Modern apps integrate with dozens of third-party SDKs, APIs, and AI services, often added by developers or AI coding assistants without security review. Posture and runtime tools only see leaks after the data has already moved. HoundDog.ai monitors every code change to surface which sensitive data elements reach which third parties, including shadow integrations and LLM prompts, before anything ships to production.
HoundDog.ai works the way developers do: in the codebase, in the IDE, and in the pull request. It traces your applications' data flows as defined in the application code logic to track more than 100 sensitive data types (including PII, PHI, CHD and auth tokens) through intermediate transformations across files, functions, and procedures regardless of nesting depth, and flagging them when they reach a third-party sink, whether that is an analytics SDK, a CRM API, or an LLM prompt.
Uncover all third-party SDKs, APIs, and shadow integrations introduced by engineering teams, often without the knowledge or approval of security and privacy teams, directly in the codebase before they ship.
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.
Apply precise allowlists per third-party SDK or API to define what each integration is permitted to receive, and automatically block pull requests that introduce unsafe data flows. Default allowlists ship out of the box for common processors, so the baseline is in place from day one and security teams only customize where the threat model diverges. For contractual gating tied to a Data Processing Agreement specifically, see DPA enforcement.
As developers build integrations with analytics, observability, CRM, or AI tools, it is common to pass contextual data for better insights. Without clear guardrails, developers or AI coding assistants accidentally transmit full user objects, exposing PII such as names, emails, phone numbers, and even Social Security Numbers. This often happens when objects are spread into function parameters or logged without filtering.
All four examples below use this shared User object. Watch how one careless invocation leaks every field straight into a third-party sink.
interface User { id: string; email: string; ssn: string; firstName: string; lastName: string; phoneNumber: string; role: string; companyName: string; industry: string; }
function handleLogin(user: User) { // BAD: full user object lands in logs datadogLogger.info("User logged in", { user }); // GOOD: only operational metadata const { deviceType, ipAddress, hostname } = getSystemInfo(); datadogLogger.info("User logged in", { deviceType, ipAddress, hostname }); }
Observability sinks like Datadog are designed for hostname, ipAddress, and deviceType. Logging the full user object exposes sensitive fields into log indexes that are hard to scrub post-ingestion and often replicated to backups and downstream tooling.
function trackUserSignup(user: User) { // BAD: object spread leaks every field gtag("event", "user_signup", { ...user }); // GOOD: only permitted fields const { deviceType, browserUsed, ipAddress } = getDeviceInfo(); gtag("event", "user_signup", { deviceType, browserUsed, ipAddress }); }
Analytics sinks routinely retain every field they receive. Spreading the user object sends names, emails, and SSNs into a system designed for behavioral attributes only, expanding the breach blast radius if the analytics account is compromised.
function syncUserToSalesforce(user: User) { // BAD: spreads the SSN and internal IDs into Salesforce sendToSalesforce("lead_create", { ...user }); // GOOD: explicit, expected CRM fields only sendToSalesforce("lead_create", { firstName: user.firstName, lastName: user.lastName, email: user.email, phoneNumber: user.phoneNumber, companyName: user.companyName, role: user.role, industry: user.industry }); }
CRM systems hold a wide audience inside the company. Sending fields like SSNs and internal user IDs creates an insider-access risk and broadens the data exposed by phishing or account takeover incidents targeting CRM users.
let promptContext = { audience: "Customer", notes: "Welcome to our platform.", }; // BAD: the variable becomes tainted with PII promptContext.audience = `${user.firstName} ${user.lastName}`; promptContext.notes = `Welcome ${user.email} to the ${user.industry} platform.`; // GOOD: use only non-identifying metadata in the prompt const prompt = `Generate a welcome message for a ${user.role} in the ${user.industry} sector.`;
Variables that begin clean become tainted with PII through reassignment, especially when dynamic prompts are constructed by AI assistants. Once the variable reaches llm.invoke, that PII is shipped to OpenAI and stored according to the provider's policy, not yours.
| Scenario | Platform | Exposed (sensitive fields sent) | Safe (expected fields) |
|---|---|---|---|
| Full user object to Datadog | Datadog | email, ssn, firstName, lastName, phoneNumber, role, companyName, industry | hostname, ipAddress, deviceType |
| Tainted variables in OpenAI prompt | OpenAI | email, firstName, lastName | role, industry, companySize, age, gender |
| Full user object to Google Analytics | Google Analytics | email, ssn, firstName, lastName, phoneNumber, role, companyName, industry | ipAddress, deviceType, browserUsed |
| Full user object to Salesforce | Salesforce | ssn | firstName, lastName, email, phoneNumber, role, companyName, industry |
Network, gateway, and storage-layer tools have a role, but they are fundamentally reactive. They sanitize or alert after data has already been collected. Monitoring at the code layer is the only way to catch unsafe flows before deployment, when remediation is cheap and the engineer who introduced the change still has the context to fix it.
| Method | Layer | Pros | Cons |
|---|---|---|---|
| Static code analysis | Code | Continuous, pre-deployment detection, scales across repos, works on developer and AI-generated code, integrates with PR workflow | May miss data generated only at runtime |
| Manual code reviews | Code | Human judgment, can catch complex context-based issues | Time-consuming, not scalable, prone to human error |
| API gateway monitoring | API | Centralized control over API traffic, can log, redact, or block | Requires all traffic to pass through the gateway, misses traffic that bypasses it such as SDKs and internal services |
| Network proxy | Network | No need to modify application code | Hard to scale across microservices, lacks understanding of data context or meaning |
| Data Loss Prevention (DLP) | Network / Storage | Detects sensitive data in transit or at rest, integrates with the broader security stack | Reactive rather than preventative, lacks visibility into app-layer data flows and third-party SDKs |
Hardcoded regex rules for DIY detection are brittle, hard to maintain, and almost never scale. They lack context around data sensitivity, awareness of sanitization or transformations, and visibility into where data actually ends up. As codebases evolve, DIY coverage falls behind. HoundDog.ai's static analysis is purpose-built to do this at scale. For contractual gating against a Data Processing Agreement specifically, see DPA enforcement.
Posture and enforcement tools only act once sensitive data is already there. Catching the exposure at the source is the missing layer, and a prerequisite for the rest of the security stack to operate on clean data.
A short walkthrough of how HoundDog.ai discovers third-party integrations, traces sensitive data flows to each one, and surfaces unsafe flows in the pull request before they ship.
Watch nowTry the free Privacy Code Scanner and see exactly which sensitive data elements reach each SDK, API, and AI integration in your codebase.