Hospitals, payers, and digital-health vendors are deploying LLM-backed clinical decision support, EHR summarisers, and patient-facing chatbots. The regulatory backdrop is unusually unforgiving: EU AI Act high-risk classification, GDPR Article 9 special-category data, and national health-authority rules on top.
Penaxtra is an enterprise AI Security Posture Management (AI-SPM) platform. Because clinical prompts carry protected health information, its self-hosted runtime gateway redacts them before any upstream call, while scheduled adversarial tests exercise the LLM endpoints and RAG pipelines and file each finding against EU AI Act Annex III, GDPR, and ISO/IEC 42001 controls.
Patient-facing chatbots are visible. The deeper risk often lives in the retriever indexes that read EHR notes, lab reports, and drug-interaction monographs.
Drug-interaction warnings, triage suggestions, differential-diagnosis lists. EU AI Act Annex III high-risk by classification when the output materially shapes care. Adversarial inputs from EHR free-text and uploaded documents are the primary attack surface; overreliance from time-pressed clinicians is the secondary failure mode.
Reads progress notes, discharge summaries, and lab reports. PHI handling is the central control concern; downstream model providers must never see un-redacted patient identifiers. Adversarial content in pasted lab values can flip summary verdicts.
Retrieves drug monographs, treatment guidelines, and internal protocols. Corpus tainting attacks (a malicious clinical guideline excerpt seeded into the retriever index) can change recommendations. Canary-based testing is the standard control.
Symptom triage, appointment booking, post-discharge instructions. Prompt-injection from free-text patient input and sensitive-information disclosure from over-eager assistants are the headline risks. Excessive agency becomes material if the chatbot is given booking or prescription-renewal tool access.
LLM-assisted ICD-10 and CPT coding, claim drafting, appeal-letter generation. Bias and overreliance failures translate to denials and audit-trail gaps; auditors will ask how the model's recommendation was independently checked.
Managed foundation-model platforms and on-prem fine-tunes for de-identified workloads. Cloud-posture scanning surfaces mis-scoped IAM, undocumented model deployments, and orphaned dev endpoints that still hold PHI.
EU healthcare programmes typically have to satisfy all four simultaneously. Penaxtra produces evidence the same auditor can copy into the certification file.
| Regulation | Healthcare-specific scope | Audit expectation |
|---|---|---|
| EU AI Act (Reg. 2024/1689) | Annex I (medical device safety components), Annex III (essential service access) | Risk management system, robustness testing, post-market monitoring, automatic event logging, human oversight provisions. |
| GDPR (Reg. 2016/679) | Art. 9 special-category data; Art. 35 DPIA | Lawful basis under Art. 9(2)(h) or (i); documented DPIA; minimisation; security of processing; data-residency policy. |
| ISO/IEC 42001 | AI management system applied to clinical workflows | Annex A controls implemented; risk treatment plan; AIMS maintained alongside the existing ISO 27001 ISMS. |
| ISO 13485 + MDR (if device-classified) | Software as a Medical Device lifecycle | Design history file; clinical evaluation; change control; post-market surveillance integrated with the EU AI Act PMS programme. |
Useful for the initial threat model. Inadequate for the post-market monitoring obligation under EU AI Act Art. 72 and the ongoing assurance the Data Protection Officer is accountable for. A single report cannot demonstrate that the most recent model update is still robust under adversarial conditions.
Sends prompts to an external scanner service. Hard to authorise under GDPR Article 9 when the prompts contain PHI. The DPO will block the integration during the data protection impact assessment.
Catches some real-time prompt injection but does not satisfy the documented testing programme expectation. Auditors will ask for an annotated evidence trail of failure cases, severity scoring, and remediation; a block log alone does not provide that.
Removes obvious identifiers but does not test whether the model still leaks inferential PHI under adversarial prompts. The DPIA needs evidence of both controls (de-identification and adversarial assurance), not one or the other.
Clinical decision support endpoint, EHR summariser, RAG retriever for clinical guidelines, patient chatbot, coding assistant. Cloud AI accounts on the major platforms. Self-hosted fine-tunes inventoried alongside the managed services.
Go agent in front of upstream LLM calls. DLP patterns extended for TC kimlik numarası, national patient identifier formats, drug code patterns, and the customer's domain glossary. Block events streamed to the SOC SIEM and the DPO inbox where configured.
