How to Choose an AI Governance Platform

1. Map what you need to govern

• List the AI clients in use today (Claude, ChatGPT, Cursor, Copilot, Codex and others) and the agents your teams have built.
• List the systems those clients and agents reach through MCP or APIs, and mark the ones holding customer, financial or health data.
• Decide whether you need to govern model calls as well as tool calls.

2. Rule platforms in or out on deployment

• Decide whether hosted SaaS is acceptable, or whether you need single-tenant, self-hosted, on-premises or air-gapped.
• Confirm the data residency regions you need are available today.
• Drop any platform that can't meet both before comparing features.

3. Check identity fits your IdP

• Confirm SSO and SCIM with your identity provider, and on which plan.
• Check that agents can have their own scoped credentials instead of shared service accounts.

4. Test enforcement on a real policy

• Pick two policies you actually need and ask each vendor to enforce them live in a demo.
• Check that detection can run alert-only first, and what blocking does to latency.

5. Measure the unmanaged surface

• Run shadow AI discovery on a pilot group, where offered, and compare the findings with your inventory.
• Check how discovered tools move onto the approved path.

6. Confirm audit and evidence

• Get a sample log record and confirm it names user, agent, tool, decision and time.
• Confirm export to your SIEM, and framework mapping if your auditors expect it.

Coverage

• Which AI clients and agent frameworks do you support today?
• Do local MCP servers on laptops pass through your controls?
• Do you cover model calls, tool calls or both?

Identity and credentials

• Where are downstream OAuth tokens stored and who can read them?
• Can an agent act for a user without inheriting all of that user's permissions?

Enforcement

• Is enforcement inline on each call or after the fact?
• Can risky actions require human approval?
• Who maintains detection rules, and can we add our own?

Evidence and operations

• Can we redact values before they are logged?
• Which frameworks do you map to, and can we export the evidence?
• What is published about pricing, and what does a pilot include?

Small teams and early pilots

A handful of power users, a few MCP servers, one security owner.

• A hosted MCP gateway with a self-serve trial gets controls in place fastest; MCP Manager and MintMCP both fit here.
• Prioritize audit logs and PII filtering over discovery at this stage.
• Check that the platform can grow into SSO, SCIM and SIEM export without a migration.

Mid-size companies rolling AI out to every team

Many clients, many connectors, non-engineers starting to use agents.

• Look for a control plane that combines discovery, the gateway, runtime security and an approved catalog. Runlayer is built for this stage.
• Role-based bundles or catalogs matter more than raw connector counts.
• Ask how the platform reports usage and adoption, not only risk.

Large enterprises

Several business units, strict isolation rules, existing security tooling.

• Require single-tenant or self-hosted deployment and per-unit isolation; Runlayer documents both.
• Check whether the platform can enforce through gateways and hooks you already run, as Noma Security describes.
• Pair a runtime control plane with a governance-of-record tool such as Credo AI if your risk team runs formal AI inventories.

Regulated: financial services, healthcare, public sector

• Shortlist only platforms with documented self-hosted or single-tenant options and the residency you need.
• Require inline, per-call enforcement with human approval for high-impact actions.
• Ask for framework-mapped evidence (EU AI Act, NIST AI RMF, ISO 42001) and the vendor's own SOC 2 and HIPAA documents.

Moderate: customer data, internal systems

• Prioritize identity, PII redaction and tamper-evident audit logs.
• Start runtime detection in alert-only mode, then move to blocking once you have a baseline.

Lower: internal productivity, no sensitive data

• A hosted gateway with an approved registry and logs is usually enough to begin.
• Add discovery before usage spreads, so the approved path becomes the default.

You already run an MDM

• Platforms that deploy discovery through MDM, such as Runlayer's AI Watch, can find unmanaged MCPs and client configs without employee opt-in.

You already run an AI or MCP gateway

• Look at platforms that enforce through existing control points, such as Noma Security, or plan to consolidate onto one gateway.

Your engineers build their own agents

• An actions runtime with SDKs and delegated auth, such as Arcade.dev, fits agents inside your products; a control plane fits governing employees' own clients.

Your risk team runs a GRC program

• A governance, risk and compliance platform such as Credo AI keeps the inventory and policy packs; pair it with a runtime control point for enforcement.

You run a SIEM

• Require OpenTelemetry or native SIEM export and confirm which plan includes it.