Insights
AI governance, security, and enterprise compliance from the Arbitex team.
How We Measure and Publish Our DLP Detection Accuracy
Most DLP vendors never publish accuracy. We do. Here is how we measure detection accuracy across every entity type, and gate product releases on them.
Enterprise Data Isolation Strategies for AI Governance
How Arbitex's four isolation tiers — Shared, Enhanced, Outpost, and Air-Gap — give enterprises control over where AI traffic lives and who can access it.
Data Sovereignty and Hybrid AI Deployment for Regulated Industries
Regulatory mandates don't care about vendor convenience. Here's how to match AI gateway deployment architecture to your actual data sovereignty requirements.
How We Audit Our Own Platform: 79 Findings, 7 Repos, One Sprint
How Arbitex audits its own platform before every major release — finding issues systematically, categorizing rigorously, and closing them before shipping.
Enterprise AI Security: Complete Architecture Overview
Enterprise AI governance requires more than a single control point. This overview covers every layer of the Arbitex Gateway — from routing to audit.
How the Arbitex DLP Pipeline Detects Sensitive Data
A deep dive into the multi-tier DLP pipeline — contextual validation, policy engine, and contextual scoring — at sub-2ms latency.
How a 3-Tier DLP Pipeline Stops Sensitive Data in AI Traffic
A guide to Arbitex's 3-tier DLP — pattern matching, NER, and contextual validation — covering when each tier fires and why cascades matter.
Outpost vs SaaS: Choosing the Right Arbitex Deployment Mode
Arbitex ships two deployment modes: Cloud SaaS and Hybrid Outpost. Choose the right model for your data residency, compliance, and infrastructure requirements.
Inside the Arbitex Policy Engine: Rules and Compliance Packs
How the Arbitex policy engine evaluates AI requests: multi-condition rules, combining algorithms, and compliance packs for precise security team control.
Why DLP Needs to Understand AI Conversations, Not Just Files
Traditional DLP tools were built for files and emails, not AI conversations. Here is why real-time AI chat demands a new approach to data loss prevention.