Privacy Code
Scanning
A privacy code scanner that helps engineering teams identify and stop privacy risks while code is being written, not after an app is already live and data is already flowing.
If your app uses AI, APIs, or third-party integrations, traditional privacy tools are already too late.
Problem: Privacy Risks Start in Code - Not After Deployment
Traditional privacy tools detect problems too late, when data is already in motion,
pushing teams into remediation rather than prevention.
Sensitive Data in Logs & Local Storage
- Sensitive data appearing in logs or local storage forces organizations into reactive cleanup.
- DLP tools surface problems only after exposure, sending teams into weeks of tracing data paths, cleaning up logs, and rewriting code.
- Incidents often start with simple oversights like printing full user objects or passing tainted variables into logging functions.
- As applications scale and code paths multiply, these mistakes become harder to catch and more frequent.
Shadow AI & Third-Party Integrations
- Data shared with third party or AI integrations must align with Data Processing Agreements and your privacy notice.
- Silent code changes can redirect sensitive fields to analytics platforms, observability pipelines, or LLM prompts.
- These hidden shifts erode user trust and increase regulatory exposure long before privacy teams are aware.
Hidden Cross-Service Flows
- Sensitive data flows between microservices and APIs in ways teams cannot easily track or document.
- Cross repo dependencies over REST, GraphQL, or gRPC and complex code transformations defeat traditional scanning approaches.
- As a result, sensitive data exposed through these API protocols often goes undocumented or poorly understood, creating hidden privacy and compliance risk.
Sensitive Data in AI Prompts
- AI usage is accelerating, increasing the risk of unintentionally sharing sensitive data with external models.
- Many companies restrict AI services, yet scans routinely uncover AI SDKs like LangChain or LlamaIndex.
- Current privacy tooling is either too reactive, discovering these flows after the fact, or completely blind to them.
- Privacy teams scramble to understand what data is sent to AI systems and whether user notices and legal bases cover those flows.
Why Existing Tools Fail
Regulations like GDPR and US privacy frameworks require accurate data maps and reports such as RoPA, PIA, and DPIA. In fast moving engineering environments, those maps quickly fall out of date.
Most data privacy solutions fall into two buckets.
Governance, Risk, and Compliance Platforms
Production focused Privacy Platforms
How Privacy Code Scanning Works
Detect and stop sensitive data exposure directly in your development workflow.
Scan Code as It’s Written
HoundDog.ai integrates directly into your development workflow to scan code in IDEs (VS Code, IntelliJ, Cursor) and in CI pipelines as it is written or generated.
Trace Sensitive Data Flows
The scanner maps how sensitive data moves through functions, APIs, third-party services, and AI integrations, revealing hidden exposure paths.
Enforce Privacy Rules Before Deployment
Apply allowlists to define which data types are permitted in LLM prompts and other risky sinks, and automatically block unsafe pull requests to maintain compliance.
Build Customer Trust Through Transparent Data Handling
- Generate evidence based data maps that show where sensitive data is collected, processed, and shared, including through AI and third party integrations.
- Auto generate audit ready Records of Processing Activities (RoPA), Privacy Impact Assessment (PIA), and Data Protection Impact Assessment (DPIA) pre-populated with detected data flows and privacy risks aligned with GDPR, CCPA, HIPAA, and other regulatory frameworks.
- Give privacy teams continuous visibility into processing activities without surveys or manual discovery.
- No production monitoring required. No retroactive cleanup. No guessing.
What Makes HoundDog.ai Different
Purpose-built for modern engineering teams building AI-powered and privacy-critical applications.
Code-Level Data Flow Intelligence
Analyze real data paths across functions, services, and repositories instead of relying on keyword scanning or runtime guesses.
Built for AI & LLM Workloads
Detect and control what sensitive data is sent to prompts, embeddings, and external AI APIs before it ever leaves your environment.
Prevent Risk Before Deployment
Catch privacy issues during development and code review, not after data has already been logged, shared, or leaked.
Compliance from Real Data Flows
Generate RoPA, PIA, and DPIA directly from detected code-level data movement, always up to date with system changes.
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.
Built for Enterprise-Grade Security
Designed to meet the requirements of large, security-conscious organizations.
Built for Enterprise Teams
- Trusted by Replit, running 8,000+ privacy scans per day to help 45M creators bake privacy into the earliest stages of prototyping and app creation
- Used by Fortune 1000 companies across technology, healthcare, and finance
- SOC 2 compliant, with a transparent Trust Center offering access to the latest SBOM and penetration testing reports
- Hands on, highly responsive customer support
Secure by Default
- No production data or runtime ingestion required
- Runs locally in your environment or CI pipelines
- Secure broker for self hosted source control systems that meets strict network and data handling standards
- Transparent Trust Center with up to date SBOM and penetration testing reports
Frequently Asked Questions
HoundDog.ai scans application code to identify sensitive data flows across functions, APIs, third-party services, and AI integrations.
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.