AI Security Gateway (RayToken)

AI Security Gateway (RayToken)

Clearer, more efficient and more secure AI usage for enterprises

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Current Location： Home > Products > AI Security > AI Security Gateway (RayToken)

Product Overview

RayToken is built for enterprise-scale AI adoption. Deployed between enterprise applications and LLM services, it serves as the unified secure access point, and the master control point for all AI traffic within the enterprise, helping organizations unlock AI productivity. To address the complexity of multi-model integration, the solution provides a unified gateway for accessing both public models and privately deployed models, with support for hybrid deployment. To mitigate security risks arising from AI usage, it delivers end-to-end security controls, including Data Loss Prevention, prompt injection protection, and content compliance detection, comprehensively safeguarding data security. To meet audit requirements, it offers end-to-end log auditing and multi-dimensional visualization capabilities.

Key Features

Clearer AI Usage
Gain full visibility into the cost-effectiveness of AI usage across departments and individuals: which models are used, by whom, and how well they perform. Usage across the entire company, every department, every group, and every user is visible on a single dashboard. Multi-dimensional usage statistics and analytics by department, user, model, and application put an end to the "AI compute black box."
More Efficient AI Usage
Generate prompt analysis reports by department and user, with effectiveness scoring, turning individual experience into enterprise assets. Intelligent model routing automatically selects the right model based on task type and sensitivity level, with automatic failover to a backup model if the primary model becomes unavailable. Semantic caching reuses results for identical or similar queries, reducing token consumption and improving response times.
More Secure AI Usage
Provide unified access and hybrid scheduling for mainstream LLMs and privately deployed models. A unified access channel for LLMs including DeepSeek, Tongyi, Zhipu, and Kimi, and is compatible with mainstream LLM API protocols. Proprietary models can be deployed on-premises, keeping core data within the organization's boundary, with support for hybrid public-private scheduling. A built-in four-layer AI security guardrail framework protects AI usage comprehensively, from ingress to egress and from data to behavior.

Application Value

Transparent · Fully Visible

Gain end-to-end visibility across models, usage, and audits, with real-time monitoring and analysis by department, team, and user. Eliminate the AI computing power black box with full traceability of token consumption.

Secure · End-to-End Protection

Build comprehensive AI security across input, content, and behavior. AI-DLP filters sensitive data in real time, while prompt injection protection defends against AI-specific attacks. Prevent sensitive data leakage in outputs, support multimodal attachment scanning, and enable end-to-end audit trails.

Cost-Efficient · Reduced Consumption

Optimize model selection based on task type through intelligent model routing. Semantic caching eliminates redundant calls by identifying duplicate or similar queries. Automatically switch to backup models to ensure service continuity, while improving AI cost efficiency and ROI.

Compliant · Regulatory Ready

Meet relevant national and industry regulatory requirements with comprehensive security and auditing capabilities. Help enterprises standardize AI usage and reduce compliance risks.

Security Guardrail

Access Control Layer: Securing the Entry Point

Unified identity authentication with enterprise SSO integration. Fine-grained access control with tiered authorization by department, role, and user. Secure centralized API key management, keeping keys out of business applications to prevent leakage and misuse. Multiple access safeguards, including IP whitelist, access time restrictions, and rate limiting.

Content Security Layer: Safeguarding Content

Real-time detection and protection against AI-specific threats, including prompt injection, and jailbreak attacks. Multi-turn conversation risk scoring with automatic blocking when security rules are triggered. Comprehensive content compliance checks covering political, violent, and sensitive content. Multimodal attachment scanning and bidirectional input and output controls to ensure AI interactions comply with enterprise policies and regulatory requirements.

Data Protection Layer: Protecting Sensitive Data

Data loss prevention (DLP) as the final security checkpoint before LLM requests are sent. Real-time detection and blocking of sensitive data transfers, including ID numbers, phone numbers, bank card numbers, source code, API keys, and internal meeting minutes. Supports tiered response policies, including blocking and alerts, to prevent sensitive enterprise data from leaking through AI tools.

Audit & Traceability Layer: Ensuring Full Traceability

End-to-end activity auditing, recording who accessed which model, when it was accessed, input and output content, triggered security rules, and token consumption. Real-time alerts for anomalous activities to identify risky operations promptly. Long-term log retention to meet enterprise audit requirements. Statistical report generation to support compliance reviews.

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Typical Deployment

SaaS Deployment

Provide one-stop cloud-based access with no need for in-house computing infrastructure. Get started in minutes and access 300+ mainstream LLMs through a unified API. Supports elastic scaling and continuous integration of the latest models.

Hybrid Deployment

Support leading domestic models such as DeepSeek, Qwen, and GLM, with one-click model switching to meet diverse business needs. Keeps 100% of inference processing and data within the enterprise intranet, meeting MLPS Level 3 and industry compliance requirements.

Private Deployment

Supports a hybrid “SaaS + private deployment” model, allowing enterprises to choose based on data sensitivity. Sensitive business data is processed by privately deployed models and stays within the enterprise intranet, while general office tasks use SaaS services to benefit from continuously updated model capabilities.

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