Privado AI Pricing Overview

The combination of continuous scanning across our live website, mobile app, and related backend/code surfaces with AI agents that actually populate assessments and data maps from real documentation and evidence. For a company managing high volumes of customer interactions online and through the app (rentals, account management, digital access, payments), having always-on visibility into personal data elements, third-party destinations, and consent behavior is a practical advantage. The agents surface findings with cited reasoning rather than just flags, which makes it easier for privacy, legal, and product/engineering teams to review and act. Setup for web and app scanning required minimal developer effort on our side, and the platform’s focus on not training on customer data or code aligns with our internal security expectations. Review collected by and hosted on G2.com.

The volume of findings can require thoughtful triage when first rolled out across a large website and app footprint; prioritization of urgent versus lower-severity items is not always immediately obvious and benefits from internal calibration. Reporting and customization options are functional but could be more flexible for highly specific internal stakeholder views or bulk export needs in a large enterprise environment. Onboarding support has been responsive, yet deeper self-serve documentation for advanced configuration would help reduce reliance on the vendor for edge cases. These are manageable and have not offset the core value for our use case. Review collected by and hosted on G2.com.

Our insurance claims payloads mix sensitive policyholder PII, such as home addresses, policy numbers, and bank routing details for Actual Cash Value payouts, right alongside commercial property fields like item descriptions, pre-tax replacement prices, and depreciation rates. Putting Privado AI's static code scanner into our GitHub Actions pipeline gave us a reliable way to trace how personal data elements move through our Python and FastAPI microservices before code ever merges. The strongest part of their architecture is how the privado scan CLI uses AST data-flow analysis to map data sources to four distinct sink categories: console logs, databases, internal APIs, and third-party endpoints. Because the scanner runs locally inside our CI runner and only pushes the generated .privado/privado.json metadata graph to the Privado dashboard, our raw proprietary source code never leaves our infrastructure. Whenever a developer modifies a Pydantic model and accidentally passes a claimant identifier into an unmasked logger.info() call or an external LLM pricing payload, Privado flags the exact file and line number right on the pull request. Review collected by and hosted on G2.com.

Out of the box, Privado's default discovery rules easily caught standard identifiers like email addresses, phone numbers, and SSNs, but missed several insurance-specific attributes in our Pydantic schemas such as loss_location_address and payee_routing_number. At the same time, generic variable names on our commercial inventory sheets, like vendor_name or manufacturer_contact for retail suppliers, initially triggered false-positive personal data alerts. I had to spend time writing custom YAML classification rules under rules/sources so the scanner could distinguish commercial retailer metadata from actual policyholder PII. Additionally, because static AST scanning evaluates one repository at a time, it maps data lineage cleanly inside our main FastAPI api-server, but when a claim dictionary is written to our cache layer and picked up asynchronously by separate background worker repositories for categorization or depreciation, the scanner cannot stitch that cross-repo hop automatically without manual linking in the UI. Full repository scans also added a few minutes to our pull request checks until we configured incremental scanning. Review collected by and hosted on G2.com.