Your databases hold the crown jewels. PrivacyPal scans them with AI that understands context—not just pattern matching. Find sensitive data across tables, documents, and object stores. Prioritize what matters. Fix it before it becomes a problem.
No agents. No re-architecture. PrivacyPal connects to your data stores and starts scanning. Your data stays where it lives—we read it in place, the same way PrivacyPal protects data at the edge.
Whether you're on cloud, on-prem, or hybrid, the same agentless approach scales with you. Add a new database tomorrow; it's in the scan queue. No infrastructure sprawl, no performance hit.
Regex sees "555-1234" and flags it. It misses "call John's cell" or a SSN buried in a notes field. PrivacyPal's classifier uses the same intelligence that powers Privacy Twins—understanding context, not just format.
PII, PHI, financials, proprietary schemas. Tables, documents, object stores. We classify what's sensitive across structured and unstructured data, including what's unique to your business. Zero tuning. Zero rule maintenance.
Every DSPM floods you with alerts. PrivacyPal ties sensitivity to business purpose, access patterns, and exposure—so you see what actually matters. A customer SSN in a public bucket is critical. A test fixture in a dev database is not.
Your team stops chasing noise. The risk engine understands context the same way we protect data at the edge—so you spend time on issues that move the needle.
Discovery without remediation is a report nobody reads. PrivacyPal lets you revoke access, mask columns, kick off workflows, or route to data owners—with full context so the right person can resolve it fast.
Policy-driven automation. One-click actions. Your team fixes issues faster than they're discovered. Same philosophy as the rest of PrivacyPal: don't just tell people about the problem—give them the tools to solve it.
"We're rolling out AI everywhere. Before PrivacyPal, we had no real visibility into what sensitive data lived where. Their internal scanning found things we didn't know we had—and the context-aware classification meant we could finally stop maintaining regex rules that kept missing the mark."
Databases, cloud storage, and the AI data layers that power RAG, agents, and memory. Same policies. Same controls.
SQL Server, MongoDB, Supabase, PostgreSQL. Google Drive, OneDrive, Dropbox. The databases and file stores your team actually uses.
Vertex AI vector databases, RAG pipelines, graph databases, agentic memory solutions. Where AI reads from—and where sensitive data can leak into models.
Object storage, local file shares, wherever your data lives. If it can feed an AI, it should be in your scan.
See how PrivacyPal scans, classifies, and helps you fix sensitive data across your estate.
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