Top 12 AI Control Plane Tools for Regulated Deployments
An AI control plane provides a shared layer for operating AI agents and agent-based applications. We compared the top 12 AI control plane tools for enterprise architects, security teams, and AI governance owners planning AI adoption at enterprise scale.
Top 12 AI control plane tools feature coverage
Read the methodology to see how we scored these products.
Vendor selection criteria:
We included vendors that provide centralized governance, security, observability, or control layers for AI systems across multiple parts of the AI stack, including models, agents, applications, and tools.
These vendors provided capabilities such as policy enforcement, runtime protection, access controls, evaluations, monitoring, auditability, inventory management, approval workflows, and risk management.
We excluded vendors focused primarily on basic MCP connectivity, tool integration, or access management, without broader capabilities in model, agent, application, security, or AI governance. We also excluded conventional agent-development frameworks and standalone infrastructure tools that lack a governance or control layer.
Architecture and deployment comparison
Governance, identity, and security comparison
Operations and product maturity comparison
Note: The tables are sorted by feature comparison score, except for our customer at the top. “Limited” means the capability is limited in scope, or not fully documented in public sources. Read the vendor descriptions below to learn about the product’s broader capabilities.
xnode cortx
xnode cortx is a customer-resident AI control plane for regulated enterprises. Its key features include multi-provider model routing, an MCP gateway, identity integration, runtime policy enforcement, shadow AI discovery, coding-assistant governance, cost attribution, and on-premises or air-gapped deployment with zero data egress.
xnode’s security coverage centers on inline governance, guardrails, data redaction, prompt-injection defense, and model-access policy, with shadow-AI monitoring for unapproved model usage. The platform also provides tamper-evident audit logs, agent evaluations, and lifecycle controls.
Kosmoy
Kosmoy combines agent discovery, gateway enforcement, compliance evidence, and sandboxed execution. Its registries identify agents, models, and MCP servers across major cloud and enterprise platforms, while its gateway applies policy to model, tool, and agent-to-agent traffic. High-risk workloads can run inside kernel-enforced Action Capsules with short-lived credentials and a kill switch.
TrueFoundry
TrueFoundry’s LLM, MCP, and agent-to-agent gateways can run as SaaS, in a customer VPC, or entirely inside an isolated Kubernetes environment with local identity, policy, and logging. The tradeoff is limited discovery of unmanaged agents and a higher operational burden for teams without Kubernetes expertise.
Airia
Airia covers agent discovery, security, governance, model routing, budgets, MCP connectivity, and runtime policy enforcement. It supports SaaS, private cloud, on-premises, air-gapped, and hybrid deployments.
Fiddler AI
Fiddler AI’s Centor Models run entirely inside the customer environment to score prompts, responses, and agent plans, returning allow, block, or redact verdicts with no data egress and no external evaluation API calls. Its coverage centers on runtime guardrails for PII/PHI, secrets, jailbreaks, prompt injection, and groundedness, paired with deep hierarchical observability from the application down to the span, with cost attribution per developer, model, repository, and pull request.
Speakeasy
Speakeasy governs MCP servers, tools, skills, and assistants across clients such as ChatGPT, Claude, Cursor, and Copilot. It provides a central catalog, tool-level permissions, SSO integration, OAuth protection, OpenTelemetry-based observability, and managed MCP generation from OpenAPI specifications.
NeuralTrust
NeuralTrust splits its platform across three products:
- TrustGate is an open-source gateway that applies one policy model to LLM and MCP traffic, with threat detection handled in-engine by TrustGuard rather than through external plugins.
- TrustTest generates domain-specific test suites and runs adversarial campaigns against deployed applications, covering both functional evaluation and red teaming.
- TrustLens provides runtime observability and alerting. It supports SaaS, hybrid, and air-gapped deployments.
Noma Security
Noma Security discovers models, agents, and MCP servers across enterprise environments and applies identity controls, tool-level controls, supply chain validation, and runtime policy enforcement. Its adaptive red-team engine can test multi-step agent workflows during development and deployment.
