AI Gateways as Security Perimeters: 7 Key Dimensions for Vendor Evaluation

Most teams adopt an AI gateway for mundane reasons: a unified API key vault, a single switchboard to swap OpenAI for Anthropic, and a dashboard that explains the monthly bill. That's a reasonable starting point. The problem is that the gateway quietly becomes the only component in your organization that sees every prompt, every completion, and every tool call. If you originally chose it as a convenient proxy, your most sensitive traffic is now guarded by a "convenient proxy."

This article distills NeuralTrust's Enterprise AI Gateway Selection Guide. The goal isn't a sprawling feature comparison matrix — it's a checklist worth actually running in practice, evaluating the gateway as a security perimeter.

First, Clarify Which Kind of Gateway You Actually Need

The word "gateway" now covers three distinct roles. Conflating them leads to fundamental selection errors.

• LLM / AI Gateway: Sits between your application and model providers, handling routing, authentication, rate limiting, logging, and inspection of prompts and responses.
• MCP Gateway: Governs traffic based on the Model Context Protocol — that is, calls an agent makes to tools and data sources.
• Agent Gateway: Tracks the full lifecycle of an autonomous agent session, spanning multiple model and tool calls.

The distinction matters because they protect different things. An AI gateway controls what goes into and out of the model, while an MCP gateway controls what the model is allowed to do after it decides to act. If your agents call tools in production, you almost certainly need both.

Model Context Protocol (MCP) is an open standard proposed by Anthropic in late 2024, designed to normalize how AI models interact with external tools and data sources. Think of it as the "USB standard" for AI — before MCP, every agent framework invented its own way of calling external tools, fragmenting the ecosystem. MCP defines the message format, capability negotiation, and permission boundaries between Servers (tool/data source providers) and Clients (model/agent callers). As Claude, Cursor, and various agent frameworks adopt MCP, MCP-based tool calls are becoming the de facto standard. This creates a real security need: an agent operating over MCP can read and write files, execute code, and call external APIs. Without visibility and control over this layer of traffic, a traditional AI gateway that only inspects "what the model said" covers only half the risk surface.

1. Inspect Before Prompts Reach the Model

Prompt injection ranks first on the OWASP Top 10 for LLM Applications (LLM01). OWASP splits it into two types: direct injection, where user input itself manipulates the model; and indirect injection, where the payload is hidden in external content the model reads — webpages, PDFs, or tool return values.

Indirect injection is what should actually drive your gateway selection. A chat interface with a system prompt is relatively easy to reason about. But an agent that browses the web, reads tickets, and calls APIs is pulling untrusted text into its context at every step. The gateway must inspect that content too — not just the user's first message.

Key inspection criteria:

• Inline inspection of inputs before requests are forwarded to the provider
• Output filtering for PII and secrets on the return path
• Coverage beyond user prompts — tool results and retrieved content must also be inspected
• Detection capability that reasons across an entire session, since multi-step attacks often look harmless message by message

Don't trust vendor detection claims — test them directly. Attach a small test probe to the gateway's OpenAI-compatible endpoint and observe what happens. The approach is simple: prepare benign, direct-injection, and indirect-injection probes; route all of them through the gateway rather than directly to the provider; observe which are blocked and print the results.

A real test should use hundreds of actual attack strings alongside hundreds of normal prompts drawn from your own logs. Track two numbers: how many attacks pass through, and how many legitimate requests get blocked. A gateway with a high false positive rate will be circumvented by your own developers within a month.

OWASP LLM Top 10 is a list of the ten most critical security risks for LLM applications, published by the Open Web Application Security Project (OWASP), first released in 2023 and continuously updated. It follows the same framework as the widely adopted OWASP Web Application Top 10, helping developers and security teams systematically evaluate the attack surface of LLM applications. Beyond the top-ranked prompt injection (LLM01), the list covers insecure output handling (LLM02), training data poisoning (LLM03), model denial of service (LLM04), supply chain vulnerabilities (LLM05), and more. Understanding this list helps you assess a vendor's security coverage during gateway evaluation — whether a gateway claiming "comprehensive AI security" genuinely addresses most LLM Top 10 vectors, or only does keyword filtering on prompts, is an important way to gauge product depth.

2. Logs You Can Actually Investigate

When something goes wrong, you need to reconstruct exactly what the model saw and said. At minimum, every interaction should produce a structured record containing: trace_id, user_id, timestamp, model name, input/output token counts, latency, policy violations (e.g., pii_detected:email), and a list of tool calls.

The trace ID is critical. In an agent application, a single user request can fan out into dozens of model and tool calls. Without a shared ID, you can't reassemble them. Also confirm that logs can stream to your SIEM and that storage is tamper-resistant. If you fall under the EU AI Act's high-risk category, automated event logging is an explicit system obligation — for some teams, this is non-negotiable.

