Data Subject Requests (DSR/DSAR)

HoundDog.ai helps privacy and IT teams fulfill Data Subject Access Requests (DSARs) by providing code-level visibility into where personal data is collected, processed, stored, and shared across your applications.

The Challenge

When a data subject submits a request to access, update, or delete their personal data, privacy teams need to answer a fundamental question: where does this person's data actually live?

In most organizations, answering this question means manually tracing data across services, databases, logs, third-party integrations, and AI systems. Without a complete picture, teams risk incomplete fulfillment, which can lead to regulatory violations and eroded customer trust.

The challenge is compounded by shadow integrations. A developer may have added a new analytics SDK or AI integration that quietly receives user data, and unless that integration was documented, it won't appear in any DSAR response.

How HoundDog.ai Supports DSAR Fulfillment

HoundDog.ai's privacy code scanner maps every sensitive data flow from your application's source code, giving privacy teams a complete, continuously updated view of where personal data goes. Using the Sensitive Datamap, privacy teams can query by data element to answer questions like "Show me every code repository and data sink where email address is processed." This includes:

• Data elements collected: What types of personal data (e.g., email, name, IP address, health records) are handled by each application, classified by sensitivity and tagged as PII, PIFI, or PHI
• Processing activities: How data is transformed, passed between functions, and used across services, with full data flow traces showing every transformation step
• Data sinks: Every destination where personal data is sent, including logs, files, databases, APIs, third-party SDKs, and AI integrations
• Shadow integrations: Third-party and AI subprocessors introduced in code that may not have been documented or covered by a Data Processing Agreement

DSAR Workflow with Jira and Linear

HoundDog.ai integrates with Jira and Linear to operationalize DSAR fulfillment. From the Dataflows page, any detected data flow can be filed as a Jira or Linear ticket directly from the platform. When a data subject request is received, the privacy or IT team can:

1. Query the Sensitive Datamap by data element to identify every repository and data sink where the data subject's personal data is collected, processed, or stored
2. Review detailed data flow traces for each occurrence, showing the exact code path, file, function, and transformation chain that leads to the data being exposed in each sink
3. File tickets in Jira or Linear for each system or integration that needs to be updated or purged, with full context including severity, compliance framework mapping (GDPR, CCPA, HIPAA, NIST 800-53), code segment, and remediation guidelines
4. Track resolution through the issue tracking integration, ensuring every data sink storing the data subject's information is addressed

The privacy or IT team assigned to the ticket has immediate visibility into the full blast radius of the request. They know exactly which systems, services, and integrations need to be updated or purged, without having to manually trace data paths across the codebase.

Supported Request Types

• Access requests: Use the Sensitive Datamap to identify all locations where a data subject's personal data is collected, processed, and stored, filtered by data element
• Rectification requests: Pinpoint every system and integration where data needs to be updated using data flow traces that show the exact code paths and destinations
• Erasure requests (Right to be Forgotten): Map the complete set of data sinks that must be purged, including third-party and AI integrations that may have received the data
• Portability requests: Identify all data elements collected about a data subject and the formats in which they are processed

Why Code-Level Visibility Matters for DSARs

Traditional DSAR tools operate at the infrastructure level, scanning databases and cloud storage for personal data. This approach misses data flows embedded in application code, such as:

• Sensitive data logged by application code and ingested by log management platforms
• Personal data sent to AI models via LLM prompts or orchestration frameworks like LangChain
• Data shared with third-party analytics, observability, or marketing SDKs added directly in code
• Cross-service data flows over REST, GraphQL, or gRPC APIs

HoundDog.ai fills this gap by scanning source code to surface these hidden flows, ensuring DSAR responses account for every destination where personal data may reside.

Getting Started

DSAR support is available on the Enterprise plan. To enable the Jira or Linear integration for your organization, see Issue Tracking Tools or contact your HoundDog.ai account team.