Arbitex Gateway vs. Zscaler ZIA/ZPA
Zscaler has earned its place in enterprise security stacks. Its global network of proxy nodes, proven SSE/SASE platform, CASB capabilities, and deep integrations with cloud identity providers are genuine strengths that thousands of organizations rely on for secure internet access and zero-trust network access. That foundation is real. The architectural gap surfaces when enterprises extend Zscaler to cover AI governance. Zscaler governs traffic that routes through its proxy — conversational AI prompts from developer endpoints, containerized pipelines, and SaaS-embedded tools frequently bypass the tunnel entirely. And when regulated data does traverse Zscaler, enforcement happens on Zscaler's infrastructure, not the customer's. AI governance requires enforcement at the model boundary: prompt-level DLP tuned for natural-language sensitive data, a policy engine that composes conditions and actions across providers, and an audit chain that produces tamper-evident records of every AI governance decision.
Feature Comparison
| Capability | Zscaler ZIA/ZPA | Arbitex Gateway |
|---|---|---|
| Global network scale and enterprise SSE/SASE platform | ✓ Genuinely strong — one of the largest globally distributed cloud security proxy networks; proven SSE/SASE platform with deep enterprise integrations, broad cloud app coverage, and established trust at scale | ~ Purpose-built for AI governance at the model boundary — not a general network security platform; complements SSE/SASE rather than replacing it |
| CASB and SaaS application visibility | ✓ Strong CASB capability — broad SaaS application catalog, Shadow IT discovery, DLP policies applied to cloud app traffic, access controls for sanctioned and unsanctioned SaaS use | ~ AI application governance rather than broad SaaS CASB — enforces policy on AI API calls across 9+ providers with consistent governance regardless of which application initiates the request |
| Prompt-level DLP — conversational AI detection (PHI, CUI, MNPI, NPI) | ~ Network DLP built for structured data in file transfers and form submissions — pattern matching on HTTPS payload bytes; misses PHI, CUI, and NPI expressed as natural-language prose in conversational AI prompts | ✓ Multi-layer content inspection pipeline: 80+ pattern rules → 40 ML recognizers (ML-based entity recognition) → AI-powered contextual validation — purpose-built for conversational AI prompt payloads in natural language |
| Policy engine — content categories, rule composition, and enforcement actions | ~ URL category filtering and DLP policy rules applied to web traffic — designed for network access control; no AI-specific content category taxonomy or rule composition across prompt conditions and model routing actions | ✓ Policy engine with content categories, composable conditions (DLP tier result + compliance rule + identity context), and enforcement actions (redact, block, route-to, quarantine) applied per AI request |
| AI traffic coverage — proxy-bypass and direct API access | ~ Governs AI traffic routing through the ZIA tunnel — developer direct API calls, containerized AI pipelines, SaaS-embedded AI tools, and CI/CD pipelines frequently bypass the proxy without enforcement | ✓ Enforcement at the AI API layer — independent of network routing; every AI request from every application routes through the governance pipeline regardless of whether a network proxy is in path |
| Air-gap Outpost — disconnected and isolated environment deployment | ✕ Not available — ZIA requires continuous cloud connectivity to Zscaler's distributed proxy nodes; air-gapped or network-isolated operation is not supported by the architecture | ✓ Outpost operates fully disconnected — cryptographically signed rule bundle updates fail-closed; local audit queue accumulates governance events and syncs on reconnection; designed for classified and isolated environments |
| Data residency — enforcement inside customer infrastructure | ✕ All governed AI traffic transits Zscaler's cloud proxy nodes before reaching model providers — PHI, CUI, MNPI, and ITAR-controlled data pass through Zscaler's infrastructure, not the customer's | ✓ Hybrid Outpost deploys the data plane inside the customer's VPC — AI requests inspected, governed, and logged within the customer's own perimeter before data leaves for model providers |
| tamper-proof audit trail — tamper-evident AI governance records | ✕ Network proxy event logs and DLP alert records — appropriate evidence for cloud access examinations; not structured as cryptographically verifiable AI governance audit records | ✓ tamper-proof audit record per AI interaction — model provider, DLP tier result, compliance rule matched, enforcement action, and cryptographic integrity chain — structured for regulatory evidence production |