Lunar.dev
Lunar.dev applies one policy model across AI model calls, MCP tools, and conventional API traffic. Its platform supports routing, authentication, rate limits, payload transformation, data sanitization, audit logging, and tool hardening.
Microsoft Foundry Control Plane and Agent 365
Microsoft’s main advantage is its integration with Entra ID, Defender, Purview, Microsoft 365, and Azure. Agents can receive managed identities and inherit many of the security, compliance, and access policies applied to users and applications.
ServiceNow AI Control Tower
ServiceNow AI Control Tower governs agents, models, and workflows across major cloud and enterprise platforms and connects them to existing risk, compliance, workflow, and asset management processes. Its Private Stack option supports customer-operated deployments for sovereign or regulated environments.
Zenity
Zenity specializes in discovering and securing agents across SaaS platforms, cloud services, low-code tools, and developer environments. It analyzes agent execution paths, including prompts, tool calls, data access, memory, and control flow.
Its enforcement model varies by environment. Through native platform integrations, agent hooks, and its MCP gateway, Zenity can evaluate and block certain actions before execution. It also supports response measures such as terminating sessions, quarantining agents, and revoking permissions during execution.
Core capabilities of an AI control plane
Air-gapped operation
An air-gapped deployment must run without internet access and without vendor-hosted services. Authentication, policy evaluation, model access, tool execution, and administration must all work within the isolated environment.
The platform needs a controlled path for importing container images, model artifacts, vulnerability feeds, and license updates. Activation checks and telemetry that call home will fail an air-gapped review even when the runtime path is clean.
Separation of control and data planes
The control plane holds identities, policies, configuration, approvals, and governance records. The data plane processes prompts, retrieved content, model responses, and tool parameters.
Separating them keeps sensitive runtime content out of the governance layer and allows the runtime scale and fail on its own.
Model gateway
The model gateway brokers access between agents and foundation models, embedding models, and rerankers. Behind the gateway is a catalog of approved models, each with its provider, version, hosting location, and permitted use.
At runtime, the gateway may select an approved model, pin traffic to a region, block unapproved endpoints, or apply token and cost limits. A vendor that observes model calls rather than brokering them cannot prevent an agent from reaching an endpoint the catalog never approved, and should not receive full credit here.
Tool (MCP) gateway
The tool gateway brokers the APIs, databases, browsers, code interpreters, and internal systems that agents invoke. MCP has become the common protocol for this traffic, though direct API integrations and framework-specific connectors remain widespread.
Each tool needs an owner, a schema, a risk classification, and a credential model. At runtime, calls should be authorized based on the agent’s identity and the action’s parameters. Reading one record and exporting the whole table are different actions on the same tool.
Enforcement
Two platforms can hold identical policy engines and differ completely in what those policies can stop:
- Own gateway: The vendor operates the proxy that model and tool calls transit through. Because traffic passes through their component, the platform can refuse to forward a request, and no governed call escapes review. The cost is a new component in the request path with its own latency budget, failure modes, and migration work.
- Attached to an existing gateway: The platform hooks into a gateway the customer runs and returns verdicts inline. Enforcement is still synchronous and precedes egress, but coverage is limited to what the host gateway can see, and integration depends on extension points that the vendor does not own. Deployment is far lighter, as nothing in the request path is replaced.
- Observe and intervene: The platform watches execution through APIs, agent hooks, or platform event streams rather than carrying the traffic. It can revoke a credential, quarantine an agent, or terminate a session, but it acts on an action in progress rather than refusing it before it starts. Coverage is broad across environments; timing is not guaranteed.
- Platform-native: Enforcement is a property of the vendor’s own runtime. Agents built on the platform inherit its identity, policy, and logging automatically, and agents built elsewhere are governed as far as the platform’s connectors reach.
- Federated: The platform holds the policy, inventory, and approval records but delegates execution to gateways, cloud controls, and agent platforms owned by other vendors.
