A negotiating temporal database that simulates writes, enforces guardrails, and proposes safe alternatives instead of blind acceptance or rejection.

SentinelDB Documentation

SentinelDB is a high-throughput, query-optimized audit logging database designed to store, analyze, and report security-critical events

Real-time compliance guardrails for LLM interactions. Runs as an MCP server checking every prompt/response against GDPR, EU AI Act, CCPA, and HIPAA rules. Features 13 built-in detection rules, risk scoring, drift detection, context-aware jailbreak detection, prompt sanitization, and immutable Parquet audit logging with DuckDB queries.

Lightweight knowledge graph from plain text notes — extract hidden relationships automatically.

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A personal OSINT and cybersecurity monitoring system built with Go, designed to continuously watch digital assets and alert when exposures or anomalies are detected.

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Build SentinelDB: An AI-driven self-healing database layer that: Monitors query behavior and storage integrity Detects anomalies in data patterns Verifies data authenticity using cryptographic fingerprints Automatically reconstructs corrupted tables Assigns a live “Trust Score” to every table

Self-hosted, production-grade PostgreSQL as a Service — with replication, connection pooling, automated backups, and smart query routing, deployed on your own cloud in minutes.

The purpose of SentinelDB is to ensure that financial data remains accurate, even when the incoming source is inconsistent. This system acts as a digital quality-control department, ensuring that "Bad Data" is never deleted, but instead analyzed, repaired, and reported.

JDBC Proxy Driver for handling audit trail and record-level encryption for Java applications

Java client for the SentinelDB API

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