In a nutshell: Centralized AI gateways create a single point of failure that puts all API keys and integrated models at risk if the gateway infrastructure is compromised.
AI gateways consolidate API keys and access control for multiple LLM providers at a single central location. This centralization creates a significant security risk if the gateway infrastructure is compromised.
An AI gateway acts as an intermediary layer between enterprise applications and external AI models from various providers (OpenAI, Anthropic, Google, Meta, and others). It collects and manages all required API keys, authentication credentials, and access control policies centrally in one place.
The risk profile of such an architecture is substantial: if an attacker manages to compromise the gateway, they potentially gain access to all stored API keys and can thus make requests across all integrated models. This leads to uncontrolled API usage, data leaks through prompt injection, and potentially significant costs from model abuse. At the same time, sensitive inputs (training data, business information, user data) are transmitted through the gateway and cached there.
For CISOs, the question arises as to how gateway infrastructure can be hardened: network access segmentation, encryption of secrets in transit and at rest, implementation of least-privilege principles, audit logging of all gateway activities, and regular security testing are necessary measures. This is complemented by the selection of a trustworthy gateway provider with transparent security documentation.
The central challenge is that gateways promise flexibility and manageability while simultaneously presenting a concentrated attack surface. A segmented architecture with multiple smaller gateways per application domain or per provider can distribute the risk, but increases complexity.
Source: www.golem.de · Published July 7, 2026
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