Zero data egress
Zero data egress means no customer payload reaches the control plane vendor. Prompts, retrieved context, tool arguments and results, traces, and model outputs remain within the customer boundary, and evaluation and guardrail scoring also execute there.
Agent egress control is a separate question. Outbound access should be denied by default and opened to approved destinations, with proxy enforcement, inspection, and logging on any permitted path. Few control planes enforce this themselves. Most rely on the service mesh or cloud firewall, so establish which layer owns it.
Discovery and inventory
The platform should find agents, models, and MCP servers across the environments where they are created: cloud accounts, SaaS platforms, low-code builders, developer machines, and CI systems.
Each record needs an owner, a business purpose, a model and tool list, a credential reference, and a current status. Discovery that returns a list without those attributes produces an inventory nobody can act on.
Agent identity
Every production agent needs a verifiable identity. Shared API keys make attribution impossible and usually give several agents the same permissions.
The chain of authority must survive the full path: user or service, then agent, then downstream system. An agent acting for itself, acting on behalf of a user, and using a service role are different authorization cases and should be evaluated separately. In multi-agent workflows, record which agent delegated the task and whether authority changed at the handoff.
Access control
Access should be least-privilege and time-bound. Credentials should be short-lived, scoped to the resource in use, and issued when the agent needs them rather than held indefinitely. Decisions should weigh the caller’s identity, the tool, the parameters, and the classification of the data involved.
Runtime policy enforcement
The control plane converts governance requirements into policies that can be applied during agent execution. Policies should be versioned, reviewed, tested, and deployed gradually. A decision may consider the user, agent identity, requested tool, action parameters, data classification, region, transaction value, risk score, and approval status.
The control plane can restrict the action, redact sensitive data, select an approved model, request human review, limit the output, or stop the workflow.
Threat detection
Detection should identify prompt injection, credential misuse, exfiltration, privilege escalation, and unauthorized tool use in real time. Signals get stronger when agent activity is joined to identity records, data classifications, and existing security telemetry.
Alerts must preserve full execution context. An analyst needs to see which user started the workflow, which agent acted, and whose authority it used. Rules should cover both known indicators and behavioral anomalies.
Containment
When an action goes wrong, the control plane should be able to stop it. Containment actions include suspending the agent, revoking its credentials, disabling a tool, blocking a model route, and terminating a session.
Many platforms detect and alert, but hand remediation to a person working in a different console, which is why blocking a single request is common and quarantining an agent is rare.
Compliance support
Evidence should come from operational records. The platform should be able to show who approved an agent, what data it could reach, which policy version applied, and where processing occurred.
Useful outputs include inventories, approval history, evaluation results, and audit trails, exportable to the GRC system in use. Residency rules, retention schedules, and control mappings should be configurable rather than assumed.
Observability
Standard service metrics, latency, errors, and uptime describe system health rather than agent decisions. The control plane should connect the full execution path from the requesting user through model calls, retrieval sources, tool calls, policy decisions, agent handoffs, and the final action.
Telemetry requires governing in its own right. Traces contain both prompts and retrieved content, so redaction, encryption, regional storage, and access limits apply to the observability store, as they do in the runtime.
Audit architecture
For high-risk workflows, the audit trail should be tamper-evident, linking each action to the identity that took it, the policy version in force, and the evidence of approval. Signed or append-only records are what raise this from a log to something defensible in a review.
Cost management
Usage should be attributable to the agent, owner, team, model, and business process, and budgets and rate limits should be enforceable rather than advisory.
Lifecycle management
The agent framework coordinates tasks. The control plane governs the conditions under which they may run: registration, testing, production approval, version promotion, rollback, suspension, and retirement.
Retirement is the step most often skipped. Decommissioning an agent involves revoking its credentials and closing its access to tools.
Evaluation
Agents should be evaluated before deployment and continuously after release, against task quality, groundedness, tool-use accuracy, policy compliance, and cost.
