Customers use the Dataflow Context Engine to ship cross-service changes faster with deterministic API context, and the Privacy Code Scanner to detect PII leaks, automate GDPR data mapping, and strengthen compliance maturity across GDPR, SCF, HIPAA, PCI, and FedRAMP.
“For companies handling sensitive data, HoundDog.ai is a real must-have. The scanner is blazingly fast and integrates seamlessly with our GitLab workflow. More importantly, it provides the peace of mind we need by ensuring that sensitive data does not accidentally leak into logs, files, or third-party systems, even with high-frequency updates to the codebases.”
Auth tokens and passwords in logs or local storage, caught at scan time.
PII and PHI to integrations that don't match published privacy notices.
AI-generated apps embed GDPR and CCPA best practices from day one.
less manual data mapping. Automated reporting across 15,000 repos and stronger HIPAA compliance.
saved. PII leak incidents cut from five a month to zero across 500 repos.
of AI integrations were Shadow AI, some without a DPA. Now flagged as suggested RoPA edits.
The HoundDog.ai privacy code scanner surfaced excessive sensitive data leaks in their application logs and identified third-party subprocessors that were missing from their published Trust Center. Sensitive data exposures are rarely intentional. They accumulate over years of application development. As codebases grow, so does the complexity of dependencies and data transformations. A developer logs a full user object, a tainted variable carries PII through a chain of transformations, and by the time anyone notices, the data has already been written to logs or sent to a third party. Below is a representative cross section of what we found:
OAuth refresh tokens and third-party API keys written to logs in plaintext. Live credentials replayable by anyone with log access.
Passport numbers and visa details captured alongside traveler identity. Special-category identity data exposed to log readers.
Real emails sent to an analytics platform as user IDs. Passenger names, phone-email-UUID combinations, and SMS bodies captured in logs across booking flows. More than 400 distinct email-in-log call sites.
Of the more than 20 subprocessors with confirmed data flows detected in the source code, only two appeared on the customer's published Trust Center list. AI and LLM providers accounted for most of the gap, highlighting how privacy documentation often lags behind active development. Some AI subprocessors, including orchestration tools, did not have an established Data Processing Agreement.
Appears 1 to 2 times a month, causing interrupt work for SREs. Days, sometimes weeks, are spent scrubbing logs, assessing exposure across tools that ingested them, and patching code after the fact.
Datadog's per-GB scanned pricing is prohibitive, nearing $1M per year for full coverage, and that's just for logs within Datadog.
Eliminated the need for Datadog's $700K log masking, reduced reliance on AWS Macie for PII detection, and removed the disruptive effort of remediating log leaks, saving an average of 80 hours per incident.
Identified 500+ sensitive leaks, established a baseline across all 500 code repositories scanned, and prioritized the remediation backlog.
GitHub App runs on every merge, proactively detecting PII leaks in new code and blocking risky PRs.
With thousands of code repositories, relying solely on DLP for PII leaks is costly and lacks third-party coverage.
Privacy-related controls including data minimization, RoPA, and third-party data sharing are only partially met or addressed reactively.
Automated data flow documentation and privacy reporting across thousands of code repositories supplemented existing privacy tools and eliminated the lengthy cycles required for manual corrections caused by missed flows from shadow third-party integrations or frequent code changes.
Reduced monthly sensitive data leak incidents from 10 to zero across thousands of code repositories by detecting and preventing leaks at the source, before they reached production logs.
Strengthened HIPAA and privacy program maturity by enabling earlier visibility into data flows, automating data mapping tasks, and reducing exposure in third-party integrations.
Based on thousands of leaks detected by the HoundDog.ai scanner, logs rank first at 65% of findings, followed by third-party integrations at 30%. Other risky mediums like local storage, files, and cookies make up the remaining 5%.
Logs are among the riskiest mediums because they are ingested by multiple tools and can make it easier for attackers already inside your network to exfiltrate data. Relying solely on DLP for PII detection in logs is reactive and unreliable due to sampling limitations. Issues are often identified too late, after logs have been ingested by multiple tools, without context on the root cause.
Among leaks found in third-party integrations, monitoring platforms like Datadog account for the largest share of PII exposure at 45%, well beyond what is permitted under accepted Data Processing Agreements.
Sales and marketing platforms, web analytics tools, and LLM integrations all contribute to DPA violations, with AI models representing the fastest-growing source of PII risk.
Detect PII leaks, automate GDPR data mapping, and give engineers deterministic cross-repo API context, all before code reaches production.