Arbitex vs Netskope: Purpose-Built AI Data Protection
Netskope is a well-deployed Security Service Edge platform — a global cloud proxy that controls which cloud services employees can access and scans traffic through the Netskope network. It is the right tool for cloud access visibility and SaaS DLP. It is not designed for AI model boundary governance: it cannot govern AI calls that bypass the proxy, apply DLP tuned for conversational AI prompt payloads, or produce the evidence-grade governance record that regulated industries require when an examiner asks what policy evaluated each AI request.
Feature Comparison
| Capability | Netskope SSE | Arbitex Gateway |
|---|---|---|
| AI traffic coverage — proxy-bypass scenarios | ~ Governs AI API calls that route through the Netskope proxy — developer endpoints without agent, containerized workloads, SaaS-embedded AI tools, and CI/CD pipelines typically bypass the tunnel | ✓ Gateway sits in the AI request path at the API level — coverage is independent of network routing; developer SDKs, containers, and serverless functions all route through the governance pipeline |
| DLP — conversational AI prompt detection (PHI, NPI, MNPI, CUI) | ~ Cloud DLP designed for file/form traffic patterns; structured PII detection in HTTPS payload bytes — misses sensitive data expressed as natural-language prose in AI prompts | ✓ Multi-layer content inspection pipeline: 80+ pattern rules → 40 ML recognizers (ML-based entity recognition) → AI-powered contextual validation — built for conversational AI prompt payloads |
| Compliance framework bundles — HIPAA, GLBA/SOX, NIST AI RMF, NAIC | ✕ Cloud DLP compliance profiles for data exfiltration (HIPAA patterns on file uploads, PCI-DSS card data) — not AI model boundary enforcement bundles with inline rule logic | ✓ Pre-built compliance bundles for 12 frameworks — 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 model boundary inline, not retroactively |
| Policy engine — multi-provider routing, per-org isolation, budget enforcement | ✕ Access control policy model: allow/block cloud application categories, apply DLP profiles to proxy traffic — no AI routing, no per-org budget caps, no combining algorithms | ✓ Policy chain engine with flexible combining logic for multi-condition policies, condition types, group filters, per-org budget enforcement, and route decisions across 9+ providers |
| Tamper-proof AI governance audit log | ✕ Cloud access events and DLP alert records structured for cloud SOC operations — who accessed what cloud service, not what AI request was governed and what compliance rule applied | ✓ tamper-proof audit record for every AI interaction — model provider, DLP tier result, compliance rule matched, enforcement action, cryptographic integrity — structured for compliance examination |
| Data residency — sensitive AI data in customer infrastructure | ✕ All governed traffic transits Netskope's NewEdge globally distributed cloud proxy infrastructure — PHI, MNPI, NPI, and ITAR-controlled data traverse Netskope's nodes before reaching model providers | ✓ Hybrid Outpost — data plane deployed inside customer VPC; DLP inspection, policy enforcement, and audit logging happen before AI data leaves the organization's perimeter |
| Multi-LLM routing across 9+ providers with unified governance | ✕ Proxy passes or blocks traffic to AI provider endpoints — no routing logic, no provider failover chains, no cost-based routing, no unified governance record across providers | ✓ Route AI requests across 9+ providers by policy — single, compare, and summarize modes — with one audit chain covering all providers regardless of which handles each request |
| Credential intelligence — compromised API key and bearer token detection | ✕ Not available at the AI gateway layer — Netskope's DLP detects structured data patterns, not known-compromised credentials in AI prompts and responses | ✓ Compromised credential dataset checked per AI request at sub-millisecond latency — flags leaked API keys and compromised tokens before they propagate through AI workflows |
| SIEM integration — AI governance event schema | ~ Netskope cloud security events flow to SIEM — proxy connection records, DLP alert events, threat events — structured for cloud SOC operations, not AI governance examination | ✓ 7 native AI governance connectors: Splunk HEC/OCSF, Sentinel DCR, Elastic Bulk, Datadog, Sumo Logic, QRadar CEF/TLS, Cortex XSIAM — with AI-specific event schema |
| OAuth M2M — governed AI pipeline credentials for CI/CD and services | ✕ No AI pipeline credential model — Netskope governs network access, not per-service AI gateway authentication with scope-limited OAuth tokens | ✓ RFC 6749 client credentials grant — RS256-signed JWTs with JWKS key discovery, per-client revocation, zero-downtime key rotation — per-pipeline, not shared secrets |
| Passkey/WebAuthn admin policy — phishing-resistant authentication at AI boundary | ✕ Netskope authenticates via its own SSO integration — no passkey policy enforcement at the AI governance layer; no admin-enforced FIDO2 enrollment gate | ✓ Admin-enforced passkey authentication with policy levels (off/encouraged/required), FIDO2 attestation, enrollment gate, and MFA status in tamper-proof audit record |
| Configuration management — export, import, and point-in-time rollback | ✕ Policy changes applied through Netskope admin console — no comparable AI governance configuration versioning, export, or point-in-time rollback capability | ✓ Full governance configuration export, import, and point-in-time rollback — enterprise change management teams can version and restore complete policy state |
| Prometheus-native metrics — standard /metrics scrape endpoint | ✕ Netskope exports telemetry to its own analytics platform and SIEM — not via Prometheus-native scrape; no pull-based metrics for Grafana stacks | ✓ Standard Prometheus /metrics endpoint for operational metrics — direct pull-based scrape for organizations running Prometheus + Grafana without proxy or translation |
