IBM ContextForge alternatives expose the MCP governance gap

At a glance

What this is: This guide compares IBM ContextForge alternatives and finds that the core issue is not gateway features alone, but whether organisations need unified governance, enterprise identity controls, and managed operations around MCP use.

Why it matters: IAM and security teams need to treat MCP gateways as part of a broader identity and governance plane, because tool access, auditability, and deployment model all shape risk once AI agents scale beyond a few internal users.

Context

MCP gateways sit at the point where agents, tools, and enterprise systems meet, so their security posture is really an identity governance problem as much as an integration problem. When access is spread across OAuth, JWT, custom headers, and tool-level controls, teams need to know which layer owns authorization and which layer only forwards requests.

TruFoundry’s article is about a familiar inflection point in AI platform adoption: lightweight, self-hosted infrastructure can work early, but governance expectations change as deployments scale. At that point, identity controls, observability, compliance posture, and operating model become the deciding factors rather than transport support alone.

The article’s central claim is that many teams are not simply choosing between gateways. They are choosing between a self-managed MCP proxy and a broader control plane that can govern AI traffic, tool access, and deployment responsibilities together.

Key questions

Q: What breaks when an MCP gateway is not the only path to a tool?

A: Policy becomes partial instead of authoritative. If developers can connect through other AI clients or direct MCP configurations, gateway controls only cover one route while tool exposure can continue elsewhere. That leaves audit logs, filters, and access scoping blind to the full estate, which is why pathway governance matters as much as tool governance.

Q: When should organisations prioritise a managed MCP platform over self-hosting?

A: Prioritise managed deployment when patching, uptime, compliance evidence, and operational support are part of the security requirement, not just a convenience. Self-hosting can work for teams with strong internal DevOps capacity, but it shifts responsibility for reliability and governance into the organisation. The right choice is the one that matches your assurance obligations.

Q: What are the signs that an MCP gateway is becoming too complex to govern?

A: Common signs include separate controls for model traffic and tool traffic, unclear ownership of access policy, growing manual monitoring, and no consistent audit evidence across teams. If platform administrators cannot answer who approved access, where the logs live, and which system enforces policy, the gateway has outgrown its original operating model.

Q: How do enterprise RBAC and SSO change MCP governance?

A: They move MCP access from ad hoc configuration toward enterprise identity control. That matters when many teams, servers, and agents need access because the gateway must enforce who is authorised, not just whether a request is syntactically valid. Without those controls, access management becomes fragmented and difficult to recertify.

Technical breakdown

Why MCP gateway design becomes an identity problem

An MCP gateway mediates how agents reach tools, but the security boundary is not just transport. Once the gateway supports OAuth, JWT Bearer tokens, Basic Auth, custom headers, and role-based access, it becomes an enforcement point for non-human identity, not merely a routing layer. That matters because every registered server, every tool call, and every discovery path creates an authorization decision that must be logged and governed. In practice, the gateway is where inventory, access control, and auditability intersect, which is why governance gaps show up first as operational sprawl.

Practical implication: Treat the MCP gateway as an identity control point and map which permissions are enforced there versus in downstream tools.

Why unified AI and MCP governance changes the operating model

The article highlights a split many teams eventually face: one gateway for model traffic and another for MCP tools. That separation can work technically, but it fragments policy, observability, and cost control across two different planes. A unified control plane reduces duplication by letting teams apply shared authentication, access control, and logging across LLM traffic and MCP tool use. The underlying issue is governance coherence. If tool access and model access are managed differently, security and platform teams lose a single view of who can do what, where, and under which operating conditions.

Practical implication: Decide whether your programme needs one governance plane for both model and tool traffic before standardising on separate gateways.

Why deployment model and support commitments matter for MCP governance

ContextForge is described as self-hosted and operationally demanding, which makes deployment model part of the security decision. A managed SaaS, VPC, on-prem, or air-gapped option changes who owns patching, uptime, and evidence collection, all of which matter when the gateway is part of regulated operations. Compliance is not just a checklist here. If the organisation must demonstrate auditability, data residency, or defined support response, the platform choice has governance consequences that go beyond feature comparison.

Practical implication: Align gateway selection with your deployment and assurance model, not just with protocol support or feature count.

