Secure and Govern AI with MCP

Take control of the AI activity already happening inside your organization with a policy-enforced, identity-aware control plane that gives you answers when it matters most.

The AI governance problem has arrived

Most organizations didn’t plan their AI rollout. Developers installed tools, teams stood up integrations, and agents started calling internal systems before governance frameworks existed. Now the bill is coming due. Three scenarios are forcing the issue:

Compliance Audit

Security and GRC teams want to know what AI tools are accessing what data, on whose authority, and whether it’s logged. Today, most organizations can’t answer those questions.

The Surprise AI Bill

A single agentic workflow can generate thousands of downstream API calls to metered SaaS services. Without usage controls, the cost arrives well before value is created.

The Infosec Mandate

Security teams are increasingly blocking AI adoption outright because the controls don’t exist yet to satisfy a review. Without a governance story, adoption stalls.

Model Context Protocol (MCP) has become the standard interface through which AI agents access tools and data inside the enterprise. Governing MCP means governing your AI.Without a secure gateway, your AI initiatives may remain siloed, insecure, and disconnected from your enterprise’s true source of truth.

Using governance to accelerate AI adoption

At Stacklok, we’ve seen this pattern across dozens of enterprise AI deployments: the teams who build governance infrastructure first move faster in the long run, because they’re not fighting security reviews and one-off approvals at every step. Our platform gives you three things:

Visibility

Know what MCP servers exist, who is using them, and what data they’re touching across every team, client and deployment model.

Control

Apply policy to AI activity the same way you apply it everywhere else: through your existing identity provider, policy engine and security tooling.

Evidence

Produce the audit logs, access records and usage reports your compliance, legal an security teams need without building custom instrumentation.

How to define your AI governance posture with Stacklok

We’ve distilled the process into five high-impact steps

1

Discover what is already running

  • Inventory every MCP server in use across your organization, including locally installed, remotely hosted, and vendor-supplied servers. Surface ungoverned deployments before they become incidents.

Stacklok Advantage

Stacklok’s registry provides a centralized catalog of all MCP activity across your environment. Servers that aren’t registered are flagged to prevent shadow AI.

2

Integrate with your existing identity provider

  • Connect Stacklok to your IdP (Entra ID, Okta, Google, or LDAP-based environments) to tie every AI agent action to a verified user identity, so that downstream systems log the actual end user, not a shared credential.

Stacklok Advantage

We handle OAuth token exchange and on-behalf-of flows so that audit trails reflect real user identity.

3

Apply policy-as-code to AI tool access

  • Define who can access which MCP servers, and which tools within those servers, using policy engines you already know: Cedar, OPA/Rego, or RBAC via your IdP. Enforce at three layers: registry, gateway and tool.

Stacklok Advantage

Enforce at three layers: registry (who can discover and install servers), gateway (which tool calls are permitted), and tool (what actions a specific user or role can execute).

4

Enforce usage controls and cost guardrails

  • Rate-limit agent calls to downstream systems and prevent runaway agentic workflows from generating unexpected API load or cost.

Stacklok Advantage

Stacklok’s MCP Optimizer filters tools to what an agent actually needs, reducing token usage by up to 85% and improving model accuracy. 

5

Produce audit-ready logs and observability

  • Capture a full record of every MCP call: which user, which agent, which tool, which data source. Route telemetry to your existing SIEM, observability stack, or data warehouse via OpenTelemetry.

Stacklok Advantage

Integration with standard OTEL pipelines means your security team sees AI activity alongside everything else, in the tools they already use.

Best practices for AI governance

Do

  • Establish governance infrastructure before your MCP footprint scales.
  • Use your existing IdP as the authoritative source of identity for AI agent actions.
  • Treat AI governance as an extension of your existing security posture, not a separate program.

Don’t

  • Rely on blocking AI adoption as your governance strategy.
  • Accept all-or-nothing access decisions; insist on granular, tool-level policy.
  • Build custom observability instrumentation for MCP that you have to maintain forever.

Stacklok enablement path

We support you wherever you are in your governance journey, responding to an audit, getting ahead of a mandate or building a framework.

  • Governance Assessment: Map your current MCP footprint and identify ungoverned exposure.
  • IdP Integration: Connect your identity provider and establish token exchange for end-user attribution.
  • Policy Implementation: Deploy Cedar or OPA-based policies at the registry, gateway, and tool level.
  • Audit Readiness: Configure OTEL-based telemetry and structured logging for compliance and SIEM integration.