MCP gateway identity controls now span model, tool, and agent traffic

At a glance

What this is: This comparison says MCP gateway choice now hinges on identity depth, guardrails, and observability across AI traffic, not just tool access.

Why it matters: It matters because IAM, PAM, and NHI teams need one control plane that can govern agents, tool calls, and user-bound access without losing auditability.

By the numbers:

• TrueFoundry says its gateway handles 350+ RPS on just 1 vCPU.
• TrueFoundry says its catalogue includes 120+ managed MCPs.

👉 Read TruFoundry's comparison of TrueFoundry and MintMCP for MCP governance

Context

MCP gateways are becoming identity control points for AI systems, because they sit between agents and the tools, data sources, and workflows those agents can invoke. Once that boundary exists, the governance question shifts from simple connectivity to who or what is allowed to act, under which token, and with what level of auditability. In this comparison, the primary issue is MCP gateway identity governance for AI agents and the non-human identities that support them.

The article contrasts two gateway approaches, but the deeper problem is familiar: enterprise teams still struggle to govern runtime authorization when requests can be mediated by agents, models, and user tokens at the same time. That makes observability, guardrails, and delegation controls matter more than raw convenience. For teams building AI platforms, the relevant question is whether the gateway becomes a real governance boundary or just a routing layer with authentication attached.

Key questions

Q: How should security teams govern managed MCP access for AI clients?

A: Security teams should treat managed MCP as a federated resource server and issue identity-bound tokens for each delegated task. That means no shared service-account secrets, narrow role scopes, and audit logs that can tie the human requester to the agent session and the resulting data access. Use the token as the control boundary, not the client config.

Q: Why do MCP gateways need more than simple allow or block rules?

A: Because AI traffic often needs contextual enforcement, not just binary filtering. Some requests should be denied, some rewritten, and some allowed only with tighter scope or extra logging. A gateway with validate and mutate modes can enforce policy at runtime instead of leaving governance to downstream tools.

Q: What do IAM teams get wrong about observability in AI gateways?

A: They often stop at request counts, tool counts, or flag counts. Those metrics do not show which identity path, policy decision, or routed tool call created the risk. For identity governance, the useful evidence is per-request trace data linked to the agent, user, or machine token that triggered it.

Q: What is the difference between acting as an agent and on behalf of a user?

A: Acting as an agent means the gateway treats the AI system as the operative identity for the request. On behalf of a user preserves the upstream user token, so the resulting action remains tied to the human context. That distinction matters when the same gateway must handle shared automation and user-scoped data access.

Technical breakdown

How MCP gateway identity works across agents and user tokens

An MCP gateway is the policy and mediation layer between an AI system and the tools it can call. In this model, identity can be bound at multiple layers: the gateway can authenticate the agent, the agent can act on behalf of a user, and machine-to-machine access can be handled separately. That distinction matters because a single bearer token does not capture the different accountability paths created by shared agents, end-user delegation, and service-to-service access. The more flexible the model, the more important it is to know which identity is actually authorizing each call.

Practical implication: teams should map each AI request to the identity that authorized it, not just the system that routed it.

Why guardrail architecture matters in MCP gateways

Guardrails in an MCP gateway do more than block obvious bad requests. A policy-as-code layer such as OPA can evaluate requests consistently, while additional classifiers can inspect content for sensitive data, risky commands, or policy violations. The distinction between validate mode and mutate mode is important: validate only approves or denies, while mutate can rewrite a request or response before it reaches the tool or user. That moves the gateway from a passive checkpoint to an enforcement surface with real governance consequences.

Practical implication: choose a gateway that can prove whether it blocked, rewrote, or passed each action.

What observability must show in AI tool routing

Basic counts are not enough once AI systems touch production tools. A useful gateway should surface latency percentiles, error categories, trace spans, routing decisions, cache behaviour, and cost by model, agent, or MCP usage. That level of telemetry is what lets security, platform, and IAM teams answer who called what, through which path, and where policy or performance broke down. Without that depth, investigations collapse into aggregate dashboards that hide the real access path.

Practical implication: require per-request traces and identity-linked logs before you put MCP traffic into production.

NHI Mgmt Group analysis

MCP gateways are now identity governance boundaries, not just AI infrastructure. Once an agent can invoke external tools, the gateway becomes the control point where authorization, delegation, and auditability have to line up. That makes the design problem closer to IAM and NHI governance than to ordinary API routing. Practitioners should treat gateway selection as a governance decision, not a developer convenience decision.

