Privacy Code Scanning: Detect PII Leaks in Code

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

Sensitive data appearing in logs or local storage forces organizations into reactive cleanup.
DLP tools surface problems only after exposure, sending teams into weeks of 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.
As a result, sensitive data exposed through these API protocols often goes undocumented or poorly understood, 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.
Privacy teams scramble to understand what data is sent to AI systems and whether user notices and legal bases cover those flows.

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Responses come back incomplete, outdated, or guessed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

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

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 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.

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

GDPR Data Mapping

Build Customer Trust with Transparent Data Handling and GDPR Data Mapping

Automatically generate GDPR data mapping and data flow maps directly from source code to show where sensitive data is collected, processed, and shared across functions, APIs, third party services, and AI integrations.
Keep your Org RoPA continuously updated with new data flows and subprocessors surfaced as suggested edits at the speed of development, giving privacy teams a centrally managed record across all processing activities, not just custom apps.
Validate privacy reviews with code-level evidence before code ships, ensuring what was approved at the design stage is consistent with what was actually implemented. This ensures Privacy Impact Assessments (PIA) and Data Protection Impact Assessments (DPIA) are pre-populated with detected sensitive data flows and privacy risks, aligned with GDPR, CCPA, HIPAA, and other regulatory frameworks.
Detect sensitive data flows with a shift-left approach that gives privacy and security teams prevention, stopping privacy risks before the data ever starts flowing.

HoundDog.ai privacy code scanner flagging critical PHI exposures to OpenAI and Sentry, with sensitive data elements mapped to data sinks and tagged PHI or PII

Key Differentiators

What Makes HoundDog.ai Different

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

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.

Flagship Deployment

HoundDog.ai + Replit

45M

Users protected

10k

Scans daily

100+

Data types

Detects leaks before publishing

Auth tokens and passwords in logs or local storage, caught at scan time.

Flags unscoped AI & third-party flows

PII/PHI to integrations that don't match published privacy notices.

Privacy by default, not retrofit

AI-generated apps embed GDPR & CCPA best practices from day one.

Replit Security and Privacy Scanner, with privacy scans powered by HoundDog.ai, flagging a Medical Record Number sent to Standard Output with GDPR, CCPA, HIPAA, and NIST compliance frameworks and a Fix with Agent remediation

Enterprise Ready

Built for Enterprise-Grade Security

Designed to meet the requirements of large, security-conscious organizations.

SOC 2 Compliant Code Never Leaves Your Environment No Production Data Required Hands-On Support

Built for Enterprise Teams

Trusted by Replit, running 10,000+ privacy scans per day to help 45M creators bake privacy into the earliest stages of prototyping and app creation
Used by Fortune 1000 companies across technology, healthcare, and finance
SOC 2 compliant, with a transparent Trust Center offering access to the latest SBOM and penetration testing reports
Hands on, highly responsive customer support

Secure by Default

No production data or runtime ingestion required
Runs locally in your environment or CI pipelines
Secure broker for self hosted source control systems that meets strict network and data handling standards
Transparent Trust Center with up to date SBOM and penetration testing reports

FAQ

Frequently Asked Questions

What does HoundDog.ai scan?

HoundDog.ai scans application code to identify sensitive data flows across functions, APIs, third-party services, and AI integrations. The free Privacy Code Scanner supports Python, JavaScript, and TypeScript, and the Enterprise edition adds C#, Go, Java, SQL, GraphQL, and OpenAPI.

Does this require production access?

No. HoundDog.ai runs entirely in your development environment or CI pipeline, analyzing source code statically. It never needs access to your production database, runtime data, or live systems.

How is this different from DLP or runtime monitoring tools?

DLP and runtime monitoring tools detect exposure after data is already flowing in production. HoundDog.ai is a source-code scanner that catches privacy issues during development, before any data ever leaves your systems. It also pre-populates PIA and DPIA documentation and keeps your RoPA current with suggested edits, which runtime tools cannot do.

Does it support AI and LLM use cases?

Yes. HoundDog.ai was built with AI-first workflows in mind. It can detect AI SDKs embedded in your code (LangChain, LlamaIndex, OpenAI, Anthropic, etc.) and trace which sensitive fields flow into LLM prompts, giving you visibility before those calls happen in production.

Can it help with compliance reporting?

Yes. HoundDog.ai keeps your Org RoPA continuously updated by surfacing new data flows and subprocessors as suggested edits, with the privacy team reviewing and approving every change. It also pre-populates audit-ready Privacy Impact Assessments (PIA) and Data Protection Impact Assessments (DPIA) with the sensitive data flows and privacy risks it detects in code, so compliance documentation stays current as your codebase evolves.

Privacy Code Scanning