Two products on one engine. Privacy Code Scanner for proactive shift-left privacy and GDPR data mapping at dev speed. Dataflow Context Engine that maps every service, API, and field across monorepos and microservices, so AI coding agents run 5× faster and cheaper on average.
API specs alone don't cover the services and fields consuming your APIs.
Without centralized dataflow context, agents burn tokens grepping repos and writing ad-hoc bash scripts to parse code relationships, often on code not even checked out locally, leaving AI with an incomplete picture.
An MCP server and Skills continuously fetch the exact cross-repo context, so prompting your agent to update a service or API runs 5× faster and cheaper, with the full picture.
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.
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.
Optional AI analysis layered on static findings auto-closes false positives, adjusts severities, and adds context. Scanning still runs locally on cheap CPU; AI only interprets traces the scanner already detected.
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...
For engineering teams & AI coding agents. HoundDog.ai builds a full service catalog of every API, every field, and every downstream consumer across your repos. Local tools see one repo; the Context Engine sees the whole organization, so your AI coding agent changes a service or field knowing exactly what depends on it.
Run it on your machine and plug into any AI agent.
Continuous, organization-wide context, fully managed.
Auth tokens and passwords in logs or local storage, caught at scan time.
PII/PHI to integrations that don't match published privacy notices.
AI-generated apps embed GDPR & CCPA best practices from day one.
less manual data mapping. Automated reporting across 15,000 repos and stronger HIPAA compliance.
saved. PII leak incidents cut from five a month to zero across 500 repos.
of AI integrations were Shadow AI, some without a DPA. Now flagged as suggested RoPA edits.
Automate GDPR data mapping at the speed of development, get suggested edits to your RoPA backed by code evidence, and give AI coding agents real API context.