Canary documents seeded into the retriever index. Cross-tenant probes confirm a department's protocol does not leak across services. Embedding-space adversarial inputs check robustness of similarity scoring. Findings carry OWASP LLM06 and LLM08 references plus NIST AI 600-1 MEASURE control IDs.
OWASP LLM Top 10 baseline tuned for healthcare; overreliance and sensitive-disclosure probes weighted heavier than the default. Three-judge plus meta-judge consensus reduces single-model bias. PDF audit-evidence export on demand for the DPO file or external auditor.
| Before Penaxtra | After Penaxtra |
|---|---|
| PHI risk for upstream LLM calls handled by a separate de-identification middleware with no test evidence. | DLP block events visible in the gateway dashboard; redaction confirmed by replay against the same prompt class. |
| RAG corpus tainting risk acknowledged but not tested. | Canary tokens seeded; thirteen test patterns confirm guardrails fire when they should. |
| DPO request for adversarial-test evidence answered with a screenshot trail. | Answered with a PDF export carrying OWASP, NIST, EU AI Act, ISO 42001 control IDs and the per-probe rationale. |
| Mean time to remediate an overreliance regression: next quarterly internal audit. | Caught on the next weekly scan run; remediation backlog tracked in Jira with control IDs. |
| Framework | Healthcare-relevant identifier | How Penaxtra answers it |
|---|---|---|
| EU AI Act | Art. 9 (Risk management) + Art. 72 (Post-market monitoring) | Continuous scan programme; documented threat model; remediation backlog. |
| EU AI Act | Art. 10 (Data and data governance) | Asset inventory + DLP layer record; tenant-configurable retention. |
| EU AI Act | Art. 14 (Human oversight) | Per-finding rationale + control IDs make oversight decisions defensible; HITL hooks in agent tool catalogues. |
| EU AI Act | Art. 15 (Accuracy, robustness, cybersecurity) | Three-judge plus meta-judge consensus probe scoring. |
| GDPR | Art. 32 (Security of processing) + Art. 35 (DPIA) | Security measures documented in the DPA Annex II; per-finding evidence supports the DPIA review. |
| NIST AI 600-1 | MEASURE-2.3 (Test misuse-resistance) | Healthcare-tuned probe templates plus three-judge scoring. |
| NIST AI 600-1 | MS-2.3 (Test for sensitive data exposure) | DLP block events; RAG canary detection. |
| ISO/IEC 42001 | A.7.1 (Operational planning) + A.8.2 (Testing and evaluation) | Weekly scheduled scans; control-mapped evidence export. |
| OWASP LLM Top 10 | LLM06 (Sensitive information disclosure) | DLP patterns + RAG canary suite. |
| OWASP LLM Top 10 | LLM09 (Overreliance) | Probe templates targeting silent-agreement and fabrication failure modes. |
AI systems intended for use as safety components of medical devices fall under EU AI Act Annex I, and AI systems used by health-care institutions to triage emergency calls or evaluate patient eligibility for essential services fall under Annex III. Clinical decision support that materially shapes a diagnosis or care plan is treated as high-risk in either path. See eur-lex.europa.eu.
The runtime gateway runs inside the hospital network. DLP patterns can be tuned to local health-record formats, national patient identifier shapes, and the customer's domain glossary. The request that reaches the upstream provider is redacted; only allow or block decisions and finding metadata flow to the Penaxtra control plane.
RAG security runs drive the configured retrieval pipeline with canary-poisoned documents, cross-tenant probes, and prompt-injection payloads. Thirteen test patterns ship today across corpus tainting, retriever drift, embedding-space adversarial inputs, and tenant isolation breaks. See the RAG runs API documentation for the live record shape.
Yes. PDF and JSON export on demand. Each finding carries OWASP LLM Top 10 categories, EU AI Act article references, ISO 42001 Annex A control IDs, NIST AI 600-1 function tags, and MITRE ATLAS technique IDs. Twenty-two pre-computed cross-framework overlaps let one observation satisfy multiple audit requirements without manual re-mapping.
Auditors verify our control mapping against the same documents we read. Each item below points to the canonical publication.
Last reviewed:
Two-week pilot against one clinical-AI surface, with an EU AI Act + GDPR + ISO 42001 control-mapped report at the end.