Securing the Adoption of AI Models: Why AI Gateway Is a Must for Enterprises

AI is penetrating every aspect of enterprise operations at unprecedented speed. However, behind the explosion of AI model usage lies a new wave of challenges: fragmented services, mounting security risks, and spiraling costs.
How can companies ride the wave of GenAI—securely and efficiently? The answer lies in a new category of infrastructure: the AI Gateway.

What is AI Gateway?

As early as 2023, Gartner predicted that by 2026, over 80% of enterprises would be using generative AI (GenAI) models. Yet, traditional API gateways—designed around RESTful architectures and static request-response models—are insufficient for the unique traits of AI services. To meet this gap, AI Gateway is developed as a purpose-built traffic management platform engineered specifically for AI-driven environments.

The AI Gateway acts as a centralized access point for large language model (LLM) services and other AI workloads. It enables intelligent routing, unified identity and access control, token-level quota enforcement, content moderation, and security auditing—all while supporting a wide range of AI providers and protocols.
It’s not just an upgraded API gateway—it’s a next-gen governance layer designed to handle the dynamic, token-driven, and high-volume nature of AI traffic.

Why Enterprises Need AI Gateway

APIs are the core connectors of the digital world—and now, they are also the engines behind real-time AI interactions. Every smart response, image generation, or decision inference from an AI model is powered by a web of API calls.

As organizations scale up their AI initiatives, they face several challenges:

• ‍Integration Complexity: Enterprises often rely on multiple AI providers, creating integration complexity.‍
• Rising Security Risks: Sensitive data leakage and prompt injection attacks are becoming more frequent—risks that traditional gateways are not equipped to handle.‍
• Uncontrolled Costs: Token-based billing models can lead to budget overruns without fine-grained control.

Traditional API gateways fall short in several key areas:

• ‍Measurement Method: Traditional gateways monitor and bill based on request volume, whereas AI resource consumption is measured by 'tokens'.‍
• Streaming Responses: AI applications often use streaming (rather than static) response formats for real-time interactions.‍
• Enhanced Compliance Needs: AI scenarios require stronger governance—fine-grained access control, data encryption, audit trails, and content filtering.

To meet these new demands, organizations need a new kind of gateway—an AI Gateway purpose-built for the future of intelligent workloads.

Paraview AI Gateway: Unified, Secure, and Cost-Aware AI Management

In response to this growing need, Paraview has developed an enterprise-grade AI Gateway, built on our expertise in API gateway and security. It empowers enterprises to unify and control their AI and API traffic across systems, models, and protocols—enabling:

• Seamless integration with LLMs and business systems
• Centralized model access management
• Comprehensive observability and security controls

Key Capabilities:

1. ‍Unified Access & Multi-Model Management: The AI Gateway aggregates AI services from multiple sources—including DeepSeek, OpenAI, and in-house models—supporting nearly all major AI providers. It abstracts away the differences between large language model (LLM) platforms and provides a unified API interface to developers, streamlining integration and reducing development complexity. Developers can flexibly switch between models as needed, while the gateway can also intelligently auto-route requests to the most suitable model based on business context.‍
2. Security & Compliance Governance Center: The AI gateway enforces compliance throughout the AI interaction lifecycle through access control, data encryption, and content moderation. For example, it supports input/output review for LLMs—validating prompts to prevent prompt injection attacks and blocking harmful or non-compliant content generation. - Integrated with Paraview IAM, the gateway allows Role-Based Access Control (RBAC) to restrict model access by department or role (e.g., only the design team can use image generation models). Advanced data masking protects sensitive data in prompts and responses to support privacy compliance. An extensive plugin ecosystem—including AI prompt templates, decorators, and prompt firewalls—enables developers to build more secure, governed, and consistent AI applications.‍
3. Intelligent Routing & Performance Optimization: It dynamically routes requests to the most cost-effective or performant model based on workload requirements. For simple tasks, lower-cost models can be prioritized to reduce usage costs. High-frequency query results (e.g., password reset procedures in customer service scenarios) can be cached using vector-based indexing for quick retrieval—avoiding repeated LLM calls and reducing consumption. Additionally, if a model fails or times out, the system automatically falls back to an alternative provider to ensure service continuity and resilience.‍
4. Token-Aware Plugins for Precision Governance: Unlike traditional API gateways that monitor requests per call, Paraview AI Gateway manages traffic at the token level—monitoring and limiting both input and output token usage. Rate-limiting rules can be enforced based on various dimensions such as URL parameters, HTTP headers, client IP addresses, or Consumer IDs. Enterprises can flexibly allocate token quotas per model or define request frequency limits by dimension to ensure optimal performance and prevent budget overruns.‍
5. Cost Control & Full Observability: In addition to token-level metering and throttling, the AI Gateway allows enterprises to set daily usage caps per user, department, or application—providing precise control over AI resource consumption. Its observability engine offers full visibility into where and how tokens are consumed: which application or user initiated the call, when it occurred, and how much was used. This end-to-end monitoring provides critical data for resource planning, optimization, and accountability. It also gives users the transparency to manage their own usage effectively, maximizing utilization and minimizing waste.

Conclusion

AI’s promise is vast—but so are its operational risks if left ungoverned. With Paraview AI Gateway, enterprises can regain control over their AI stack—bridging the gap between innovation and regulation, scale and security.
As GenAI becomes a core part of digital business, a robust AI Gateway is no longer optional—it’s essential. Contact Paraview to learn more about how AI Gateway can enhance your cybersecurity strategy.