Healthtech companies are shipping AI features faster than their security programs can keep up. Clinical documentation assistants, patient-facing chatbots, RAG systems pulling from electronic health records, and autonomous agents that triage messages or schedule care are all now in production at companies that, two years ago, had none of this in scope. The problem is that most of these systems were built and tested for accuracy, not for security. Nobody red-teamed them against a hostile prompt.
That gap matters more in healthtech than almost anywhere else. A jailbroken customer support bot at a retailer is embarrassing. A jailbroken clinical assistant that leaks protected health information (PHI), fabricates a dosage recommendation, or lets an attacker pivot from a chat interface into a patient record system is a regulatory incident, a breach notification, and a trust problem your sales team will be fighting for a year.
Why healthtech carries higher stakes
Three things make LLM security a different conversation for healthtech than for a generic SaaS company.
First, the data. Large language models deployed in clinical or administrative workflows are almost always connected to PHI, whether directly through a database or indirectly through retrieval-augmented generation (RAG) pipelines that pull patient records, lab results, or claims data into the model's context window. If that retrieval layer isn't scoped correctly, a low-privilege user can craft a query that returns another patient's data. This is not a hypothetical, it's one of the most common findings in RAG-backed systems that haven't been tested.
Second, the regulatory exposure. PHIPA in Ontario, PIPEDA federally, and HIPAA for any company touching US patients all treat an AI-driven data disclosure the same way they treat any other breach: notification obligations, potential fines, and a very uncomfortable conversation with enterprise customers who signed a business associate agreement or a data processing addendum on the assumption your AI systems were tested. If you're pursuing SOC 2 or preparing for enterprise procurement, unreviewed AI systems are increasingly a specific line item auditors and security questionnaires ask about.
Third, the agentic risk. A growing number of healthtech products give an LLM tool-calling access, letting it query a scheduling system, send a message, update a record, or trigger a workflow. Every tool you hand an agent is a new attack surface. If an attacker can influence the model's input (through a message, an uploaded document, or even a field in a patient intake form), and the model has a tool that writes to a system of record, you have a path from a chat box to unauthorized data modification.
What actually gets tested
An AI security assessment for a healthtech product isn't a generic penetration test with an LLM bolted on. It follows the OWASP LLM Top 10 as a baseline and adapts it to how your product actually uses AI. In practice, that means testing for:
- Prompt injection, both direct (a user typing adversarial instructions) and indirect (malicious instructions hidden in a document, email, or web page the model later ingests).
- RAG leakage, verifying that retrieval scoping actually enforces per-user or per-tenant boundaries, and that the model can't be coaxed into surfacing data outside the requester's authorization.
- Agent tool abuse, probing whether an attacker can manipulate the model into calling tools it shouldn't, with parameters it shouldn't, on behalf of a user it shouldn't be able to impersonate.
- Output handling and insecure integration, checking whether model output is trusted downstream in ways that create injection or execution risk in connected systems.
- Data and model supply chain issues, including how third-party models, fine-tuning data, and plugins are vetted before they touch patient data.
The goal isn't a checklist exercise. It's a working exploit chain, where possible, that shows exactly how a real attacker would get from an unauthenticated or low-privilege entry point to PHI exposure or unauthorized action, along with a clear remediation path your engineering team can act on without a six-month rewrite.
How traztech scopes this for healthtech companies
We start with an architecture review before any testing begins. We need to understand what the model can see, what it can touch, and who it's supposed to be able to see and touch on behalf of. For a healthtech company, that usually means mapping the RAG retrieval layer against your actual data access model, not just the happy path.
From there, our AI security assessments combine manual adversarial testing led by Jacob Masse, a published CVE researcher, with structured coverage of the OWASP LLM Top 10 categories relevant to your build. We test the way an attacker actually would: crafted prompts, poisoned documents, adversarial file uploads, and tool-call manipulation, not just automated scanners running canned payloads against your API. The output is a report your engineering and compliance teams can both use: technical findings with reproduction steps for developers, and a plain-language summary of PHI and regulatory exposure for whoever owns your compliance posture. If you're also working toward SOC 2 or building out a broader compliance program, we scope the AI assessment to feed directly into that evidence trail rather than sitting as a separate exercise.
The cost of skipping this
Most healthtech leaders we talk to know their AI features carry risk. What they underestimate is how quickly that risk becomes a blocker. Enterprise health system procurement teams are starting to ask specific questions about LLM testing in security questionnaires. Cyber insurance underwriters are starting to ask too. And a single incident involving PHI moving through an untested model is exactly the kind of story that ends up in a breach disclosure, not a case study.
Testing your AI systems before an attacker, an auditor, or a regulator finds the gap is a lot cheaper than the alternative, and for a healthtech company it's fast becoming table stakes rather than a nice-to-have.
If your product uses an LLM anywhere near patient data, contact traztech to talk through scoping an AI security assessment for your specific architecture.