AI Governance and Shadow AI Discovery for Developers
Discover shadow AI, detect sensitive data flowing from apps to LLMs, and enforce AI governance at the code level
Build and scale AI apps without compromising user trust. Discover AI integrations, detect sensitive data flowing from application code into AI prompts, and enforce privacy by design before anything reaches production.
Discover shadow AI and LLM integrations in code, both direct (like OpenAI and Anthropic) and indirect (like LangChain)
Detect sensitive data flowing from application code into AI prompts during development, covering over 100 data types including PII, PHI, CHD, and authentication tokens
Enforce AI governance at the code level with granular allowlists that block unapproved data types in PRs and CI workflows
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.
import langchain
import crewai
import llama_index
import pydantic_ai
Track sensitive data flowing from application code into AI prompts
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 AI governance rules and stop risky code before it reaches production.
Built-in AI governance controls for applications that build prompts from customer, business, or user data
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 for Data Flows from Your Apps to AI
Disadvantages of Current Approaches:
Runtime filtering tools inspect prompts only after applications are live and communicating with LLMs, which is too late to prevent the exposure.
They rely on pattern matching and often miss organization-specific data.
Without code-level visibility, these tools can’t trace how sensitive data entered a prompt from application code, making prevention difficult.Advantages of HoundDog.ai’s Approach:
HoundDog.ai detects sensitive data flowing from custom applications into dynamically generated AI prompts during development, before it reaches LLMs or other AI services.
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.
AI-Specific Privacy Assessments and Data Flow Mapping
Disadvantages of Current Approaches:
Privacy assessments are typically manual, relying on surveys or observations after deployment that fail to capture what actually happens in the code.
Most tools lack visibility into how sensitive data flows to third party SDKs or AI integrations, relying on manual surveys or production analysis that misses shadow AI and new integrations in code.
RoPAs, PIAs 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 which AI models receive it and through which SDKs or frameworks.It provides audit-ready evidence for RoPAs, PIAs, and DPIAs, including AI-specific assessments and EU AI Act documentation, backed by code-level data flow traces.
Privacy teams get continuous, real-time visibility into processing activities, without relying on self-reported surveys or manual discovery.
Enabling AI Governance Across All Stages of Development
IDE Plugins
Detect sensitive data leaks directly in your IDE as you write code.
Catch privacy risks early before they reach production.
Managed Scans
Offload scanning to HoundDog.ai with direct source control integrations.
Automatically analyze repositories for privacy risks.
CI/CD Integrations
Use HoundDog.ai source control integrations to auto configure CI.
Block risky pull requests before they are merged.
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
- [Coming Soon] 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
ROI for Automated Privacy Compliance
Backed by Incredible Investors
Sensitive Data Protection at the Speed of Development
Juvare
Why Shift-Left Privacy Matters
Stop privacy risks at the source — while code is being written, not after it reaches production.
AI Exposure Happens Fast
Sensitive data can be exposed to AI tools
within minutes of code changes.
Post-Production Tools Are Too Late
Fixing leaks after release
doesn’t prevent real damage.
Compliance Requires Prevention
Modern privacy programs must prevent risks,
not just report them after exposure.
Trusted by Replit to detect privacy leaks across AI generated applications built by more than 45 million creators.
Make Privacy-by-Design a Reality in Your SDLC
Shift left on privacy with code scanning. Detect PII leaks, map sensitive data flows, and generate GDPR data maps, RoPA, PIA, and DPIA before code reaches production.