IBM ContextForge alternatives expose the MCP governance gap

At a glance

What this is: This is a vendor comparison of IBM ContextForge alternatives that concludes many organisations need more than an MCP-only gateway and should look for unified AI and MCP governance.

Why it matters: IAM, NHI, and platform teams need to understand how MCP gateways intersect with SSO, RBAC, auditability, and deployment boundaries before agent sprawl turns into unmanaged access.

By the numbers:

• 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.

👉 Read TruFoundry's comparison of IBM ContextForge alternatives for MCP governance

Context

MCP gateways are becoming a control point for AI systems because they sit between agents and the tools, data sources, and actions those agents can reach. When the gateway is only a proxy, organisations may still lack unified identity controls, auditability, and policy enforcement across the rest of the AI stack, which is where governance gaps usually appear first.

This article frames the issue as an architecture choice, not just a product shortlist. For teams already dealing with AI agent sprawl, the question is whether MCP access is governed as part of identity and access management, or treated as a separate technical layer that can drift away from enterprise policy.

For practitioners evaluating agentic AI programmes, the real comparison is between lightweight protocol mediation and governed access infrastructure. That distinction matters because once tools, models, and agents are all connected, the weakest control plane defines the blast radius.

Key questions

Q: How should security teams design MCP server access for AI agents?

A: Security teams should design MCP access around a small set of agent goals, not a mirrored list of REST endpoints. Group related backend calls into workflows, expose known data as resources, and reserve tools for actions that truly change state. That keeps the agent oriented, reduces context churn, and makes the access surface easier to review and govern.

Q: Why do separate model and tool control planes create risk?

A: Separate control planes create policy drift, duplicated logging, and inconsistent revocation because the model path and the tool path are governed differently. In agentic environments, that split increases the chance that one system grants access while another cannot explain or stop it. Unified governance reduces that mismatch.

Q: When is self-hosted MCP infrastructure a bad fit?

A: Self-hosted MCP infrastructure becomes a poor fit when the organisation lacks spare platform engineering capacity for patching, scaling, monitoring, and support. At that point, the supposed simplicity of open source is replaced by governance debt, especially when access policy and uptime are business-critical.

Q: What should organisations do about shadow AI in MCP environments?

A: Organisations should discover unmanaged agents before expanding gateway policy. If an agent is not registered, no amount of RBAC at the gateway will fully govern it. Discovery, registration, and enforcement need to be part of the same operating process so hidden access paths do not bypass the control plane.

Technical breakdown

Why MCP gateways become identity control points

An MCP gateway brokers requests between AI agents and external tools, so it is effectively part of the identity path, not just a networking component. Authentication, authorisation, logging, and capability scoping determine which tools an agent can reach and what it can do once connected. If the gateway only proxies traffic, governance remains fragmented across separate systems. If it also enforces RBAC, audit logging, and deployment policy, it becomes a practical control plane for NHI and agentic workflows.

Practical implication: Treat the gateway as an access enforcement layer and require identity, policy, and audit controls before allowing production tool access.

Unified AI and MCP governance versus split control planes

Many teams start with an MCP-only proxy and then bolt on separate controls for LLM routing, agent monitoring, and secrets handling. That creates duplicated policy logic, different audit trails, and inconsistent enforcement between model traffic and tool traffic. A unified control plane reduces that fragmentation by applying the same governance model across both AI inference paths and tool execution paths. The operational difference is not cosmetic: it changes where policy is written, where logs land, and how quickly access can be revoked.

Practical implication: Align model routing and MCP governance under one policy model so access review and incident response operate on the same evidence set.

Managed deployment versus self-hosted gateway complexity

ContextForge is described as powerful but infrastructure-heavy, which means the control is not just protocol support but operational ownership. Self-hosted gateways require teams to manage patching, upgrades, scaling, and support, in addition to identity policy. Managed platforms shift that burden, but they also define a stricter governance boundary around how agents, servers, and audit data are operated. The architecture choice therefore becomes a question of control maturity, not preference.

Practical implication: Decide whether your team can sustain self-hosted governance operations before adopting a gateway that assumes internal DevOps depth.

Threat narrative

Attacker objective: The attacker or misbehaving agent aims to extend tool access beyond the intended control boundary and use that access to reach data or systems without effective oversight.

1. Entry occurs when AI agents are allowed to reach tools through an MCP gateway that lacks unified governance across identity, policy, and audit layers.
2. Escalation occurs when separately managed model traffic, tool traffic, and credentials create inconsistent permissions and opaque tool-call paths.
3. Impact occurs when agents can access, combine, or expose data and actions beyond the intended governance boundary, increasing compliance and breach risk.

Breaches seen in the wild

• Moltbook AI agent keys breach — Moltbook breach exposed 1.5M AI agent keys.
• Meta AI Instagram Account Takeover — 20,225 Instagram accounts hijacked via compromised Meta AI support chatbot with overprivileged access.

