Arbitex Gateway vs. Forcepoint DLP
Forcepoint is a recognized leader in data loss prevention built around behavioral analytics and insider threat detection. Its UEBA-driven approach monitors endpoint activity, network egress, and user risk scoring across traditional data channels. Arbitex Gateway addresses a different surface — governing AI model requests and responses at the API boundary, where enterprise data enters and exits large language models.
Where Forcepoint Excels
Behavioral Analytics & UEBA
Forcepoint's User and Entity Behavior Analytics continuously profiles user activity patterns to detect anomalous data handling — unusual file access, atypical data movement, or risk score spikes. This behavioral layer adds context that pure pattern-matching DLP cannot provide for traditional channels.
Insider Threat Detection
Forcepoint's insider threat program combines behavioral indicators with DLP policy violations to identify users whose data handling patterns suggest elevated risk. It correlates activity across endpoints, email, web, and network channels into a unified risk score.
Endpoint & Network DLP
Forcepoint covers traditional DLP vectors comprehensively: endpoint agents monitor file operations and removable media, network inspection governs data in transit, and web/email gateways enforce policy on those channels. It is a mature platform for the channels it was built to govern.
Feature Comparison
| Capability | Forcepoint DLP | Arbitex Gateway |
|---|---|---|
| AI model request governance (real-time, pre-model) | ✕ Not in scope — Forcepoint governs endpoint, network, and email channels, not AI API traffic | ✓ Core product purpose — every AI request inspected and governed before reaching any model |
| Behavioral analytics / UEBA | ✓ UEBA with continuous user risk scoring and anomaly detection across traditional channels | ✕ Not in scope — Arbitex governs data content at the AI boundary, not endpoint user behavior patterns |
| Insider threat program | ✓ Unified risk scoring combining behavioral indicators with DLP policy violations across all monitored channels | ✕ Not an insider threat platform — Arbitex enforces data policy on AI model traffic regardless of user risk score |
| Endpoint DLP (file, clipboard, removable media) | ✓ Comprehensive endpoint agent coverage — file operations, USB transfers, clipboard, print, and screen capture monitoring | ✕ No endpoint agent — Arbitex operates at the AI API layer, not the desktop |
| 3-tier DLP pipeline for AI content | ✕ Pattern and fingerprint detection designed for structured data in traditional channels — not optimized for AI prompt/response natural language | ✓ 80+ regex detectors → ML-based entity recognition → contextual analysis, purpose-built for AI request and response content |
| Pre-built compliance bundles (12 frameworks) | ✕ Compliance templates for traditional DLP channels — no pre-built AI governance bundles | ✓ 12 compliance frameworks enforced at the AI model boundary out of the box |
| Multi-LLM provider routing (9+ providers) | ✕ No AI provider integration — Forcepoint governs traditional network and endpoint egress | ✓ Route AI traffic across 9+ providers under a single compliance policy layer |
| Hybrid deployment — data plane in your VPC | ✓ On-prem and cloud deployment options for DLP agents and network appliances | ✓ Hybrid Outpost runs inside your VPC; AI traffic inspected entirely within your environment |
| Policy enforcement on AI model responses | ✕ No visibility into AI model response content or response-layer data governance | ✓ Every model response inspected and governed by the same pipeline as the request |
| Tamper-proof audit logging | ✕ Standard DLP event logs — no cryptographic chain of custody for AI governance decisions | ✓ Every AI request, detection, and enforcement action in an immutable tamper-proof log |
Where Arbitex Gateway Wins
Forcepoint DLP does not sit in the AI model request path
Forcepoint's architecture was designed around endpoints, network egress points, and email/web gateways — the channels where enterprise data has historically moved. When users submit prompts to AI models, or when applications call model APIs, that traffic flows through HTTPS API calls that bypass Forcepoint's endpoint agents and network appliances. Arbitex Gateway is the enforcement layer purpose-built for this surface — every AI request and response passes through the gateway before data reaches any model.
Measured accuracy on AI content, not just traditional data types
Forcepoint's detection accuracy is tuned for structured and semi-structured data in traditional channels — credit card numbers in email, document fingerprints on endpoints. AI prompts and model responses contain dense natural language with mixed context, embedded code, and conversational patterns that traditional DLP detectors were not evaluated against. Arbitex's 3-tier pipeline — regex patterns, ML-based entity recognition, and contextual analysis — is built and measured specifically for AI content.
A policy engine for AI governance, not endpoint/network rules
Forcepoint's policy framework governs traditional data movement — blocking USB transfers, quarantining email attachments, restricting web uploads. These enforcement actions target channels, not AI model interactions. Arbitex's policy engine was designed around AI governance primitives: block, redact, warn, and route decisions applied to prompts and responses, with compliance framework bundles that map detection to regulatory requirements at the AI boundary.
Air-gap Outpost for sovereign AI governance
Forcepoint supports on-premises deployment for its endpoint and network DLP components. However, it does not offer an air-gapped AI governance data plane that keeps all model traffic within a customer's environment. Arbitex's Outpost deployment model runs the full inspection pipeline — 3-tier DLP, policy engine, audit logging — inside your VPC or air-gapped network, ensuring that AI traffic and governance decisions never leave your controlled environment.
Related Resources
See Arbitex Gateway in action
Govern every AI request. Enforce compliance at the model boundary. Produce the audit record that holds up in an examination.