Two-layer auth is the more defensible pattern when AI systems act across user and machine contexts. A gateway that can authenticate the agent and separately preserve the end user’s upstream token creates a clearer chain of accountability than a single act-as-agent model. This matters because AI systems often need to touch both shared services and user-scoped data. The operational conclusion is simple: delegate only as narrowly as the business flow requires.

Policy-as-code and rewrite controls change the enforcement model for AI traffic. A gateway that can validate and mutate requests is not just filtering risk, it is actively shaping runtime behaviour. That is materially different from middleware that only logs or blocks after the fact. For identity teams, the implication is that governance must extend to the logic that transforms requests, not only to the credentials that initiate them.

Observability depth is becoming the difference between governable and ungovernable AI access. Aggregate counts tell you volume, but they do not explain which identity path, tool call, or policy decision produced a risky outcome. The named concept here is runtime governance gap: the blind spot created when AI access is mediated by multiple identities but only summarized in coarse metrics. Teams need traceable identity-level evidence before they can certify control.

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.
• In the same report, 80% of organisations say their AI agents have already performed actions beyond their intended scope, including unauthorised system access, sensitive data sharing, and revealing access credentials.
• For a broader control model, see OWASP Agentic Applications Top 10, which helps teams translate agent behaviour into governance requirements.

What this signals

With only 52% of companies able to track and audit the data their AI agents access, the practical gap is no longer theoretical. Security teams need identity-linked traces, not just gateway summaries, if they want to investigate policy violations or prove compliance after an incident.

Runtime governance gap: AI gateways are becoming the place where authorization, observability, and delegation either align or fail together. If the gateway cannot show who acted, what was touched, and whether the request was rewritten, the control plane is only partially governing the traffic.

For teams aligning AI gateway design with external guidance, the NIST AI 600-1 Generative AI Profile is a useful reference point for governance, while the OWASP Top 10 for Agentic Applications 2026 captures the runtime risks that gateway controls must absorb.

For practitioners

• Map every AI gateway request to an accountable identity Separate gateway authentication, end-user delegation, and machine-to-machine access so you can tell which identity authorized each tool call and which one inherited the resulting risk.
• Require per-request traces before production rollout Insist on latency percentiles, error breakdowns, routing decisions, and trace spans that can be tied back to the calling agent or user context, not just aggregate request counts.
• Test whether guardrails can rewrite as well as block Validate that the gateway can inspect, deny, and mutate requests or responses, because some risky AI interactions need transformation rather than simple rejection.
• Classify MCP servers by access sensitivity Tag tools that touch production systems, regulated data, or privileged workflows so the gateway can apply stronger policy and tighter delegation boundaries to high-risk connectors.

Key takeaways

• MCP gateways are now part of identity governance because they decide which agent, user, or service context can reach operational tools.
• Aggregate dashboards are not enough for AI access control, because teams need per-request evidence, delegation clarity, and policy decisions tied to identity.
• The strongest governance models combine layered authentication, rewrite-capable guardrails, and traceable runtime telemetry.

Key terms

• MCP Gateway: The control layer that relays assistant intent to tools and data sources through the Model Context Protocol. In practice, it becomes a policy boundary, not just a transport layer. If it trusts model output too early, it can turn unverified reasoning into real-world execution or disclosure.
• On-Behalf-Of Authorization: On-behalf-of authorization is a decision pattern that evaluates both the calling service and the original user whose action triggered the request. It prevents delegated workflows from exceeding user intent by enforcing policy on the combined identity context instead of trusting the service alone.
• Policy as Code: Policy as code stores authorization logic in version control and evaluates it through testable, reviewable rules. For agent governance, it makes runtime decisions reproducible and measurable, which is critical when actions can be triggered by untrusted content and executed at machine speed.
• Agentic Runtime Governance Gap: The distance between approving an AI agent as a project artefact and controlling its actual production behaviour. This gap appears when static approvals, access reviews, or policy documents do not keep pace with the agent’s live tool use and changing operational context.

What's in the full article

TruFoundry's full comparison covers the operational detail this post intentionally leaves for the source:

• Side-by-side deployment details for SaaS, VPC, on-prem, and hybrid operating models.
• The full identity feature matrix covering SSO, SCIM, RBAC, agent identities, and on-behalf-of access.
• Operational examples of guardrail, tracing, and SDK integration patterns for production teams.
• The complete feature comparison table for teams evaluating gateway fit against implementation requirements.

👉 TruFoundry's full comparison lays out deployment, identity, observability, and guardrail differences in detail.

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 building or maturing an identity programme that now includes AI systems, it is worth exploring.