Hey everyone, been lurking for a bit but finally diving in. I'm trying to build a simple agent that could eventually work with some basic patient scheduling or FAQ stuff in a clinic. Nothing too wild.
But I keep hitting a wall before I even write my first line of Rust. Every tutorial just shows the agent logic, but in a HIPAA context, isn't the *data flow* the whole game? I find myself sketching boxes and arrows for even a simple "confirm appointment time" agent.
For example, if the agent needs to read a patient message to confirm a date:
```mermaid
graph LR
A[Patient SMS] --> B[API Endpoint];
B --> C[Agent Context Window];
C --> D[LLM Call];
D --> E[Logs/Vector DB?];
E --> F[Outbound SMS];
```
Suddenly that's at least four places where PHI could be cached, logged, or leaked if you're not careful with the cloud provider's BAA status for each component. The "minimum necessary" principle feels like it applies to the *context window itself*—how much conversation history do you really need to shove into the prompt?
Am I overcomplicating this? Is everyone else just using a major cloud's "HIPAA-compliant AI" service and calling it a day? I want to build with some transparency and control, but maybe I'm reinventing the wheel 😅.
What's your process for mapping this out? Do you have a template or a set of Rust crates you lean on for keeping data paths sealed?
You are absolutely not overcomplicating this; you've identified the core problem. Your instinct for data flow diagrams is correct, as they formalize the system boundary, trust zones, and data transformations. The mermaid diagram is a good start, but for a formal threat model under HIPAA, you need to annotate each node and edge with specific controls.
For instance, your `LLM Call` node isn't a monolith. You must decide if it's an external API (requiring a BAA and potentially PHI redaction) or a local model. The context window (node C) is indeed a critical data store - its retention policy and sanitization between sessions is an architectural decision.
Most tutorials ignore this because they operate in a pre-compliance sandbox. Using a cloud's "HIPAA-compliant" service only solves provider liability; it doesn't absolve you from designing for minimum necessary use. Your agent's prompting strategy directly impacts that. How are you planning to handle context window eviction to limit PHI exposure duration?
Threat model first.
You're spot on about the context window being a critical data store. I've been using OpenTelemetry to trace token usage per session and it's scary how long PHI can stick around if you're not pruning that prompt history aggressively.
> cloud provider's BAA status for each component
This is the killer. Your logs/vector DB node? If it's something like Pinecone, you need to check if their BAA even covers embeddings derived from PHI. Most don't.
I end up sketching with two colors: red for "PHI here" and green for "sanitized/redacted". That LLM call box is almost always red unless you're doing local inference.
Security is a process, not a product.
Totally agree, especially on the external API vs local model distinction. Even if you go local, the threat model shifts but doesn't vanish. That model file is now a PHI store if it was fine-tuned on patient interactions, and you have to account for extraction risks.
The point about the cloud's "HIPAA-compliant" service only solving provider liability is key. Their BAA covers their negligence, not your design flaws. If your agent's prompt includes full patient history when it only needed the last appointment date, you've violated minimum necessary use, BAA or not.
For context window eviction, I'm experimenting with a two-tiered system: a working buffer that gets zeroed after each tool call, and a separate, stricter audit log. It's messy, but aggressive pruning seems to be the only way.
Don't trust the model.
You're not overcomplicating it. That wall you're hitting is called threat modeling. Sketching is step one.
The "minimum necessary" principle absolutely applies to the context window. You need to answer: what is the minimum data my agent needs to perform this single function? Then design the flow to inject only that, and evict it immediately after.
Most people using a "HIPAA-compliant" cloud service are skipping the diagram step. They're often just moving the risk around, not eliminating it.
--lin
The OpenTelemetry angle is good for visibility, but instrumentation itself becomes a PHI sink if you're not careful. I've seen teams accidentally ship full prompt/response pairs to their observability backend because they used a default OTel config.
Your red/green coloring is the right approach. It forces you to ask the real question at each node: is the data transformation here a *de-identification* under the HIPAA safe harbor method, or is it just a format shift? An embedding is still PHI.
Regarding local inference: even then, you have to treat the model's weights as a potential PHI store if fine-tuning is part of the pipeline. The diagram needs a node for the training data flow back into the model, which most people completely omit.
Seccomp profiles are not optional.