MCP gateways are becoming the control layer for enterprise AI access

At a glance

What this is: This is Kong’s explanation of why MCP gateways are becoming the front door for enterprise AI infrastructure and how they centralise access, routing, and policy.

Why it matters: It matters because IAM and security teams need a governed control point for AI clients, MCP servers, and the identities that bind them together.

By the numbers:

• Organizations using AI in at least one business function jumped from 55% to 78% in just one year.
• Generative AI usage specifically rose from 33% in 2023 to 71% in 2024.

👉 Read Kong's explanation of MCP gateways for enterprise AI infrastructure

Context

MCP gateways are a governance answer to a simple scaling problem: once AI agents need access to many MCP servers, direct point-to-point connectivity becomes hard to secure, hard to observe, and hard to operate. The primary keyword here is MCP gateway, and the article’s core claim is that a gateway becomes the practical control plane when AI connectivity expands faster than identity and policy management can keep up.

For IAM and security teams, the issue is not whether MCP itself works. The issue is whether authentication, routing, and policy enforcement remain consistent when every new server creates another access path, another set of credentials, and another audit dependency. That is why gateway design starts to matter as soon as production AI moves beyond a small pilot footprint.

The article’s starting position is typical of enterprise AI expansion: useful in proof of concept, fragile at scale. That makes the governance question more important than the protocol debate itself.

Key questions

Q: How should security teams govern access to MCP servers used by AI agents?

A: Security teams should place MCP servers behind a gateway that enforces authentication, policy, and audit at one control point. That keeps access decisions consistent across tools and prevents every server from becoming a separate identity island. The goal is central mediation, not scattered per-server exceptions.

Q: Why do MCP environments create new identity governance problems at scale?

A: MCP environments multiply access paths as more agents and servers are added, which makes direct point-to-point governance brittle. Each new endpoint adds another authentication pattern, another routing decision, and another audit dependency. Without central control, identity policy fragments faster than operations can manage it.

Q: How can organisations tell whether their MCP access controls are working?

A: They should be able to reconstruct every agent request from authentication through routing to the backend server. If logs cannot show which tool was called, which policy applied, and how the session moved, the control is incomplete. Auditability is the clearest signal that governance is real.

Q: Who should own MCP gateway governance in an enterprise AI programme?

A: Ownership should sit with the teams responsible for identity, security architecture, and platform governance together, because MCP gateways span access control, routing, and observability. Treating the gateway as only a network component leaves policy gaps. Treating it as only an AI platform feature leaves accountability unclear.

Technical breakdown

MCP gateway architecture and trust boundaries

An MCP gateway sits between AI clients and MCP servers as a reverse proxy and policy layer. Instead of every client connecting directly to every server, the gateway presents one endpoint, then handles authentication, routing, and observability behind the scenes. That architecture reduces endpoint sprawl and creates a single place to apply identity controls. In practice, the gateway becomes the trust boundary for enterprise AI access, not the MCP server itself.

Practical implication: centralise access decisions at the gateway so AI clients do not bypass policy by reaching servers directly.

Authentication, OAuth 2.0, OIDC, and SSO for MCP traffic

The article places authentication at the centre of MCP gateway design because AI access fails quickly when each server has its own identity pattern. OAuth 2.0 handles token-based access, OIDC supports federated identity, and SAML or SSO integration lets the gateway fit enterprise identity architecture. The important shift is that the gateway does not just proxy traffic. It normalises identity enforcement before any tool call reaches backend services.

Practical implication: align MCP access with existing identity providers and policy engines rather than creating separate AI-specific authentication paths.

Routing, session affinity, and observability in AI tool access

MCP traffic is not static API traffic. Agents may request different tools in sequence, keep session state across multiple calls, and rely on the gateway to route requests to the right server while preserving context. That makes session affinity, load balancing, logging, and tracing part of the security model, not just the reliability model. A gateway that cannot preserve visibility across the full request chain cannot support audit, troubleshooting, or policy enforcement at production scale.

Practical implication: require full request tracing and audit logs for AI tool access so session-level behaviour stays visible across the entire chain.

NHI Mgmt Group analysis

MCP gateways are becoming the control point where AI identity policy either scales or fragments. The article shows that distributed MCP access creates the same operational pressure that once drove API gateway adoption, but with a harder identity problem because agents can touch many tools quickly. The field should treat the gateway as an identity enforcement layer, not a convenience proxy. Practitioners should expect MCP governance to converge on central policy, central audit, and central access mediation.

Ephemeral tool access expectation: The assumption that AI clients can be governed through scattered server-level checks was designed for small, stable deployments. That assumption fails when agents dynamically traverse many MCP servers because access paths, routing decisions, and audit points multiply faster than teams can review them. The implication is that identity programmes must rethink where authority lives in AI infrastructure, not just add more logging.

