Arbitex Gateway vs. Protect AI
Protect AI is a well-regarded ML security platform that addresses a critical gap in the AI stack: securing the models and datasets themselves. Their tools scan ML models for backdoors, evaluate model vulnerabilities through red-teaming (Garak), generate SBOMs for AI artifacts, and assess dataset integrity before models reach production. This is valuable pre-deployment security work. The gap that Arbitex addresses is different: once a model is deployed and users are sending prompts containing regulated data — PHI, MNPI, PII, CUI — who governs what data flows through the model boundary at runtime? Protect AI secures the model. Arbitex secures the data flowing through it. Many security teams deploy both.
Feature Comparison
| Capability | Protect AI | Arbitex Gateway |
|---|---|---|
| Primary focus | ✓ ML model and dataset security — scanning models for backdoors, evaluating model vulnerabilities, securing the ML supply chain, and generating AI SBOMs; strong ML security research community and open-source tooling (Garak, ModelScan) | ✓ Runtime data governance at the AI model boundary — DLP for conversational AI prompts, compliance bundles, policy enforcement, and audit logging for regulated data flowing through deployed AI systems |
| Deployment stage | ✓ Pre-deployment and CI/CD pipeline — model scanning and vulnerability assessment before models reach production; continuous monitoring of model artifacts in registries | ✓ Runtime — enforcement at the point where users and systems send data to AI models; governance happens on every request, not before deployment |
| AI red-teaming and model vulnerability assessment | ✓ Garak — open-source LLM vulnerability scanner that probes models for prompt injection susceptibility, jailbreak resistance, data leakage risks, and hallucination patterns; comprehensive red-teaming methodology | ✕ Arbitex does not red-team models — it governs the data flowing through them; model vulnerability assessment is a different security function from runtime data governance |
| ML model scanning and SBOM generation | ✓ ModelScan detects backdoors, malicious payloads, and unsafe serialization in ML model files; AI SBOM generation provides supply chain visibility for model artifacts across registries | ✕ Arbitex does not scan model files or generate AI SBOMs — it operates at the API request layer, not the model artifact layer |
| DLP at the AI model boundary — runtime data inspection | ✕ Not a DLP platform — Protect AI secures models and datasets, not the content of runtime AI prompts and responses; no entity detection for regulated data in conversational payloads | ✓ Multi-layer content inspection pipeline built for conversational AI: 80+ pattern rules → 40 ML recognizers → AI-powered contextual validator — detects regulated data (PHI, MNPI, PII, CUI) in natural-language AI prompts at runtime |
| Policy engine — combining algorithms, condition types, per-org isolation | ~ Guardian product provides policy-based controls for AI applications — focused on model behavior governance (prompt injection defense, output validation) rather than data handling policy with combining algorithms and compliance framework mapping | ✓ Policy chain engine with flexible combining logic for multi-condition policies, condition types, group filters, per-org budget enforcement, and route decisions across 9+ AI providers |
| Compliance audit trail — HIPAA, SOC 2, SEC evidence | ~ Model risk documentation and AI governance reporting — focused on model security posture and AI risk assessment rather than per-request compliance evidence for data handling regulations | ✓ tamper-proof audit record for every AI interaction — model provider, DLP tier result, compliance rule matched, enforcement action — structured for HIPAA, SOC 2, and regulatory compliance examination |
| Air-gap deployment — data sovereignty inside customer VPC | ~ On-premises deployment options for model scanning infrastructure — model security tools can run within customer environments for sensitive model artifacts | ✓ Hybrid Outpost — full governance data plane deployed inside customer VPC; DLP inspection, policy enforcement, and audit logging happen within the customer's authorized boundary |
| Data entity detection — per-entity accuracy metrics | ✕ Not an entity detection platform — Protect AI identifies model vulnerabilities and dataset issues, not regulated data entities embedded in AI prompts | ✓ Three-tier detection — pattern rules, ML entity recognition, and contextual validation — with per-entity evaluation against a labeled corpus |
| Complementary deployment — use both together | ✓ Secures the model before it reaches production — scanning for backdoors, assessing vulnerabilities, generating supply chain documentation; pairs with runtime governance for full-stack AI security | ✓ Secures the data flowing through deployed models — DLP, compliance enforcement, and audit logging at runtime; pairs with pre-deployment model security for full-stack AI security coverage |
Where Arbitex Gateway Wins
Different threat models — complementary, not competing
Protect AI addresses a critical question: is this model safe to deploy? ModelScan finds backdoors in model files. Garak probes for prompt injection susceptibility and jailbreak resistance. AI SBOMs document what's in the model supply chain. These are pre-deployment security controls for the model artifact itself. Arbitex addresses a different question: when users send regulated data to a deployed model, does the data handling comply with organizational policy and regulatory requirements? Runtime DLP, compliance enforcement, and audit logging operate on every request at the model boundary. The threat models are complementary — one secures the weapon, the other secures the ammunition.
Runtime data governance is not model security — different data, different controls
Protect AI's detection surface is model files, datasets, and model behavior under adversarial prompts. Arbitex's detection surface is the content of production AI prompts — the PHI a clinician types, the MNPI a trader pastes, the CUI an engineer queries. These are fundamentally different data types requiring different detection techniques. ML-based entity recognition, contextual validators, and compliance framework mapping are data governance controls, not model security controls. Organizations need both, but conflating them creates gaps in both coverage areas.
Compliance evidence for data handling, not model risk
When a HIPAA examiner asks how PHI is protected in AI workflows, or a SEC examiner reviews MNPI controls in trading AI, the evidence they need is per-request data handling records: what data was detected, what policy was applied, what enforcement action was taken, and whether the audit trail has integrity. This is different from model risk documentation. Protect AI provides model security posture evidence — important for AI governance frameworks. Arbitex provides data handling compliance evidence — important for regulatory examinations. Both are needed; neither substitutes for the other.
Full-stack AI security: model security + data governance
The most mature AI security programs deploy pre-deployment model security AND runtime data governance together. Protect AI scans models before they reach production — ensuring no backdoors, no malicious payloads, no unsafe serialization. Arbitex governs data at runtime — ensuring no regulated data leaks through the model boundary unprotected. Together they cover the full AI security stack: the model is safe AND the data flowing through it is governed. This is complementary architecture, not competitive positioning.
Related Resources
See Arbitex Gateway in action
Runtime data governance for regulated industries — DLP tuned for AI prompts, compliance bundles with inline enforcement, published accuracy metrics, and a governance data plane that stays inside your perimeter. Protect AI secures the model; Arbitex secures the data.