Privacy Code Scanning

A privacy code scanner that helps privacy and engineering teams detect PII leaks, trace sensitive data flows, and automate GDPR data mapping while code is being written, not after apps are live. If your app uses AI, APIs, or third-party integrations, traditional privacy tools are already too late.

The Problem

Privacy Risks Start in Code, Not After Deployment

Traditional privacy tools detect problems too late, when data is already in motion, pushing teams into remediation rather than prevention.

Sensitive Data in Logs & Local Storage

• DLP tools surface sensitive data in logs or local storage only after exposure, forcing weeks of reactive cleanup: tracing data paths, cleaning up logs, and rewriting code.
• Incidents often start with simple oversights like printing full user objects or passing tainted variables into logging functions.
• As applications scale and code paths multiply, these mistakes become harder to catch and more frequent.

Shadow AI & Third-Party Integrations

• Data shared with third party or AI integrations must align with Data Processing Agreements and your privacy notice.
• Silent code changes can redirect sensitive fields to analytics platforms, observability pipelines, or LLM prompts.
• These hidden shifts erode user trust and increase regulatory exposure long before privacy teams are aware.

Hidden Cross-Service Flows

• Sensitive data flows between microservices and APIs in ways teams cannot easily track or document.
• Cross repo dependencies over REST, GraphQL, or gRPC and complex code transformations defeat traditional scanning approaches.
• Sensitive data exposed through these APIs often goes undocumented, creating hidden privacy and compliance risk.

Sensitive Data in AI Prompts

• AI usage is accelerating, increasing the risk of unintentionally sharing sensitive data with external models.
• Many companies restrict AI services, yet scans routinely uncover AI SDKs like LangChain or LlamaIndex.
• Current privacy tooling is either too reactive, discovering these flows after the fact, or completely blind to them.

Existing Tools

Why Existing Tools Fail

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• Responses come back incomplete, outdated, or guessed.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.

GRC Platforms

• Provide blank RoPA, PIA, and DPIA templates, and leave privacy teams to interview engineers and collect data flows by hand.

Privacy Platforms Are Blind to the Codebase

• Privacy platforms infer flows after deployment, missing shadow AI and SDKs added in code.

How Privacy Code Scanning Works

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

Scan Code as It Is Written

Integrates with CI pipelines across GitHub, GitLab, Bitbucket, Jenkins, CircleCI, and Azure Pipelines. Analyzes source code to map sensitive data flows across logs, storage, APIs, third-party and AI integrations, including hidden or "Shadow" integrations.

Trace Sensitive Data Flows

Automated data flow mapping shows exactly which sensitive data elements reach each data sink per repository, with every flow rated safe or risky.

Surface Suggested Edits

New data flows and subprocessors become suggested edits in your Org RoPA, each traceable to the code that generated it.

Enforce Before Deployment

Bake your privacy policies into the CI pipeline: customize the data types allowed per data sink, and block unsafe data flows when pull requests introduce them.

The result

Stale Evidence: Documentation runs months behind the code. Drift: Documented activities drift from code each release. Exposure: Undocumented subprocessors create an Article 30 risk.

Key Differentiators

What Makes HoundDog.ai Different

Purpose-built for teams that detect sensitive data flows and automate GDPR data mapping directly from source code.

Code-Level Data Flow Intelligence

Detect and map sensitive data flows directly from source code across APIs, services, and third-party integrations, without surveys, spreadsheets, or privacy tools that miss hidden integrations and SDKs.

Make Privacy-by-Design a Reality in Your SDLC

Detect PII leaks, map sensitive data flows, generate GDPR data maps, PIA, and DPIA, and keep your RoPA current, before code reaches production.