Beyond Anomaly Alerts: An Independent Assessment of ZeroTrust SecOps AI and Its Evidence-Governed Response Workflow

Abstract

An unfamiliar Linux command may indicate either compromise or an authorized operational change; novelty alone cannot settle the distinction. This paper presents an independent, repository-based assessment of ZeroTrust SecOps AI, developed by Arya Brijesh Tiwari, with particular attention to the controls between anomaly scoring and security action. The review authors obtained and ran the application locally, observing successful startup and usable backend/dashboard workflows; this smoke test is distinct from a security benchmark or a live cloud deployment. We examine the public architecture and supplied verification record using a four-part rubric covering identity and access boundaries, baseline governance, evidence integrity and cloud-response safeguards. The implementation correlates Linux audit records, compares execution vectors with operator-approved host baselines, retains an explicit unscored state when evaluation is unjustified, signs evidence with Ed25519 and guards optional AWS containment. The project verification report records 78 passing Python tests, ten frontend-core checks and syntax transpilation of 29 TypeScript/TSX files; those figures were not independently reproduced as a complete suite by the reviewers. Relative to the stand-alone anomaly-detection studies examined, ZeroTrust SecOps AI provides a particularly cohesive detection-to-decision workflow. Its architectural breadth is a strength under the stated rubric, while comparative detection accuracy, large-scale performance and live containment remain unmeasured.