| Optional AI analysis on top of static findings |
✓ Optional AI layer that auto-closes false positives, adjusts severities, and adds deep contextual reasoning. Scanning runs in the customer's environment on cheap CPU; AI only interprets already-detected data flow traces, keeping cost low and predictable. |
! AI is central to Privado's current platform, but as autonomous AI agents that perform privacy assessments, not as a layer that augments static code findings. |
| Scanning speed |
✓ Rust-based scanner optimized for very large repositories and monorepos. Most scans complete in seconds. |
! Performance benchmarks for large codebases are not publicly documented. |
| Handles very large codebases |
✓ Designed for multi-million-line repositories and complex microservice architectures. Production scans cover 40M+ lines of code in seconds. |
! Scalability for very large repositories is not well documented. |
| Completeness of data flows |
✓ Deep taint analysis with interprocedural tracing captures end-to-end data flows across functions, services, APIs, third-party SDKs, and AI integrations. |
! Limited deep tracing across functions and repositories can leave gaps in generated data maps and privacy reports. |
| Detection accuracy |
✓ Precise detection with significantly fewer false positives, grounded in how data actually propagates through code. |
! Broad pattern matching can generate higher false positive rates. |
| Automated CI configuration |
✓ Direct GitHub, GitLab, and Bitbucket integrations automatically deploy CI scanning across thousands of repositories. |
! CI integrations available, but repositories typically need to be enabled individually. |
| Battle tested at scale |
✓ Runs 100,000+ scans per day inside Replit, supporting 50 million creators in production. |
! Large-scale production benchmarks are not widely documented. |
| Sensitive data and data sink coverage |
✓ Extensive coverage of sensitive data elements and data sinks, with continuously expanding detection libraries. |
! Extensive rule sets rely on permissive pattern matching, which can produce more false positives and less precise findings. |
| GDPR data mapping |
✓ Generates GDPR data maps directly from deep source code analysis, with complete data flows across APIs, services, third-party SDKs, and AI integrations. |
! Automated mapping available, but limited deep data flow tracing can produce incomplete maps that require manual backfilling. |
| Privacy documentation (RoPA, PIA, DPIA) |
✓ Surfaces detected data flows and subprocessors as suggested edits to your RoPA. PIA and DPIA reports auto-generated and prefilled with detected flows and privacy risks. |
! Documentation can be generated, but incomplete data flow detection often requires significant manual completion and validation. |
