdated clinical information |
| Medical Information Assistants | Sensitive data exposure and manipulated responses |
| Regulatory Drafting | Governance, credibility and evidence gaps |
| Pharmacovigilance | Missed or incorrectly prioritized safety information |
| GxP AI | Uncontrolled model, prompt or corpus changes |
| Internal Healthcare AI | Shadow AI and uncontrolled PHI access |
Controls and evidence map once, in GRCortex AI, to the regimes a healthcare or life sciences organization answers to: HIPAA and HITECH, the EU AI Act (medical devices and safety components are high-risk), EU MDR and FDA guidance for AI/ML-enabled software as a medical device, 21 CFR Part 11 and GAMP 5 for GxP validation and change control, ISO/IEC 42001 and the NIST AI RMF, with technical testing aligned to the OWASP LLM and Agentic Top 10 and MITRE ATLAS.
Continuous adversarial red teaming from a 1M+ scenario library, model validation and drift monitoring for every model, copilot and agent in scope, with findings flowing into GRCortex AI as evidence and into CyberTix AI as detection context.
Dynamic AI risk register fed by runtime findings, controls mapped once across every framework in scope, continuous control monitoring, audit-ready evidence and board-ready AI assurance.
The runtime security layer for enterprise AI. CyberTix Secure discovers every LLM, RAG pipeline and agent, blocks AI-native attacks inline and contains threats in real time. CyberTix Defend stops AI phishing and BEC, deepfakes, AI malware and autonomous intrusions, one backbone, one AI-risk picture.
Discover → Understand → Validate → Protect → Govern → Monitor → Assure → Improve
A healthcare provider running Hexashield AI found and closed more than 80 high- and medium-severity AI risks with a retest error rate under 3%. Across healthcare deployments, clinical-trial chatbots, diagnostics-imaging assistants and patient onboarding, Cygeniq delivers runtime AI risk monitoring, AI audit readiness against NIST, EU and ISO requirements, and no AI breaches during the engagement.
Reference calls available under NDA.Risks include PHI exposure, prompt injection, retrieval poisoning, unsafe AI-generated information, AI agent misuse and shadow AI.
Organizations should control retrieval sources, test systems for indirect prompt injection and poisoning, monitor runtime behavior and reassess them as knowledge sources change.
Organizations should understand where PHI enters and leaves AI systems, test applications for data extraction and prompt injection risks, monitor prompts and outputs and apply controls around sensitive AI interactions.
GRCortex AI connects AI assets with ownership, risk, controls and evidence for more consistent governance.
Yes. A tamper-evident audit trail of every AI interaction, retesting triggered by model, prompt or corpus changes, and control evidence collected from the operating environment support the validation and change-control expectations for AI in GxP environments.
No. Cygeniq adds AI-specific security and governance around existing security programs.
CyberTix AI provides visibility into AI-native threats including data exfiltration, agent misuse and retrieval-source poisoning.
Protect sensitive data and AI systems across clinical, research and regulated workflows as AI evolves.
Cygeniq helps you secure AI systems, govern AI risk and prove control.
AI Trust Infrastructure for secure, governed and accountable enterprise AI
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