| Compliance Policy Packs — 12 frameworks enforced at the AI model boundary | ~ DLP policy templates for HIPAA and PCI-DSS in the network DLP context — no AI-specific compliance bundles mapped to NIST AI RMF, NAIC Model Bulletin, or GLBA framework requirements at the model request layer; each compliance mapping requires custom configuration work | ✓ 8 pre-built Policy Pack bundles with inline enforcement rules — PCI-DSS, HIPAA, GDPR, GLBA, SOX, CCPA, BSA/AML, SEC Reg FD, FERPA, EU AI Act, NIST AI RMF, ISO/IEC 42001 — enforced at the AI request layer per prompt; single-toggle activation, no custom configuration per framework |
| Multi-provider AI routing — policy-governed across 9+ providers | ✕ Proxy passes or blocks traffic to AI provider endpoints — no routing logic across providers, no policy composition combining conditions and actions, no unified governance record across providers | ✓ Route across 9+ AI providers by policy — single, compare, and summarize modes — with one policy engine and one audit chain governing all providers regardless of which handles each request |
| SIEM integration — AI governance event schema | ~ Zscaler network security events delivered to SIEM — proxy connection records and DLP alerts structured for cloud SOC operations; no AI governance event schema fields (model, prompt classification, compliance rule, enforcement action) | ✓ 7 native AI governance SIEM connectors: Splunk HEC/OCSF, Sentinel DCR, Elastic Bulk, Datadog, Sumo Logic, QRadar CEF/TLS, Cortex XSIAM — with AI-governance-specific event fields per record |
| Credential intelligence — compromised API key detection at AI boundary | ✕ Not available at the AI gateway layer — Zscaler DLP detects structured data patterns, not known-compromised API keys and tokens embedded in AI request and response payloads | ✓ Compromised credential dataset checked per AI request at sub-millisecond latency — detects leaked keys and compromised credential dataset matches before they propagate through AI workflows |
| OAuth M2M — governed credentials for AI pipelines and DevSecOps | ✕ No AI pipeline credential model — Zscaler is a network proxy; it does not issue scope-limited OAuth tokens for service-to-service AI gateway authentication | ✓ RFC 6749 client credentials grant — RS256-signed JWTs with JWKS key discovery, per-client revocation, zero-downtime key rotation — per-pipeline credentials, not shared secrets |
| GeoIP and anonymous IP detection — geographic access policy enforcement | ~ Geographic location data available from Zscaler proxy nodes for traffic routing — limited anonymous IP detection; no bundled offline GeoIP database for air-gap environments or per-request ASN metadata enrichment | ✓ Bundled GeoIP database with anonymous IP detection — identifies proxy, VPN, Tor, and hosting-provider origins per request; country, region, and ASN metadata enrichment; offline-capable for Hybrid Outpost deployments |
| DLP detection accuracy — published per-entity accuracy metrics | ✕ Zscaler does not publish per-entity detection accuracy metrics for inline DLP — detection capabilities described in terms of data patterns and ML classifiers, not measured per-entity detection accuracy; no published validation dataset or regression gate methodology | ✓ Three-tier detection — pattern rules, ML entity recognition, and contextual validation — with per-entity evaluation against a labeled corpus |
| Inspection latency — time added per AI request for DLP + policy evaluation | ~ Network proxy inspection adds 50–200ms per request depending on routing through Zscaler's globally distributed proxy nodes — latency varies by geographic distance and tunnel overhead | ✓ <2ms p99 inspection latency — multi-layer content inspection pipeline and policy evaluation execute locally at the gateway with no cloud round-trip; optimized offline models run deterministic inference at the edge |
| Offline inference for air-gap and Outpost deployment | ✕ No local ML inference capability — Zscaler's DLP runs on Zscaler's cloud infrastructure; air-gapped environments cannot access the detection pipeline | ✓ AI-powered contextual validator using optimized offline inference — runs deterministic inference on Outpost hardware without cloud connectivity; same detection accuracy in air-gap as cloud deployment |
| Pricing transparency — per-request cost visibility for AI governance | ✕ Enterprise platform licensing with per-user pricing across the full SSE/SASE stack — AI governance cost is bundled into the broader platform subscription with no per-request visibility | ✓ Per-request pricing with granular cost visibility — organizations see governance cost per AI interaction with budget caps and usage quotas enforced at the gateway level |
| Encryption enforcement at startup | ~ Full TLS/SSL inspection via Zero Trust Exchange — Zscaler inspects all encrypted traffic at the network layer with unlimited SSL/TLS inspection and cloud HSM key management; enforcement is at the proxy level, not at the application startup level | ✓ Application-level startup validators reject plaintext connections in production — Redis, telemetry, model provider URLs validated at process start; mTLS with CA pinning for Outpost traffic; gateway refuses to start if encryption is misconfigured |
