TL;DR: AI agents are already connecting to production systems through Model Context Protocol, and Stacklok argues that without a registry, teams lose visibility into who invoked which server, what it accessed, and what credential reached downstream resources. The real issue is governance, not convenience: ungoverned MCP deployments turn tool access into an audit blind spot and breach path, with no stable control plane for identity, attestation, or blast-radius limits.
NHIMG editorial — based on content published by Stacklok: How to build a local MCP registry your security team will actually approve
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: How should security teams govern MCP servers in production?
A: Treat each MCP server as a governed access boundary, not just a utility.
Q: Why do ungoverned MCP deployments create audit and breach risk?
A: Ungoverned MCP deployments create risk because they obscure who approved access, which server executed the call, and what credential reached the target system.
Q: What breaks when MCP servers rely on a single shared credential?
A: A single shared credential breaks attribution, scope control, and revocation precision.
Practitioner guidance
- Connect MCP to your identity provider first Require authenticated user identity before onboarding any server, and reject designs that rely on shared backend credentials for downstream access.
- Inventory every server and downstream dependency Document the source, owner, permissions, data classification, and backend systems for each MCP server, then keep that manifest in version control as the authoritative registry input.
- Enforce source attestation and image signing Block any MCP server that lacks provenance, signed artifacts, or approval history.
What's in the full article
Stacklok's full how-to covers the operational detail this post intentionally leaves for the source:
- Helm installation commands for deploying the registry inside a Kubernetes environment.
- CRD examples for version-controlled server definitions and approval workflows.
- Day-two operations guidance for onboarding, updating, and removing MCP servers.
- Concrete examples of OIDC and OAuth 2.0 SSO integration with enterprise identity providers.
👉 Read Stacklok's guide to building a local MCP registry →
Local MCP registry governance: what security teams need to approve?
Explore further
Local MCP registries are becoming the missing control plane for AI tool governance: MCP makes tool connectivity easy, but enterprise approval, attribution, and revocation still have to be supplied by the identity layer. When teams skip the registry, they do not just create shadow integrations, they create shadow access paths that sit outside normal IAM review. The practitioner takeaway is that MCP should be governed like any other high-risk NHI surface, not treated as a developer convenience.
A few things that frame the scale:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%), according to AI Agents: The New Attack Surface report.
- 48% of companies say they cannot track and audit the data their AI agents access, which leaves compliance and breach investigation with a structural blind spot.
A question worth separating out:
Q: Who is accountable when an AI agent takes action through an MCP server?
A: The accountable party is the human or team that authorised the agent's access, but only if the organisation can prove that chain. Without immutable logs that connect the initiating identity to the tool call and final action, accountability becomes weak, and legal or compliance teams lose the evidence they need.
👉 Read our full editorial: Local MCP registry governance is now a security requirement