Data Flow Mapping
The code logic in your custom applications defines how sensitive data flows across storage, APIs, and third party and AI integrations. HoundDog.ai maps those flows directly from source code, before production.
What Is Data Flow Mapping?
Data flow mapping traces how sensitive data moves through an application: what is collected, where it is stored, how it travels between functions, services, third party integrations, and AI tools, and whether those flows comply with policy.
HoundDog.ai builds the map statically from source code, so it reflects what your applications actually do, not what surveys and diagrams say they do.
What sensitive data is collected
Where that data is stored
How it moves between services, third parties, and AI tools
Whether each flow meets policy and regulatory requirements
Coverage spans every relevant data type:
Data Flows You Can't See in Production Tools
Privacy teams rely on three workflows today, and none of them keeps up with modern development.
Manual Documentation Does Not Scale
- Engineering gets flooded with privacy questionnaires every release
- Responses come back incomplete, outdated, or guessed
- The cycle repeats with every code change, so records lag behind by design
GRC Platforms
- Provide blank RoPA, PIA, and DPIA templates, like this one from Vanta, and rely on privacy teams to manually interview engineers and collect data flows
- The process must be repeated every time code changes, making it slow and unreliable at scale
Privacy Platforms Are Blind to the Codebase
- Privacy platforms infer flows after deployment, missing shadow AI and SDKs added in code
- They rely on predefined knowledge of third party services, leaving them blind to new integrations introduced directly in code
- They never see what developers actually shipped until personal data is already flowing
Stale Evidence
Documentation runs weeks or months behind the code.
Drift
Documented activities diverge from implementation every release.
Exposure
Subprocessors slip into production undocumented, an Article 30 risk.
Why Data Flow Mapping Is Critical for Modern Teams
Identify Where Sensitive Data Actually Lives
In complex applications, sensitive data rarely stays where teams expect it to. HoundDog.ai maps data across:
This visibility reveals exposure points most tools never see, including legacy paths, forgotten integrations, and indirect flows created by shared libraries or helper functions. Teams often discover sensitive data traveling far beyond its intended scope.
Prevent AI Data Leaks Before They Happen
As AI usage expands, so does the risk of unintentionally sharing sensitive data with external models. Prompts often combine user input, internal metadata, and system context in ways that are difficult to reason about manually. HoundDog.ai detects when sensitive data is included in prompts sent to:
More importantly, it blocks unapproved flows at the source, before data ever reaches an AI model. This prevents irreversible exposure while still allowing teams to innovate safely with AI.
Replace Guesswork with Code-Level Evidence
Traditional privacy reviews often rely on interviews, architecture diagrams, and self-reported documentation. These methods break down as systems evolve. HoundDog.ai analyzes actual code paths to understand how data moves through:
Because the platform understands root causes, not just outcomes, it enables teams to fix issues permanently rather than respond to recurring alerts. Engineers know precisely where to intervene, and compliance teams gain evidence they can trust.
Stay Audit-Ready by Default
Mapped data flows become code-level evidence for your compliance documentation, including:
The evidence updates continuously as systems change, eliminating the scramble to recreate reality during audits. Documentation reflects how the system actually works today, not how it worked months ago.
The result: faster releases, fewer audit scrambles, and no surprise subprocessors.
How HoundDog.ai Data Flow Mapping Works
HoundDog.ai operates inside the development pipeline. Scans run locally. Your code never leaves your machine.
Scan Code as It Is Written
Integrates with IDE plugins for VS Code, IntelliJ, and Cursor, and with CI pipelines. Analyzes source code to map sensitive data flows across logs, storage, APIs, third-party and AI integrations, including hidden or "Shadow" integrations.
The taint-flow static analysis detects sensitive data elements by variable, method, function, and field name, tracing them through intermediate transformations across files, functions, and procedures regardless of nesting depth, and flagging them when they reach a sink, whether it is a controlled sink like a database or a high-risk one like an LLM prompt.
