Topic starter
24/06/2026 8:05 pm
TL;DR: MCP gateways are emerging as the missing control layer for enterprise AI, centralising authentication, routing, policy enforcement, and observability as AI agents fan out across more servers, according to Kong. The governance problem is not protocol support alone, but the collapse of manageable trust boundaries once access, routing, and audit move point to point.
Editorial analysis by NHI Mgmt Group, based on content published by Kong: “What is an MCP Gateway? Key to Secure Enterprise AI at Scale”.
By the numbers:
- Organizations using AI in at least one business function jumped from 55% to 78% in just one year, according to Stanford HAI AI Index Report data cited by Kong.
- Generative AI usage specifically rose from 33% in 2023 to 71% in 2024, according to Stanford HAI AI Index Report data cited by Kong.
Key questions
Q: What breaks when MCP servers are added without a gateway?
A: Authentication, authorization, and audit responsibilities fragment across servers, which makes policy inconsistent and revocation slow.
Q: Why do MCP gateways matter to identity governance for AI agents?
A: MCP gateways sit at the point where agents request access to tools, data, and downstream APIs, so they become the operational boundary for authorization and audit.
Q: How can organisations tell whether MCP access is actually being governed?
A: A governed MCP deployment can answer who requested access, what scope was granted, when the token expires, and which tool calls were made under that token.
Practitioner guidance
- Define the gateway as the enforcement point Place authentication, authorization and policy checks at a single MCP gateway instead of duplicating them across individual servers.
- Inventory every direct MCP path Map all AI clients, servers and credential paths so you can see where point-to-point access still bypasses centralized governance.
- Require session-level tracing Ensure logs, traces and correlation IDs follow the full AI interaction so multi-step tool use can be reconstructed later.
Bottom line: MCP gateways are emerging because direct client-to-server access does not scale cleanly once AI agents need to reach many tools under consistent controls.
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25/06/2026 5:14 am
Centralising MCP access is becoming an identity architecture decision, not a convenience choice. Once AI agents consume tools through many servers, the control plane must define who can reach what, under which policy and with what evidence. That pushes gateway design into the same governance conversation as workload identity, access review and privileged access. Practitioners should treat the gateway as the policy boundary for agentic access.
A few things that frame the scale:
- 24,008 unique secrets were exposed in MCP configuration files in 2025 alone, the protocol's first year of widespread adoption, according to the State of Secrets Sprawl 2026.
- Enterprise AI use rose from 55% of organisations in 2023 to 88% in 2025, according to McKinsey’s Global Surveys on the State of AI.
A question worth separating out:
Q: What happens when AI agents fan out across too many MCP servers?
A: The operational result is configuration sprawl, fragmented audit evidence and a wider attack surface. The governance result is harder recertification, weaker accountability and more difficulty proving which tool access was actually used in a session.
👉 Read our full editorial: MCP gateways are becoming the control layer for enterprise AI access