Where Arbitex Gateway Wins
Zscaler's network proxy architecture has structural coverage gaps for AI workloads
Zscaler's SSE/SASE platform is purpose-built for securing outbound internet access and application access — and it does that well. The gap is architectural when applied to AI governance. Zscaler governs AI traffic that routes through the ZIA tunnel. Enterprise AI workloads originate from surfaces that frequently do not: developer endpoints without the Zscaler agent installed, containerized AI pipelines in cloud-native environments, SaaS applications with embedded AI features routing their own network calls, and CI/CD pipelines making direct API calls from infrastructure Zscaler was not designed to intercept. Asserting that Zscaler covers AI governance requires the organization's entire AI API call volume to route through ZIA — an assumption that rarely holds across diverse enterprise AI deployment patterns.
AI prompts require prompt-level DLP — network DLP detects different surfaces
Zscaler's DLP is strong for what it was designed to do: detecting SSNs, credit card numbers, and classified markings in files and form submissions crossing the network perimeter. AI prompts require different detection capability. A clinical AI prompt containing a patient's diagnosis and treatment plan written in natural-language prose does not present PHI as the structured field formats network DLP is built to detect. A query referencing ITAR-controlled design parameters embedded in a technical summary is not a file upload pattern. Arbitex's multi-layer content inspection pipeline — ML-based entity recognition that detects sensitive data in conversational text, followed by AI-powered contextual validation — is designed for the AI prompt surface that network DLP was not built to cover.
Air-gap Outpost and data residency for environments Zscaler's cloud architecture cannot reach
Zscaler's cloud proxy model is the right architecture for most enterprise environments — and it delivers genuine scale and coverage there. The boundary is environments that cannot route traffic through Zscaler's cloud nodes: classified government networks, ITAR-controlled manufacturing facilities, healthcare systems with strict data residency requirements, and financial institutions where regulated data cannot traverse third-party infrastructure. Arbitex's Hybrid Outpost deploys the enforcement data plane inside the customer's own VPC or air-gapped network. AI requests are inspected, governed, and logged within the customer's perimeter before data leaves for model providers. For regulated data that cannot leave the customer's infrastructure during inspection, this is the enforcement model the architecture requires.
Compliance examinations request AI governance records with audit chain integrity
When a FISMA auditor, OCR investigator, or federal IG requests AI governance evidence — which AI requests involved regulated data, what policy evaluated each one, what enforcement action executed, and whether the record is tamper-evident — Zscaler produces network proxy event logs and DLP alert records. These are appropriate evidence for cloud access governance examinations. They are not AI governance audit records. Arbitex produces a cryptographically tamper-proof audit trail documenting every AI governance decision: what data was in the request, which DLP tier flagged it, which compliance rule applied, what enforcement action ran, and a cryptographic integrity chain that demonstrates the record has not been modified since creation. That structure is what compliance evidence production requires for AI governance specifically.
We measure and publish accuracy. Zscaler does not.
Zscaler's inline DLP describes detection quality in terms of data patterns and ML classifiers rather than per-entity measurement. Arbitex evaluates detection quality per entity type against a labeled corpus rather than as a single blended score. Neither vendor publishes per-entity accuracy figures today: Arbitex is expanding its evaluation corpus to the point where such figures would carry a meaningful confidence interval, and will publish them with their corpus size and measurement date attached when it does. The difference a buyer can act on now is architectural — Arbitex inspects at the model boundary with three independent detection tiers, and can be run against their own traffic before purchase.
Related Resources
See Arbitex Gateway in action
AI model boundary governance for regulated organizations — enforcement inside your VPC, prompt-level DLP for conversational AI payloads, and tamper-proof audit records that satisfy compliance examination requirements.