Topic starter
24/06/2026 7:16 pm
TL;DR: As organizations move from LLM experiments to production AI, Kong says fragmented model, MCP, and API integrations create governance, observability, and security gaps that edge inspection and gateway control are meant to close. The core issue is not model choice but control-plane sprawl, where policy, routing, and data protection are no longer centrally enforceable.
Editorial analysis by NHI Mgmt Group, based on content published by Kong: “Secure, Scalable AI Infrastructure with Kong + Akamai: A Technical Intro”.
Key questions
Q: How should security teams govern AI gateways in production environments?
A: Security teams should govern AI gateways like shared control planes, not convenience proxies.
Q: Why do fragmented LLM and MCP integrations increase security risk?
A: Because each integration can introduce a separate trust boundary, teams lose the ability to enforce one consistent rule set for routing, observability, and data handling.
Q: What are the signs that an AI gateway is not operating effectively?
A: Common warning signs include inconsistent API behavior across providers, weak audit trails, poor visibility into who called which model, and rising costs from duplicate or repeated prompts.
Practitioner guidance
- Define a single AI control plane Standardise how authentication, routing, logging, and policy enforcement are applied across LLM providers, MCP servers, and REST APIs so governance does not fragment by integration.
- Inventory MCP tools and access paths List every tool exposed through MCP, the identities that can call it, and the policies that should restrict each call path before production rollout.
- Enforce edge inspection for prompts and outputs Apply request and response filtering at the boundary to catch prompt injection, toxic output, and data exfiltration before they reach internal services or users.
Bottom line: Production AI becomes harder to govern once model providers, MCP tools, and APIs are stitched together without a common control plane.
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25/06/2026 4:29 am
AI control-plane sprawl is now the core governance problem for production AI: once organizations combine multiple model providers, MCP servers, and API backends, security is no longer enforced by any single control point. Policy, logging, and routing become distributed across components that were not designed to behave like one identity plane. The practitioner conclusion is that AI governance now depends on making the control plane explicit.
A few things that frame the scale:
- AI-related credential leaks surged 81.5% year-over-year in 2025, with the surrounding AI infrastructure leaking 5x faster than core LLM providers, according to the State of Secrets Sprawl 2026.
A question worth separating out:
Q: What should teams do when MCP tools are added without access scoping?
A: Pause deployment until each tool has an explicit business purpose, a minimum permission set, and a named approver. Broad or default access should be treated as a governance defect, not a convenience. Without scoping, the system can invoke functions the business never intended, which is exactly how delegated access becomes abuse.
👉 Read our full editorial: Kong and Akamai frame the AI gateway control gap for agentic systems