The most proactive privacy scanner for AI applications
- most proactive
- deepest
- fastest
- most lightweight
- most accurate
Trusted By
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.
from langchain.llms
from crewai
from llama_index
from pydantic
Track sensitive data across code - no matter how deep it’s buried
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
Enforce privacy rules and stop risky code before it reaches production.
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.
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 difficultAdvantages 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
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.
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
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
ROI for Automated Privacy Compliance
Backed by Incredible Investors
Sensitive Data Protection at the Speed of Development
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.