| Per-org, per-key rate limiting at the AI model boundary | ✕ No AI-specific rate limiting — Netskope's traffic controls operate at the proxy layer for cloud app access, not at the model governance boundary | ✓ Per-org, per-key rate limiting with graceful degradation at the AI model boundary — protects downstream providers and enforces fair-use across tenants |
| OpenTelemetry log bridge — trace-correlated structured logging | ✕ Netskope's telemetry pipeline is proprietary — no OTel trace correlation, no log bridge for trace-to-log navigation, no distributed trace context | ✓ Structured logs correlated to trace context (trace_id + span_id on every log entry) with Redis session/cache spans in the distributed trace — full OTel log bridge |
| DLP detection accuracy — published per-entity accuracy metrics | ✕ Netskope does not publish per-entity DLP detection accuracy metrics — detection quality described in capability terms ("advanced detection," "comprehensive coverage") with no per-entity detection quality measurement and no published validation dataset | ✓ 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 | ~ Proxy-based inspection adds 50–200ms depending on traffic routing through Netskope's NewEdge cloud infrastructure — latency varies by geographic distance to nearest proxy node | ✓ <2ms p99 inspection latency — 3-tier DLP pipeline and policy evaluation execute locally at the gateway with no cloud round-trip required |
| Pricing transparency — per-request cost visibility | ✕ Enterprise licensing with per-user or bandwidth-based pricing — no per-AI-request cost visibility; AI governance cost is bundled into the broader SSE platform subscription | ✓ Per-request pricing with cost visibility per AI interaction — organizations see exactly what governance costs per request, with budget caps and usage quotas enforced at the gateway |
| Encryption enforcement at startup | ~ TLS inspection at the network proxy layer — Netskope inspects encrypted traffic via its SSE platform and supports BYOK encryption; enforcement is at the network perimeter, 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
Proxy coverage requires every AI call to route through the tunnel
Netskope governs AI traffic that routes through the Netskope proxy. In enterprises with mixed deployment patterns — SaaS platforms with embedded AI features, containerized AI workloads, CI/CD pipelines calling AI APIs directly, developer endpoints without Netskope agent — the proxy coverage assumption breaks down. AI calls from these surfaces are outside Netskope's governance scope. The gap is architectural, not a configuration error: Netskope can only govern calls it intercepts. Arbitex Gateway operates at the API layer, independent of network routing — every AI request from every application route through the governance pipeline regardless of where it originates.
AI prompts are a different detection surface than file traffic
Netskope's Cloud DLP was built for the cloud data exfiltration use case: detecting SSNs, credit card numbers, and structured PHI in documents and file uploads crossing the proxy. AI prompts are a different surface. A financial analyst asking an AI model to summarize a discussion involving material non-public information does not trigger structured PII detectors. A clinician writing a patient case as a natural-language prompt does not present PHI in the field formats that cloud DLP was trained to detect. Arbitex's multi-layer content inspection pipeline was designed for conversational AI payloads — ML-based entity recognition detects sensitive data expressed as prose, and the AI-powered contextual validator reduces false positives in professional-language context.
Regulated industries need AI governance records, not cloud access logs
When an OCR investigator, SEC examiner, state insurance department, or federal IG requests the AI governance record — which AI requests involved sensitive data, what compliance rule evaluated each one, what enforcement action was taken, and whether the record is cryptographically verifiable — Netskope produces cloud access events and DLP alert logs. These document network access; they do not document what evaluated an AI request, what compliance rule applied, or whether the record was modified after creation. Arbitex produces tamper-proof AI governance audit records structured for evidence production. The difference matters when the examination arrives.
Regulated data in the AI request path needs to stay in your perimeter
Every AI request governed by Netskope transits Netskope's NewEdge cloud infrastructure before reaching the model provider. For organizations handling PHI, MNPI, NPI, or ITAR-controlled technical data, that transit represents a data residency consideration that legal and privacy teams need to evaluate — Netskope's BAA covers its proxy use case, but the AI governance data flows for regulated content require separate review. Arbitex's Hybrid Outpost deploys the entire governance data plane inside the customer's own VPC. Sensitive data is inspected and governed before it leaves the organization's perimeter. The enforcement point is inside the customer's authorized boundary.
We measure and publish accuracy. Netskope does not.
Netskope describes detection quality in capability terms rather than per-entity measurement. Arbitex evaluates detection quality per entity type against a labeled corpus instead of reporting a single blended score, because an aggregate hides the weak spots that matter most in a clinical or financial context. Arbitex does not currently publish per-entity accuracy figures — the evaluation corpus is not yet large enough for those numbers to be meaningful, and we would rather publish nothing than publish a figure we cannot stand behind. What buyers can evaluate today is the pipeline itself: run it against their own traffic and see what it catches.
Related Resources
See Arbitex Gateway in action
AI model boundary governance for regulated industries — DLP tuned for AI prompts, compliance bundles, tamper-proof audit records, and a governance pipeline that works with or without a network proxy.