GlitchZero: An AI-Native Middleware Trust Infrastructure for Real-Time Institutional Data Integrity Using Hybrid Anomaly Detection, Blockchain Anchoring, Explainable AI, and Agentic Mitigation

Modern institutional information systems remain critically vulnerable to unauthorized post-write data mutations - commonly called "Silent Edits" - wherein privileged users alter stored records without triggering any system alert. Traditional countermeasures either impose rigid hard-locks that disrupt legitimate administrative operations or rely on passive database logs that provide no real-time forensic analysis. This paper introduces GlitchZero v5, a novel AI-native middleware trust infrastructure implemented as a production-ready, language-agnostic service that integrates with any existing institutional system via a single RESTful API endpoint, requiring zero modifications to the host system's database schema or middleware. The system employs a five-stage AI pipeline: (1) an Isolation Forest-inspired triage engine classifies each incoming data mutation into ROUTINE, SUSPICIOUS, or CRITICAL tiers using a four-dimensional weighted feature vector; (2) a Semantic Delta Audit computes SHAP-style Explainable AI factor attribution, decomposing every risk score into normalized percentage contributors; (3) a Merkle Tree blockchain anchoring mechanism batches SHA-256 hashes and commits roots to Polygon Amoy Testnet; (4) an Agentic Mitigation Engine autonomously fires real HTTP POST rollback webhooks to the host system for CRITICAL events; and (5) an in-process performance logger computes live F1-score, Precision, Recall, Accuracy, and latency percentile metrics. Uniquely, GlitchZero v5 uses SQLite persistence with WAL mode, ensuring all audit history survives server restarts - a critical requirement for production deployment. Experimental evaluation on a synthetic dataset of 200 mutation events demonstrates a 66% ROUTINE triage rate reducing average processing latency by 40%, a weighted average latency of 3.4ms with sub-10ms p95, and classification metrics of Precision 0.94, Recall 0.92, and F1-score 0.93.