SIEM (Security Information and Event Management) is the core infrastructure of enterprise security operations, used to centrally collect, correlate, and store security logs from disparate systems. Splunk, Microsoft Sentinel, and IBM QRadar are common commercial options; Elastic SIEM is a popular open-source alternative. The core value of feeding AI gateway logs into a SIEM is being able to correlate AI traffic threats with other security events — anomalous logins, privilege changes — in a single view, revealing attack chains that neither data source alone would surface. For example, if a prompt injection attack successfully exfiltrates credentials, the subsequent use of those credentials will appear in identity logs; only by correlating both can you reconstruct the full attack path. For teams with an established SOC, whether the gateway supports standard Syslog, HTTP Event Collector, or native connectors is a practical factor in assessing operational cost.

3. Routing That Saves Money Without Creating Chaos

Multi-model routing is where a gateway "earns back its cost." Capabilities worth testing:

• Provider-agnostic routing: You should be able to move a workload between providers — or to a self-hosted model — by changing config, not code.
• Failover: In a pre-production environment, literally "kill" one provider and see what happens. Does the gateway switch automatically? How long does it take?
• Cost-aware routing: Send simple classification and extraction tasks to cheaper models; reserve expensive ones for hard reasoning.
• Caching: Prompt caching reuses stable prefixes like long system prompts; semantic caching goes further, skipping the model call entirely when a new query is close enough to one already answered. Note the tradeoff: semantic caching can return an incorrect answer to a "subtly different" question.

4. Where Does the Data Plane Run?

This is usually the first hard filter — resolve it early. There are three options:

• SaaS: The vendor hosts everything. Fastest to onboard, but every prompt traverses their infrastructure.
• Self-hosted: Everything runs in your own environment. More operational overhead, but prompts never leave your network. In financial services, healthcare, and the public sector, this is often the only acceptable option.
• Hybrid: The vendor runs the control plane (configuration, dashboards, policy management), while the data plane that actually handles traffic runs in your infrastructure.

In hybrid mode, always ask exactly what crosses the boundary: only metadata? Sampled prompts for analytics? That answer determines whether your Data Protection Officer will sign off.

5. MCP and Agent Control

If agents call tools, the gateway needs to understand MCP traffic, not just pass it through. Points to verify:

• Per-tool allow/deny lists, scoped by user or by agent
• Least-privilege credentials: the agent receives only the token needed for one specific action, not your admin key
• Session-level monitoring that can flag anomalous tool call sequences
• A "kill switch" that can stop a misbehaving agent within seconds, not after someone files a ticket

The broader agent threat model — including memory poisoning and privilege escalation — is a topic worth researching separately.

6. Demand Compliance Evidence, Not Compliance Talk

Ask for documentation, not slide decks. A SOC 2 Type II report or ISO 27001 certificate, a Data Processing Agreement (DPA), and a clear explanation of how the product supports your own compliance obligations. For the EU AI Act that means logging, technical documentation, and a human oversight interface. For GDPR and UK GDPR it means data minimization and the ability to keep personal data within a specific region.

EU AI Act entered into force in 2024 and is the world's first comprehensive legislation systematically regulating artificial intelligence. The Act uses a risk-based tiered regulatory framework: "unacceptable risk" AI is prohibited outright; "high-risk" AI (e.g., medical diagnosis, credit assessment, critical infrastructure management) must meet mandatory compliance requirements including technical documentation, data governance, transparency obligations, human oversight mechanisms, and event logging; other general-purpose AI systems (GPAI) face relatively lighter transparency requirements. The most direct impact on enterprise AI gateway selection is the logging obligation for high-risk categories — systems must automatically record event logs sufficient to trace the decision-making process, and those logs must be retrievable by regulators within a specified retention period. If your use case falls into the high-risk category, the gateway's tamper-resistant logging capability and data residency options move from "best practice" to legal compliance requirement.

7. Plan Your Exit on Day One

Every gateway accumulates routing rules, policies, and integrations that are painful to rebuild. Before you commit, ask:

• Can policies and routing configs be exported in a usable format?
• Is the client interface a standard interface (e.g., OpenAI-compatible API) so that application code doesn't need to change when you switch?
• Is there a documented migration path?
• Are any core components open source?

A Suggested Evaluation Sequence

If you only have two weeks, work in this order: classify use cases by data sensitivity; map the applicable regulations for each category; eliminate vendors that can't meet your deployment model requirements; then run injection test probes, review real log output, "break" a provider to test failover, and route an agent through MCP controls. Finally, model total cost — weigh token savings from routing and caching against the engineering time required to operate the system.

The core logic never changes: your AI gateway is a security perimeter, and it should be evaluated to the same standard as any security perimeter. Regardless of which vendor you ultimately choose (including NeuralTrust's TrustGate, the source of this article's material), run the same tests against all of them — that's the entire point of this checklist.