The most proactive privacy code scanner

The

most proactive
deepest
fastest
most lightweight
most accurate

privacy code scanner

Stop chasing data flows after deployment. Find shadow AI, third party SDKs, and log leaks in code and auto generate RoPA, PIA, and DPIA at development speed.

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Trusted By

Discover

AI governance starts with visibility

import openai

import anthropic

import google.genai

import semantic_kernel

Discover all AI integrations including shadow AI and track connections to every embedded provider, SDK, and framework in your codebase

+ many others

import langchain

import crewai

import llama_index

import pydantic_ai

Trace

Track sensitive data across code - no matter how deep it’s buried

We track over 100 sensitive data types, like PII, PHI, CHD and auth tokens, through complex transformations and nested data flows to detect exposure in LLM prompts, third-party SDKs and other risky mediums before code reaches production

Detect developer (or AI-generated) mistakes that leak sensitive data into logs, files, local storage, and other risky areas.

Logs

Files

Local Storage

JSON Web Tokens

Guard

Enforce privacy rules and stop risky code before it reaches production.

Ensure AI and third-party data flows match your privacy notices. Apply precise allowlists to block risky code before production.

Replace questionnaires and production tools with code level data flow mapping

AI Governance & Shadow AI Discovery

Disadvantages of Current Approaches:

Most platforms rely on identity providers or network traffic to detect AI usage, which only reveals tools that have already been authorized or actively used.

These methods miss AI SDKs used directly in code, including LLM providers like OpenAI and Anthropic and orchestration layers like LangChain, leaving privacy teams blind to Shadow AI.

Advantages of HoundDog.ai's Approach:

HoundDog.ai detects AI SDK usage in code including LLM providers and orchestration layers before deployment or data access.

Undocumented AI data flows are flagged in CI so teams can assess privacy impact and block risky code before production

AI Governance & Shadow AI Discovery

Disadvantages of Current Approaches:

Most platforms rely on identity providers or network traffic to detect AI usage, which only reveals tools that have already been authorized or actively used.

These methods miss AI SDKs used directly in code, including LLM providers like OpenAI and Anthropic and orchestration layers like LangChain, leaving privacy teams blind to Shadow AI.

Advantages of HoundDog.ai's Approach:

HoundDog.ai detects AI SDK usage in code including LLM providers and orchestration layers before deployment or data access.

Undocumented AI data flows are flagged in CI so teams can assess privacy impact and block risky code before production

Data Mapping and Privacy Assessments

Disadvantages of Current Approaches:

Privacy assessments rely on questionnaires or production only signals that drift from reality as code changes.

Most tools lack code level visibility into how sensitive data flows through AI SDKs, third-party integrations and APIs.

RoPA, PIA, and DPIA reports become outdated quickly in fast moving engineering environments.

Advantages of HoundDog.ai’s Approach:

Automatically maps data flows in code, showing where sensitive data is collected, processed, and shared, including through AI models and third-party SDKs.

Auto generates audit-ready RoPAs, PIAs, and DPIAs with evidence-backed insights from the code itself.

Gives privacy teams continuous visibility into processing activities without surveys or manual discovery.

Prompt Governance, Data Minimization, and Leak Prevention

Disadvantages of Current Approaches:

Runtime filtering tools inspect and sanitize prompts only after applications are live and communicating with LLMs.

They rely on pattern matching and often miss organization-specific sensitive data types.

Without code-level visibility, these tools can’t trace how sensitive data entered a prompt, making prevention difficult

Advantages of HoundDog.ai’s Approach:

HoundDog.ai detects sensitive data in prompts during development, before any data is exposed to models or third parties.

It enforces allowlists at the code level, blocking unapproved data types in PRs and CI workflows.

By tracing data through function calls and transformations, it uncovers risks that reactive tools miss.

This proactive, shift-left approach helps teams prevent leaks at the source rather than patching them later.

Enabling PII Leak Detection & Data Mapping Across All Stages of Development

IDE PLUGINS. (VS Code, Cursor, IntelliJ, and Eclipse)

Highlights PII leaks as code is being written

Managed Scans

Offload scanning to HoundDog.ai with direct source control integrations

CI/CD Integrations

Use HoundDog.ai's direct source code integrations to automatically push CI configurations and embed the scanner for pre-merge checks

HoundDog.ai - Protecting All Stages of Development

DIY PII Detection Doesn’t Scale

Hardcoded RegEx rules break easily and are a nightmare to maintain. Most DIY efforts stall before they scale

HoundDog.ai: Purpose-Built for PII Detection & Data Mapping

Catch PII leaks early with IDE plugins, Managed Scans, and CI/CD integration. Get data maps at the speed of development - no more manual tracking or stale documentation.

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Unparalleled Coverage and Accuracy

Built-in detection with extensive coverage across:

Sensitive data elements (PII, PHI, PIFI, CHD)
Risky data sinks (including hundreds of third-party integrations)
Sanitization functions (flag only when data isn’t properly sanitized)

Endless Flexibility

Finetune detection across data elements, sinks, and sanitization to fit your environment.

Ready to Scale

Connect to GitHub, GitLab, or Bitbucket to scan code, block PRs, and leave actionable comments – automatically.
Managed Scans: Offload scanning to HoundDog.ai for continuous, hands-off coverage
CI Jobs: Push CI configs to selected repos using your self-hosted runners, with options for direct commits or approval-based PRs

AI-Ready

AI-powered detection that plugs into any LLM running in your environment – boosting coverage across data elements, sinks, and sanitization, while minimizing manual tuning.

Return On Investment

ROI for Proactive Sensitive Data Protection

For Every1mLines of Code

Time Saved 4,000Hours

Productivity Gain2Full-Time Employees (FTEs)

ROI for Automated Privacy Compliance

For Every200Code Repositories

Time Saved3,200Hours

Productivity Gain1.5Full-Time Employees (FTEs)

Check out our ROI calculator for an estimation tailored to your organization's inputs.

Go to ROI

Backed by Incredible Investors

Sensitive Data Protection at the Speed of Development

“For companies handling sensitive data, HoundDog.ai is a real must-have. The scanner is blazingly fast and integrates seamlessly with our GitLab workflow. More importantly, it provides the peace of mind we need by ensuring that sensitive data does not accidentally leak into logs, files, or third-party systems, even with high frequency updates to the codebases.”

Bryan Kaplan, CISO
Juvare

Make Privacy-by-Design a Reality in Your SDLC

Shift Left on Privacy. Scan Code. Get Evidence-Based Data Maps. Prevent PII Leaks in Logs and Other Risky Mediums Early—Before Weeks of Remediation in Production.

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