Session-aware routing is now part of the identity control surface. In agentic environments, routing is no longer a purely network concern because it determines which tools an agent can reach and whether context is preserved across calls. That makes least privilege, segregation of duties, and service accountability dependent on how the gateway routes and records traffic. Practitioners should evaluate routing logic as a governance control, not a performance feature.

The MCP gateway pattern validates a broader market shift toward control planes for agentic systems. As AI adoption expands, enterprises are unlikely to manage tool access through ad hoc server-by-server integrations. The more realistic path is centralised mediation with policy, audit, and identity plumbing built in. For security leaders, that means the buying question is moving from point solutions to control-plane architecture.

Identity visibility must extend from user authentication to agent-tool interaction. The article makes clear that knowing who signed in is not enough if teams cannot see which tools were called, in what order, and under which policy. That is the practical gap between human IAM and agentic access governance. Practitioners should expect audit requirements to shift toward request-level traceability for AI workloads.

From our research:

• Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation, according to AI Agents: The New Attack Surface report.
• Only 80% of organisations report their AI agents have already performed actions beyond their intended scope, including unauthorised system access, inappropriate sharing, and credential exposure.
• That visibility gap makes OWASP Agentic Applications Top 10 the right next step for teams trying to govern tool use, scope drift, and policy enforcement.

What this signals

Tool mediation is becoming the practical boundary for agentic governance. Once AI agents can fan out across multiple MCP servers, the security question shifts from whether an agent can authenticate to whether every downstream tool call remains observable and policy-bound. Teams that still rely on server-by-server access patterns will struggle to prove control, especially when request chains span multiple systems and owners.

Gateway-centric AI architecture will increasingly look like identity architecture. The organisations that get ahead will connect AI access to the same governance primitives they already use for IAM, logging, and policy enforcement. That means strong alignment with the NIST AI Risk Management Framework for governance and with OWASP Top 10 for Agentic Applications 2026 where tool misuse, identity abuse, and access scope are concerned.

Runtime visibility is the new control requirement. The difference between a pilot and a production-grade AI programme is often whether teams can answer basic questions about what an agent touched, when, and under which policy. If you cannot trace those movements, the environment is already outgrowing traditional IAM review cycles.

For practitioners

• Centralise MCP access behind one governed endpoint Require AI clients to reach MCP servers only through a gateway that enforces authentication, routing, and policy in one place. This reduces endpoint sprawl and gives security teams a single control point for access review and audit.
• Bind MCP traffic to enterprise identity providers Connect gateway authentication to your existing SSO stack using OAuth 2.0, OIDC, or SAML so AI access inherits the same identity governance standards as other workloads. Avoid creating separate AI-only login paths that fragment policy.
• Treat session affinity as a governance requirement Preserve conversation context across multi-step tool calls, but pair that with traceable logs and correlation IDs so the full request chain remains auditable. Without that visibility, session state becomes a blind spot rather than a control.
• Review routing rules as access policy Map which MCP tools each route can reach, then validate that routing tables, health checks, and failover behaviour do not widen access beyond intended scope. Route changes should go through the same change control as privilege changes.
• Measure audit completeness across AI tool calls Test whether your logs can reconstruct who accessed which MCP server, which tool was called, and how the request moved across the gateway. If you cannot answer that, you do not yet have production-grade AI observability.

Key takeaways

• MCP gateways are emerging because direct server-to-server AI access does not scale cleanly across authentication, routing, and audit.
• The security value of the gateway is central enforcement, but its real governance value is traceability across the full agent-to-tool request chain.
• IAM teams should treat MCP gateway adoption as an architecture decision about where identity authority lives in agentic systems.

Key terms

• MCP Gateway: An MCP gateway is a control layer that sits between AI clients and MCP servers to manage access, routing, and policy enforcement. It reduces endpoint sprawl and gives enterprises one place to apply identity and security controls across many tool connections.
• Session Affinity: Session affinity is the practice of keeping related requests on the same backend path so context is preserved across multi-step interactions. In agentic environments, it matters because tool calls often depend on prior state, but that state must still remain visible and auditable.
• Policy Enforcement Point: A policy enforcement point is the component that applies access rules before a request reaches a protected resource. In AI infrastructure, the gateway often becomes that point, deciding whether an agent can reach a tool and under what identity and policy context.
• Tool Routing: Tool routing is the process of directing an AI request to the correct backend service based on the capability it needs. In MCP environments, routing is not only an architecture concern because it also shapes which identities, logs, and controls govern the request path.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or programme maturity, it is worth exploring.

This post draws on content published by Kong: What is a MCP Gateway? The Missing Piece for Enterprise AI Infrastructure. Read the original.