IN-SESSION DATA MASKING
Hoop identifies PII, PHI, financial data, and credentials inside database responses, API payloads, and terminal output, then redacts them in real time before they reach the engineer or AI agent. No pre-configuration. No schema discovery. One rule, thousands of resources.
SELECT * returns everything: SSNs, emails, payment details. On a laptop before your DLP tools notice.
HOW IT WORKS
Hoop parses wire protocols at the gateway layer. When a database response flows through, Hoop inspects the actual bytes in transit and identifies sensitive patterns. The redaction happens before the response reaches the client. What the engineer or agent receives is masked: ***-**-6789 instead of the original value.
Works across PostgreSQL, MySQL, MSSQL, MongoDB, HTTP APIs, and terminal output. No agents, no sidecars, no schema mapping.
No column tagging. No maintenance when schemas change. SSN formats, email addresses, credit card numbers, medical record identifiers.
Define domain-specific identifiers: member IDs, account numbers, internal codes. One pattern covers every connected database.
Different rules per team, role, or access level. Engineers see the data structure they need to debug without seeing customer records.
Every masking event logged: what was redacted, when, and for whom. Provable compliance without manual review.
CUSTOMER STORY
One rule: “No social security numbers in query responses.” Hoop applied it across every connected database without touching a single schema. No discovery phase. No per-database configuration. No maintenance when schemas change. The same rule works whether the database has 10 tables or 10,000.
USE CASE
Organizations sending production data to external LLMs or training pipelines can route traffic through Hoop. Sensitive fields are masked before the data leaves your network. The model trains on structurally accurate data with PII replaced. If the training provider is breached, your customer data isn't in the dataset.
SHELL AND FILE ACCESS
SSH into a server and read a file with customer data. The output arrives masked. Grep a log with payment details. Masked. Check environment variables with credentials. Masked. No rules to configure. The ML model detects sensitive patterns in any text stream, in any protocol, in real time.
ORGANIZATIONAL IMPACT
Every masked field, every redacted response becomes an auditable event. Your compliance team sees which frameworks are covered, which controls are passing, and where gaps remain — updated continuously.