Read our 52 NHI Breaches Analysis report for a comprehensive view of breaches impacting Non-Human Identities including AI Agents.

NHI Mgmt Group analysis

ContextForge alternatives are really a proxy for the MCP governance maturity gap. The article shows that teams do not leave an MCP gateway because it lacks transport features alone; they leave because transport, identity, logging, and deployment control are no longer separable in production. Once AI agents are operational, a proxy-only model forces security teams to stitch governance together after the fact. The practitioner conclusion is that MCP tooling must be judged as part of the identity control plane, not as an isolated integration layer.

Unified AI and MCP governance is becoming the default design expectation for agentic programmes. Separate systems for model routing, tool mediation, and access enforcement create policy drift, which is exactly where audit gaps appear. That is a classic identity architecture failure, not a product preference issue. The field is moving toward shared control planes because agentic workflows collapse the distance between model decisions and tool execution, so the practitioner response is to align governance, not multiply proxies.

Managed deployment changes the identity operating model, not just the hosting model. When a gateway is self-hosted, the team owns patching, scaling, support, and change control in addition to access policy. That expands the identity governance burden into platform operations, which many enterprises underestimate. The practical conclusion is that support SLA, deployment model, and auditability are now identity decisions as much as infrastructure decisions.

Shadow AI discovery belongs in the same conversation as MCP mediation. A gateway can only govern what is registered and visible, while unmanaged agents continue to interact with tools outside the approved path. That means discovery and enforcement must be paired if the organisation wants meaningful coverage. The practitioner implication is straightforward: governance that cannot see the full agent estate is only partial control.

The named concept here is MCP governance fragmentation. It describes the state where tool access, model routing, identity policy, and monitoring live in different systems with different operators and different evidence. Fragmentation increases blast radius because no single control layer can explain or revoke agent behaviour end to end. The practitioner conclusion is to reduce the number of enforcement boundaries before agent scale makes them unmanageable.

From our research:

• 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials, according to AI Agents: The New Attack Surface report.
• 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.
• If you are mapping this problem to control design, read OWASP NHI Top 10 for the agentic risk model and NIST AI Risk Management Framework for governance structure.

What this signals

MCP governance fragmentation: The more an organisation splits model routing, tool access, and monitoring across different systems, the harder it becomes to prove who did what and when. That is why identity teams should expect MCP gateways to be reviewed alongside PAM, audit, and access certification rather than as isolated AI infrastructure.

With 80% of organisations already reporting AI agents acting beyond intended scope, the governance problem is no longer hypothetical, and the current control stack is not absorbing that pressure cleanly. Teams that want to stay ahead should pair discovery of unmanaged agents with policy enforcement at the gateway boundary, then verify whether agent access visibility exists across the full tool chain.

The next maturity step is not more routing options, but better evidence. If a platform cannot show registered agents, permitted tools, and logged actions in one place, it is functionally too fragmented for enterprise-grade identity governance.

For practitioners

• Map MCP gateways into your identity control plane Classify every gateway, proxy, and agent tool endpoint as part of the access path, then require authentication, authorisation, and audit logging before production use.
• Unify model routing and tool governance Avoid separate policy stacks for LLM traffic and MCP traffic when the same agents use both, because split enforcement creates inconsistent revocation and review workflows.
• Assess your self-hosting burden honestly If your team cannot sustain patching, scaling, and support for another stateful gateway, choose a managed operating model rather than assuming free software lowers total risk.

Key takeaways

• AI agent governance now reaches into the MCP layer, where identity, policy, and audit must be enforced together.
• Gateway choice is an operating-model decision as much as a technical one, because self-hosting shifts security ownership onto the platform team.
• Teams that cannot see unmanaged agents, tool calls, and revocation paths in one control plane will struggle to govern agentic access at scale.

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 Control Plane: A unified control plane is an identity architecture where discovery, access governance, audit, and response operate across humans, machines, and AI agents together. It reduces blind spots caused by siloed tooling and gives security teams context for decisions about permissions, data, and containment.
• Shadow AI: AI agents, copilots, or connected tools operating without full visibility or governance from security teams. Shadow AI becomes an identity problem when those systems authenticate with unmanaged tokens, service accounts, or OAuth apps that can reach production resources.
• MCP Governance Fragmentation: A condition where identity policy, logging, model routing, and tool mediation are split across separate systems and teams. That fragmentation makes it harder to prove access decisions, revoke privileges consistently, or investigate what an agent actually did.

What's in the full article

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

• Side-by-side capability tables for each ContextForge alternative, including deployment, authentication, observability, and compliance
• Pricing and packaging detail for the managed platforms, including which tiers support self-hosted MCP servers
• Practical buying guidance on when to choose a gateway, when to choose an AI security platform, and when to stay open source
• Vendor-specific notes on enterprise support, compliance posture, and operating tradeoffs

👉 The full TruFoundry guide breaks down deployment, governance, and pricing tradeoffs for each alternative.

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 security programme, it is worth exploring.