HoundDog.ai ships two products on the same deterministic dataflow core. Both run static analysis on your code; each is tuned to a different audience and outcome.
Keeping processing activities in sync with the codebase fails today: surveys don't scale and produce stale or guessed answers, GRC platforms ship blank templates, and privacy platforms infer flows post-deployment, missing shadow AI and SDKs in code.
Stale evidence: documentation weeks or months behind the code, and reported processing activities that diverge from implementation with every release.
GDPR data mapping grounded in code-based evidence across logs, storage, APIs, third-party and AI SDKs, keeping your RoPA current at development speed so you prevent risks instead of reacting after the fact.
DSPM gives you posture, and DLP removes or redacts sensitive data, but only reacts once the data is already there. Neither helps with prevention.
When PII, PHI, or auth tokens leak into logs, remediation is reactive and disruptive, often hundreds of hours scrubbing logs, auditing access, and halting ingestion by third-party monitoring and SIEM tools.
Track 100+ sensitive data flows across nested code paths and transformations, and prevent exposure to risky sinks like logs or LLM prompts before any data starts flowing.
Every detected sensitive data flow rendered in one map: PII, PHI, cardholder data, and auth tokens traced from source to every sink, including logs, storage, APIs, third-party SDKs, and AI integrations. Built from code, not surveys.
HoundDog.ai operates inside the development pipeline. 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 data flows and subprocessors become suggested edits in your Org RoPA, each traceable to the code that generated it.
For processing activities outside the scope of scanned applications, such as Support or Sales, a collaborative workflow lets you invite stakeholders to review and make suggestions, while the privacy team keeps track of all processing activities in one place with full historical tracking.
Bake your privacy policies into the pipeline by customizing the types of data allowed per data sink and blocking unsafe data 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 in Data Processing Agreements per data sink, e.g. Stripe's allowlist includes bank card details whereas Slack's does not.
At 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.
DLP reacts once sensitive data is already written. HoundDog.ai traces it into the log statement at scan time, before it ever executes.
String msg = String.format( "%s charged %s %s to the %s %s held by %s", merchant.getName(), amount, currency, card.getType(), card.getLast4(), cardholder.getName()); log.warn(msg); // cardholder + card data traced before it runs
WARN Uber Eats charged 148.27 USD to the CREDIT VISA-4242 held by Sarah Johnson ([email protected])
log.debug("token refresh failed {}", provider, grantType, refreshToken, ex) // secret traced before it runs
DEBUG token refresh failed provider=salesforce Refresh Token eyJhbGciOiJIUzI1 NiIsInR5cCI6IkpXVCJ9...
Model selection matters less than the harness around the agent. Give Claude Code, Cursor, and Codex a centralized API and dependency graph, refreshed at development speed, so they reason about cross-repo change without anyone checking out the full codebase locally.
HoundDog.ai Dataflow Context Engine integrates with your existing AI agents using mainstream methods including a local MCP server, CLI, and Skills.
Connect via MCP, CLI, or Skills. Works locally with your AI coding agents.
Free tier runs on the developer's machine. Enterprise runs in your tenanted cloud or fully on-premises. Your code never leaves your infrastructure either way.
The engine is extremely lightweight and fast. Built in Rust, it can analyze millions of lines of code in less than a minute.
The larger and more complex the codebase, the more valuable HoundDog.ai Dataflow Context Engine becomes. It thrives where other tools struggle.
HoundDog.ai Dataflow Context Engine supports any AI coding agent that implements the MCP protocol.
Two deployment models for two scales. Local for individuals and small repos. Centralized for the codebases no developer can keep checked out on a laptop.
The MCP server runs as a local process and indexes whatever code is checked out locally. No infrastructure to stand up. Free.
Cloud or on-premises. Connects directly to GitHub, Bitbucket, or GitLab. Auto-scans every selected repo from your SCM, with no local checkout needed. Developers and AI agents query a centralized catalog that already holds the answer.
Whether you're building a privacy program from code or wiring up AI coding agents at scale, start in minutes.