HoundDog.ai - Privacy Code Scanner and Dataflow Context Engine

HoundDog.ai is an ultra-fast, deterministic context engine that scans source code and provides your AI agents where sensitive data flows, and how your APIs and services connect.

Two capabilities, one scan

Privacy Code Scanner

Detects sensitive data flows and leaks in your code, mapping 100+ sensitive data elements across 800+ data sinks.

It answers questions such as:

• What data is processed? (e.g., personal data, financial data, health data)
• Where is data stored? (e.g., logs, files, databases)
• Who is data shared with? (e.g., AWS, Stripe, internal microservices)

It is useful for:

• Early prevention of data leaks during development.
• Automated and evidence-based data mapping for privacy compliance (e.g., GDPR, HIPAA).
• Reducing engineering fatigue, stale data inventories, and regulatory fines.

Dataflow Context Engine

Builds a live, cross-repo catalog of every gRPC and Apache Thrift connections, so AI coding agents have reliable context instead of rediscovering it on every prompt.

It answers questions such as:

• What gRPC/Thrift services exist, and which methods do they expose?
• Where is each service defined, and who calls it? (file, line, branch, commit)
• What breaks across repositories if I change or remove a field or an RPC?

It is useful for:

• Giving AI coding agents deterministic, up-to-date context to reason about API and service changes.
• Reducing wasted tokens and time spent re-analyzing the codebase on every prompt.
• Keeping a service catalog accurate at the speed of development, with no manual upkeep.

HoundDog.ai in action:

Technical highlights:

• Runs as a standalone binary on your machine. Your code never leaves your environment by default. For organization-wide, in-network deployment, see self-hosted installation.
• Fast and ready for large codebases. It can scan 1 million+ lines of code in seconds on modern laptops.
• Deterministic static analysis. The same commit produces the same result, every time.
• Supports 100s of data elements and sinks out of the box.

Check out the sample Markdown report and FAQ for more information.

Installation

Install HoundDog.ai on a developer machine using the commands below. To run it across your organization inside your own network, see the self-hosted installation guide.

Linux and macOS

curl -fsSL https://raw.githubusercontent.com/hounddogai/hounddog/main/install.sh | sh

To install a specific version:

curl -fsSL https://raw.githubusercontent.com/hounddogai/hounddog/main/install.sh | sh -s -- --version 1.2.3

Windows

irm https://raw.githubusercontent.com/hounddogai/hounddog/main/install.ps1 | iex

To install a specific version:

$env:HOUNDDOG_VERSION = '1.2.3'; irm https://raw.githubusercontent.com/hounddogai/hounddog/main/install.ps1 | iex

Alternatively, you can download the binary directly from the releases page.

Self-Hosted (Organization-Wide)

To deploy HoundDog.ai across your organization inside your own network, with SCM integration, automated scans, and a centralized dataflow and API context catalog, follow the self-hosted installation guide.

Uninstallation

# Linux and macOS
rm -rf ~/.hounddog

# Windows
Remove-Item -Recurse -Force "$env:LocalAppData\hounddog"

Usage

hounddog scan [OPTIONS] [PATH]

A single scan produces both the privacy dataflow map and the API/service context for the target codebase.

Privacy Code Scanner

For a quick demonstration, scan our Python test repository:

# Clone the test repository
git clone https://github.com/hounddogai/hounddog-test-python-app

# Scan the test repository
hounddog scan hounddog-test-python-app

By default, only risky dataflows are shown to minimize noise. Use --severity=all to see everything:

hounddog scan hounddog-test-python-app --severity=all

Use --trace to see detailed dataflow traces (one of our coolest features and useful for debugging):

hounddog scan hounddog-test-python-app --trace

Use --output-format=markdown to generate a Markdown report:

hounddog scan hounddog-test-python-app --output-format=markdown --output-path=report.md

We recommend the Markdown Viewer Chrome extension for viewing it (see setup and sample report).

Dataflow Context Engine

To see the API and service context across a polyglot codebase, scan our monorepo test repository, which spans gRPC and Thrift services in Python, TypeScript, C#, Java, Go, and Rust:

# Clone the test repository
git clone https://github.com/hounddogai/hounddog-test-monorepo

# Scan the test repository
hounddog scan hounddog-test-monorepo

Alongside the dataflow map, the scan prints a service catalog for each protocol. It resolves every service, its method definitions, and the servers and clients that implement and consume it, down to the file, line, branch, and commit. Across a whole organization, the self-hosted deployment unifies these per-repo catalogs into a single cross-repo catalog and refreshes it in CI on every pull request. A HoundDog.ai MCP server that will expose this context directly to AI coding agents is coming soon.

More Options

To see the up-to-date list of supported data elements in HTML format:

hounddog data-elements

To see the up-to-date list of supported data sinks in HTML format:

hounddog data-sinks

Use --help to see all subcommands and options:

hounddog [SUBCOMMAND] --help

Features

HoundDog.ai has two capability pillars. Both run from the same hounddog scan and are available in a free tier on a developer machine or across your organization via self-hosted installation.

Privacy Code Scanner