Trace Sensitive Data Flows
Automated data flow mapping shows exactly which sensitive data elements reach each data sink per repository, from logs and AI services like OpenAI to third parties like Slack, Stripe, and Twilio, with every flow rated safe or risky.
- More than 100 sensitive data types supported, spanning traditional PII per GDPR's definition, PHI per HIPAA's definition, CHD per PCI's definition, and auth tokens and secrets, which can pose a serious data breach risk when exposed in logs.
- More than 1,000 integrations supported, including direct and indirect AI SDKs, many of which are embedded in code without an established Data Processing Agreement, and third-party integrations spanning monitoring, SIEM, sales and marketing, payment, and many other categories.
Surface Suggested Edits
New data flows and subprocessors become suggested edits in your Org RoPA, each traceable to the code that generated it.
For processing activities outside the scope of scanned applications, such as Support or Sales, a collaborative workflow lets you invite stakeholders to review and make suggestions, while the privacy team keeps track of all processing activities in one place with full historical tracking.
Enforce Before Deployment
Bake your privacy policies into the pipeline by customizing the types of data allowed per data sink and blocking unsafe data flows when they are introduced in pull requests as part of your CI pipeline. Default allowlists are available out of the box, incorporating the standard data types expected in Data Processing Agreements per data sink, e.g. Stripe's allowlist includes bank card details whereas Slack's does not.
Build Customer Trust with Transparent Data Handling and GDPR Data Mapping
- Automatically generate GDPR data mapping and data flow maps directly from source code to show where sensitive data is collected, processed, and shared across functions, APIs, third party services, and AI integrations.
- Keep your Org RoPA continuously updated with new data flows and subprocessors surfaced as suggested edits at the speed of development, giving privacy teams a centrally managed record across all processing activities, not just custom apps.
- Validate privacy reviews with code-level evidence before code ships, ensuring what was approved at the design stage is consistent with what was actually implemented. This ensures Privacy Impact Assessments (PIA) and Data Protection Impact Assessments (DPIA) are pre-populated with detected sensitive data flows and privacy risks, aligned with GDPR, CCPA, HIPAA, and other regulatory frameworks.
- Detect sensitive data flows with a shift-left approach that gives privacy and security teams prevention, stopping privacy risks before the data ever starts flowing.
What Makes HoundDog.ai Different
Purpose built for engineering teams that need to detect sensitive data flows and automate GDPR data mapping directly from source code.
Code-Level Data Flow Intelligence
Detect and map sensitive data flows directly from source code across APIs, services, and third party integrations without relying on surveys, spreadsheets, or privacy tools that miss hidden integrations and SDKs.
Built for AI & LLM Workloads
Discover AI SDKs embedded in code and detect sensitive data flows to LLM prompts and external AI APIs before your apps go live.
Prevent Risk Before Deployment
Catch privacy issues during development and code review, not after data has already been logged, shared, or leaked.
Data flow mapping is one capability of the Privacy Code Scanner. The same code-level maps power automated GDPR data mapping, RoPA, and privacy assessments for compliance teams.
Data Flow Mapping Frequently Asked Questions
What is data flow mapping?
Tracing how sensitive data moves through an application: what is collected, where it is stored, how it travels between services, third parties, and AI tools, and whether those flows comply with policy. HoundDog.ai maps flows statically from source code, before production.
What does data flow mapping software do?
It discovers and visualizes sensitive data flows automatically. HoundDog.ai scans code in IDEs and CI, detects more than 100 sensitive data types, traces each to sinks like logs, databases, SDKs, and AI APIs, and rates every flow by severity.
How is data flow mapping different from GDPR data mapping?
Data flow mapping is the technical capability of tracing flows through code. GDPR data mapping is its compliance application: Article 30 records, assessments, and audit evidence. HoundDog.ai connects the two with suggested Org RoPA edits that the privacy team reviews and approves.
Does HoundDog.ai send my source code anywhere?
No. Scans run locally in the IDE or CI pipeline. Only scan findings are used to build the data map.
Make Privacy-by-Design a Reality in Your SDLC
Detect PII leaks, map sensitive data flows, and automate GDPR data mapping at the speed of development.