Trace every PHI flow directly in your application's source code to satisfy HIPAA 164.308(a)(1)(ii)(B) risk analysis, 164.312(b) audit controls, and 164.314(a)(2) business associate oversight, before code is ever pushed to production.
PHI exposures to risky data sinks like logs, third-party services, and AI integrations are rarely intentional. They happen as codebases grow. A developer prints a full user object, a tainted variable carries PHI through a chain of transformations, and by the time anyone notices, the data has already been logged or sent to a third party. HoundDog.ai traces every flow into every SDK, API, and AI integration directly in code, so Business Associate Agreement violations are caught at scan time, before any data leaves your application.
Risk analyses drift the moment a new release ships.
Medical history and identifiers reach OpenAI, Sentry, and Datadog without anyone signing off.
HIPAA fines are calculated per record and per category, multiplying fast.
HoundDog.ai operates inside the development pipeline, tracing how PHI actually flows as code is written and changed. Scans run locally. Your code never leaves your machine, and HoundDog.ai never touches production PHI.
Integrates with IDE plugins for VS Code, IntelliJ, and Cursor, and with CI pipelines. Analyzes source code to map PHI flows across logs, storage, APIs, third-party integrations, and AI services, including hidden or "shadow" integrations.
The taint-flow static analysis detects PHI elements by variable, method, function, and field name, traces them through intermediate transformations across files, functions, and procedures regardless of nesting depth, and flags them when they reach a sink, whether it is a controlled sink like a database or a high-risk one like an LLM prompt.
164.308(a)(1)(ii)(B) This continuous code-level scan replaces point-in-time risk analyses with one that updates on every commit.
Automated data flow mapping shows exactly which PHI and PII elements reach each data sink per repository, from logs and AI services like OpenAI to third-party integrations like Salesforce and Slack, with every flow rated safe or risky.
164.312(b) Every PHI sink becomes a known audit point with line-level code evidence, not a guess.
New PHI flows and subprocessors become suggested edits in your Org RoPA, each traceable to the code that generated it, with the privacy team reviewing and approving every change. The same evidence feeds your BAA inventory so you always know which vendors actually receive PHI, not just which ones were named in last quarter's review.
164.314(a)(2) Subprocessor changes surface as soon as a developer wires up a new SDK, weeks before they reach production.
Bake your HIPAA and BAA policies into the pipeline by customizing the types of data allowed per data sink, then block unsafe PHI flows when they are introduced in pull requests as part of your CI pipeline. Default allowlists ship out of the box, incorporating the standard data types each vendor typically covers under a BAA.
Unapproved PHI sharing is addressed while context is fresh and remediation costs are low. Preventive enforcement turns compliance from a paperwork exercise into an operational control.
Risk analysis, audit controls, and business associate oversight. Three Security Rule provisions where the textbook answer breaks once real code ships. Here is what changes when evidence comes from the codebase.
Accurate risk assessment of every ePHI flow across the system.
Mechanisms to record and examine activity in systems that handle ePHI.
Business associate contracts and satisfactory assurances PHI is safeguarded.
Purpose built for healthcare and AI-driven engineering teams that need HIPAA evidence grounded in real PHI flows detected directly from source code, not surveys or assumptions.
Detect and map PHI flows directly from source code across APIs, services, and third-party integrations without relying on surveys, spreadsheets, or production tools that miss hidden integrations and SDKs.
Discover AI SDKs embedded in code and detect PHI flowing into LLM prompts and external AI APIs before your apps go live, even when the data passes through helper functions and string templates.
Catch PHI oversharing during development and code review, not after an MRN has already been written to a non-healthcare CRM. Each finding ships with HIPAA rationale and remediation guidance.
Every PHI flow links to a specific line of source code, with HIPAA rule mapping and severity rationale. Your Org RoPA and PIA documentation stay current with suggested edits the privacy team can approve in one click.
HoundDog.ai performs static analysis on source code to map every PHI data flow across storage, logs, third-party integrations, and AI services. This produces a continuously updated risk surface that satisfies the periodic risk analysis requirement of HIPAA 164.308(a)(1)(ii)(B) using actual processing behavior, not interview-based estimates.
HIPAA 164.312(b) requires audit controls over PHI. HoundDog.ai builds a code-level inventory of every place PHI is read, written, and forwarded, and flags PHI reaching unintended sinks like logs or files before code ships. The result is a continuous, evidence-backed trail that maps to audit control requirements and shrinks the storage footprint of PHI.
HIPAA 164.314(a)(2) requires that PHI sharing with business associates is limited to what is permitted by the BAA. HoundDog.ai detects every third-party SDK and API call in code, traces which PHI elements flow into each one, and lets you define a per-vendor allowlist that fails the pull request when an unapproved PHI element is added.
No. HoundDog.ai runs entirely on source code in your development environment or CI pipeline. It never needs production access, never sees actual PHI, and never sends source code off your machine.
Yes. HoundDog.ai recognizes direct and indirect AI SDKs including OpenAI, Anthropic, LangChain, and LlamaIndex, and traces which PHI fields end up inside prompts or model inputs before the call ever executes in production.
Trace every PHI flow before it ships, shrink the storage footprint of patient data, and keep your BAA inventory honest with evidence pulled directly from source.