GDPR Data Mapping Software: RoPA, PIA & DPIA

The Problem

Why Processing Activities Fall Out of Sync with the Codebase

Privacy teams rely on three workflows today, and none of them keeps up with modern development.

Manual Documentation Does Not Scale

Engineering gets flooded with privacy questionnaires every release
Responses come back incomplete, outdated, or guessed
The cycle repeats with every code change, so records lag behind by design

Works at 10 apps. Breaks at 1,000.

GRC Platforms

Provide blank RoPA, PIA, and DPIA templates, like this one from Vanta, and rely on privacy teams to manually interview engineers and collect data flows
The process must be repeated every time code changes, making it slow and unreliable at scale

Ships the template, not the data flows.

Privacy Platforms Are Blind to the Codebase

Privacy platforms infer flows after deployment, missing shadow AI and SDKs added in code
They rely on predefined knowledge of third party services, leaving them blind to new integrations introduced directly in code
They never see what developers actually shipped until personal data is already flowing

Looks at production. Never at the code.

The result

Stale Evidence

Documentation runs weeks or months behind the code.

Drift

Documented activities diverge from implementation every release.

Exposure

Subprocessors slip into production undocumented, an Article 30 risk.

How It Works

Code-Level Visibility From IDE to CI

HoundDog.ai operates inside the development pipeline. Scans run locally. Your code never leaves your machine.

Scan Code as It Is Written

Integrates with IDE plugins for VS Code, IntelliJ, Eclipse, and OpenAI Codex, 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.

Source code defines how data flows into files, logs, databases, APIs, AI prompts, and third-party integrations

Trace Sensitive Data Flows

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.

More than 100 sensitive data types supported, spanning traditional PII per GDPR's definition, PHI per HIPAA's definition, CHD per PCI's definition, and auth tokens and secrets, which can pose a serious data breach risk when exposed in logs.
More than 1,000 integrations supported, including direct and indirect AI SDKs, many of which are embedded in code without an established Data Processing Agreement, and third-party integrations spanning monitoring, SIEM, sales and marketing, payment, and many other categories.

Automated data map by data sink showing which PII and sensitive data elements flow to logs, OpenAI, Slack, Stripe, and Twilio per repository

Surface Suggested Edits

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.

Suggested edit to the RoPA subprocessor list with DPA status, queued for review

Enforce Before Deployment

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.

Stripe data sink rule with trust mode and customizable safe data elements allowlist

See It in Action

Privacy Impact Assessments and Third-Party Risk, Driven by Code

Watch HoundDog.ai discover third-party SDKs in source code, map GDPR data flows, and auto-generate a Privacy Impact Assessment so privacy teams can validate reviews with code-level evidence.

Watch Now →

Discover

Every integration, straight from the code

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.

OpenAI

Anthropic

LangChain

Salesforce

Datadog

HubSpot

LLM Prompts

Third-Party SDKs

Logs

Files

Local Storage

Many Others

Trace

Follow sensitive data into every sink

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.

Verify & Suggest

RoPA that keeps itself current

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.

Suggest

Org RoPA updates

Verify

Alignment with PIA

Block

Risky data flows

Catch

Log leaks early

Full Data Map

Automated Data Mapping for GDPR Compliance

Unlike GDPR compliance software that relies on questionnaires, HoundDog.ai builds the data map from code. PII detection covers more than 100 sensitive data types spanning PII, PHI, cardholder data, and authentication tokens, plus custom patterns for proprietary fields that standard scanners miss. Processing purposes are derived from actual application behavior, third-party recipients and AI endpoints are identified from real integration points in code, and the resulting data map holds up when a supervisory authority requests your records.

Full sensitive data flow map generated by HoundDog.ai from source code

Key Differentiators

What Makes HoundDog.ai Different

Purpose built for keeping records of processing activities accurate from source code, not surveys.

Data map of critical sensitive data flows showing Auth Token, Passport Number, and Visa Information flowing into the Acme service

Code-Level Data Flow Intelligence

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.

HoundDog.ai tracing Medical History PHI through patient_context into a LangChain SystemMessage and an llm.invoke call sent to OpenAI

Built for AI & LLM Workloads

Discover AI SDKs embedded in code and detect sensitive data flows to LLM prompts and external AI APIs before your apps go live.

Critical auth token exposure finding with compliance framework tags and the console.log code segment leaking apiKey and apiSecret

Prevent Risk Before Deployment

Catch privacy issues during development and code review, not after data has already been logged, shared, or leaked.

Org RoPA review awaiting approval with a suggested edit to categories of personal data generated from code scanning

Compliance from Real Data Flows

Automatically generate GDPR data mapping along with 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.

Automated GDPR Data Mapping, RoPA, and Privacy Assessments