Breaches seen in the wild

• LiteLLM MCP auth bypass 2026: An exploited LiteLLM MCP auth bypass and default sk-1234 master keys let attackers steal AI gateway master and provider API keys.
• Moltbook AI agent keys breach: Moltbook breach exposed 1.5M AI agent keys.

Read and download The State of NHI & AI Agent Breach Report 2026, covering 200+ breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

MCP governance is now an identity architecture problem, not a protocol feature question. The article shows that once an MCP gateway must handle auth, tool registration, audit logs, and policy enforcement, the control plane becomes part of the identity stack. That is why teams comparing gateways should evaluate access governance, not just transport compatibility. Practitioners should treat MCP routing as a governed identity surface.

The named concept here is governance fragmentation. When LLM traffic and MCP tool traffic are controlled in separate systems, policy drift becomes harder to detect and explain. The article’s strongest signal is that operational convenience can hide control duplication, inconsistent logging, and unclear accountability across two planes. Practitioners should look for a single owner and a single evidence model before sprawl becomes normal.

Open-source flexibility does not remove the governance burden. The article is explicit that self-hosting pushes patching, monitoring, and compliance work onto the enterprise. That means the real trade-off is not free versus paid, but whether the organisation can sustain the controls needed around access, auditability, and service reliability. Practitioners should assess whether internal ownership is a deliberate operating choice or an unpriced risk.

Managed deployment becomes a security decision once AI systems are operationally material. The article’s comparison makes clear that support, SLAs, and deployment options matter because they determine how quickly teams can recover, evidence, and adapt when MCP usage scales. In practice, the decision should be framed as governance capacity, not vendor convenience. Practitioners should match the platform to their assurance obligations.

Enterprise identity controls are the dividing line between a gateway and a governed platform. The article repeatedly returns to SSO, RBAC, audit logs, and compliance readiness because those are the controls that let organisations scale access without losing oversight. This is the point where MCP infrastructure becomes an IAM and IGA decision as much as an engineering one. Practitioners should test whether the platform can carry the access model they already run.

From our research library:

• 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.
• Read next: Agentic AI Identity Maturity Model

What this signals

Governance fragmentation: MCP programmes fail when model access, tool access, and logging live in different control planes. The result is not just overhead, but inconsistent evidence and unclear accountability when teams need to explain who authorised what.

Organisations should expect MCP gateway selection to converge with IAM and platform governance, because the access model matters as much as the protocol support. A gateway that cannot align with enterprise identity, audit, and deployment requirements becomes a local optimisation rather than a durable control point.

For practitioners

• Map MCP authorization boundaries Document where OAuth, JWT, custom headers, and tool-level permissions are enforced so you can see which layer actually owns access decisions.
• Consolidate model and tool governance Evaluate whether separate AI gateway and MCP gateway stacks are creating duplicate policy, logging, and cost controls across the same agent workflows.
• Define your operating model for self-hosting Decide whether your team can realistically own patching, monitoring, and evidence collection for an MCP gateway before choosing an open-source path.
• Require enterprise access controls Check for org-wide RBAC, SSO, and audit logging as baseline controls when MCP access must scale beyond a few internal users.

Key takeaways

• MCP gateways are becoming identity control points, not just transport routers, because access, audit, and tool registration all sit in the same decision path.
• The central risk is governance fragmentation, where separate model and tool controls create inconsistent policy and weak evidence.
• Practitioners should choose platforms based on operating model, enterprise access control, and assurance needs, not feature breadth alone.

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.
• Unified Governance Platform: A unified governance platform is a system that combines spend, risk, and control data into one reporting view. In practice, it reduces manual reconciliation and gives leaders a single operating picture for compliance, assurance, and investment decisions across the enterprise.
• Managed Deployment: A centrally administered rollout method for installing and activating a browser extension or agent through endpoint management tooling. It reduces end user friction, supports standardised deployment, and gives administrators a repeatable way to extend security controls across supported browsers.
• Fragmented Governance: Fragmented governance is a state where policy, access control, lineage and accountability are split across separate tools or teams. The result is inconsistent enforcement and weak traceability, especially in AI programmes where data moves quickly across domains and identities.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle 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 NHI governance in your organisation, it is worth exploring.