Discover every AI integration, including Shadow AI, directly in source code. Trace which sensitive data flows into LLM prompts before deployment. Keep your RoPA, AI inventory, and DPIA in sync with what your applications actually do.
Most AI inventories, RoPA entries, and DPIAs fall out of sync for one simple reason. They are created after systems are designed, AI integrations are live, and data flows are already in motion.
Never ending questionnaires flood engineering with every release.
Data Processing Agreement violations at best, GDPR fines at worst.
Sensitive data leaks into logs and spreads across ingestion systems before anyone is aware.
HoundDog.ai operates inside the development pipeline, tracing how sensitive data actually flows to AI systems as code is written and changed. Scans run locally. Your code never leaves your machine.
Integrates with IDE plugins for VS Code, IntelliJ, and Cursor, and with CI pipelines. Analyzes source code to map sensitive data flows across logs, storage, APIs, third-party and AI integrations, including hidden or "Shadow" integrations.
The taint-flow static analysis detects sensitive data elements by variable, method, function, and field name, tracing them through intermediate transformations across files, functions, and procedures regardless of nesting depth, and flagging them when they reach a sink, whether it is a controlled sink like a database or a high-risk one like an LLM prompt.
Automated data flow mapping shows exactly which sensitive data elements reach each data sink per repository, from logs and AI services like OpenAI to third parties like Slack, Stripe, and Twilio, with every flow rated safe or risky.
New AI integrations and the categories of sensitive data they receive become suggested edits in your Org RoPA and AI inventory, each traceable to the code that generated it, with the privacy team reviewing and approving every change.
Auto-generate Privacy Impact Assessments and Data Protection Impact Assessments pre-populated with detected AI data flows and risks, aligned with GDPR, the EU AI Act, HIPAA, and other frameworks. Because assessments are grounded in actual processing behavior, they accurately document which AI systems receive data and which categories of personal and sensitive data are involved.
Bake your AI policies into the pipeline by customizing the types of data allowed per AI provider and blocking unsafe flows when they are introduced in pull requests as part of your CI pipeline. Default allowlists are available out of the box, incorporating the standard data types expected per provider, e.g. an internal LLM endpoint's allowlist differs from a public AI API.
Unapproved AI data sharing is addressed while context is fresh and remediation costs are low. Preventive enforcement turns governance from advisory documents into operational controls.
Watch a live demo of HoundDog.ai discovering AI integrations from source code, tracing PHI and PII into LLM prompts, and turning each finding into evidence privacy and security teams can act on, before anything ships.
A walkthrough of the scanner running against a real codebase, surfacing AI integrations, tracing sensitive data into prompts, and producing the artifacts privacy teams need to keep AI processing activities in sync.
Watch NowAt development speed. Prevent risks instead of documenting them after the fact, with privacy teams in control: the engine proposes, the DPO approves.
All third-party and AI integrations detected directly in source code, including Shadow AI, whether the data flows through an SDK or API, with 1,000+ integrations covered out of the box.
Trace 100+ sensitive data types (PII, PHI, CHD, auth tokens) across code paths and into every data sink, including logs, storage, APIs, third-party, and AI integrations.
Keep your RoPA updated as new categories of personal data and subprocessors are introduced, detected directly from source code.
Validate design-phase privacy reviews with code-based evidence before code is pushed to production.
Purpose built for teams that need AI governance grounded in real data flows detected directly from source code, not surveys or assumptions.
Detect and map sensitive data flows directly from source code across APIs, services, and third party integrations without relying on surveys, spreadsheets, or privacy tools that miss hidden integrations and SDKs.
Discover AI SDKs embedded in code and detect sensitive data flows to LLM prompts and external AI APIs before your apps go live.
Catch privacy issues during development and code review, not after data has already been logged, shared, or leaked.
Automatically generate audit ready PIA and DPIA documentation, and keep your RoPA current through scanner suggested edits, all from detected code level data movement so compliance stays up to date as systems evolve.
Detect every AI integration, trace sensitive data into LLM prompts, and keep your AI inventory and RoPA in sync with what your applications actually do.