The most proactive privacy scanner for AI applications

The
  • most proactive
  • deepest
  • fastest
  • most lightweight
  • most accurate
privacy scanner for AI applications
Privacy by design for the AI era. Build and scale AI apps without compromising user trust.

Trusted By

Discover

AI governance starts with visibility

import openai

import anthropic

import google.generativeai

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

from langchain.llms

from crewai

from llama_index

from pydantic

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, across code paths to detect exposure in LLM prompts and other risky mediums, stopping accidental leaks 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

Cookies

JSON Web Tokens

Guard

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

Apply precise allowlists for LLM prompts and other risky sinks to enforce compliance with Data Processing Agreements, automatically blocking unsafe changes in PRs that could result in privacy violations.

Redefining AI development with built-in privacy and data control

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, open-source agents, and homegrown LLM usage embedded directly in code, leaving security teams blind to Shadow AI.

Advantages of HoundDog.ai's Approach:

HoundDog.ai discovers AI models, SDKs, and agents directly in the codebase before they are deployed or granted access to data.

This early detection provides security and privacy teams with complete visibility into both sanctioned and unsanctioned AI usage across the development lifecycle.

Shadow AI is surfaced as part of the CI workflow, making it easy to block risky code before it creates compliance or security issues.

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 data like internal IDs or proprietary fields.
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.

Data Mapping and Privacy Assessments

Disadvantages of Current Approaches:

Privacy assessments are typically manual, relying on surveys or runtime observations that fail to capture what actually happens in the code.
Most tools provide no real visibility into how sensitive data flows into models, SDKs, or external APIs.
RoPAs and DPIAs are often incomplete or quickly outdated, especially in fast-moving engineering environments.

Advantages of HoundDog.ai’s Approach:

HoundDog.ai automatically maps data flows in code, showing where sensitive data is collected, processed, and shared, including through AI models.
It generates audit-ready RoPAs, PIAs, and DPA risk flags with evidence-backed insights from the code itself.
Privacy teams get continuous, real-time visibility into processing activities, without relying on self-reported surveys or manual discovery.

HoundDog.ai Coverage Across OWASP LLM Top 10

LLM01: Prompt Injection

LLM02: Insecure Output Handling

LLM03: Training Data Poisoning
LLM04: Model Denial of Service
LLM05: Supply Chain Vulnerabilitie

LLM06: Sensitive Information Disclosure

LLM07: Insecure Plugin Design
LLM08: Excessive Agency
LLM09: Overreliance
LLM10: Model Theft

LLM02: Insecure Output Handling

HoundDog.ai enforces guardrails on the types of sensitive data embedded in prompts and detects insecure patterns before code is deployed. This helps prevent LLMs from exposing sensitive data through their responses.

LLM06: Sensitive Information Disclosure

By scanning for accidental exposure of PII, PHI, CHD, and authentication tokens in logs, temporary files, and other risky mediums, HoundDog.ai proactively prevents unintentional sensitive data leaks.

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

IDE PLUGINS. (VS Code 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

  • Integrate the scanner into CI pipelines 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

DIY PII Detection Does Not 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.
Book a demo

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. (Coming in Q2 2025)

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

HoundDog.ai backed by Mozilla Ventures
HoundDog.ai backed by E14 Fund

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.