Results are useful if they are reproducible. Test sets, scoring criteria, model versions, and prompts should be pinned to the agent release they assessed. Without that, a regression cannot be traced back to the change that caused it.
Red teaming
Red teaming is the pre-production counterpart to threat detection: the attacks are the same; however, they are provoked rather than observed. Scenarios include prompt injection through retrieved content, jailbreaks, confused-deputy attacks, unauthorized delegation, and manipulation of tool parameters.
Testing a model in isolation leaves the agent-level failure modes untested. The failures that matter involve the agent’s identity, its tool permissions, and the approval steps between it and a downstream action.
Coding-agent governance
Coding agents have write access to repositories, secrets, and deployment pipelines. Access should be scoped to the repositories, files, and operations required by the assigned task.
Most of the enforcement here is handled by existing systems. Branch protection lives on the source host; release gates live in CI; dependency provenance comes from the registry and scanning tools. The control plane’s job is to require those controls, verify that they were applied, and refuse the change when they were not.
The one control with no substitute is separation of duties: an agent must not approve or deploy its own change. Every generated change should stay attributable to the requesting user, the agent and model version, the instructions given, and the review that let it through.
Why AI control planes matter now
The risk profile of AI changes when a model gains tools. A conventional language model produces an answer. An agent may use that answer to send an email, modify a customer record, run code, approve a workflow, or pass work to another agent. Errors therefore move from the content layer into business processes and external systems.
Agent proliferation creates fragmented control
Agents are often introduced by separate departments using different AI models, agent frameworks, cloud accounts, service identities, and observability tools. Some are deployed by central engineering teams; others begin as departmental automations or low-code experiments.
Without centralized management, the enterprise may not know:
- Which agents exist or whether they are still active
- Who owns each agent
- Which model, prompt, tools, and data sources it uses
- Whether its credentials are shared or overprivileged
- Which version is in production
- What agent actions have occurred
- Whether the agent still creates business value
Our AI governance research shows a fragmented market in which governance, MLOps, LLMOps, data governance, and monitoring products address different parts of the problem.
Governance must move closer to execution
Traditional AI governance often concentrates on model approval, documentation, bias testing, and periodic risk assessment. Those controls remain important, but an agent can make a new decision every time it receives context or calls a tool. AI control plane can:
- Block customer data from being sent to an unapproved model.
- Prevent an agent from calling a payment tool outside its assigned business process.
- Require human approval for a transaction above a risk threshold.
- Restrict model access by geography, department, or data classification.
- Stop an agent after unusual tool activity or repeated policy failures.
Cost becomes an operational control
Agent costs are less predictable than those for single-model requests. One task may trigger several model calls, retrieval steps, retries, sub-agents, and external tools. A poorly configured loop can consume tokens without producing useful work.
The control plane should attribute model use to the requesting user, agent, team, workflow, and business outcome. It should also enforce budgets, concurrency limits, model-routing rules, and maximum execution steps.
AI control plane architecture
An AI control plane architecture separates centralized management from distributed enforcement.
The central layer maintains the organization’s view of agents, owners, policies, identities, versions, risk classifications, and deployment status. Enforcement points sit near the systems where decisions must take effect: agent runtimes, model gateways, tool gateways, data platforms, API gateways, and execution environments.
A simplified architecture involves:
- Users and business applications sit at the top of the architecture. They initiate requests, trigger workflows, and provide the business context in which AI agents operate.
- Those requests are passed to agent applications and multi-agent workflows. This is where agents interpret goals, coordinate tasks, and decide which models, tools, or data sources they need.
- Before execution, actions pass through runtime enforcement points. These may include agent framework adapters, AI gateways for model calls, agent or MCP gateways for tool calls, data access controls, and human-approval services for higher-risk actions.
- The execution layer contains the systems that perform the work. This includes AI models, enterprise data platforms, internal APIs, SaaS applications, code and browser environments, and other agents participating in the workflow.
The AI control plane connects these layers through:
- An agent and tool registry maintains visibility across the environment, while identity and credential services manage who or what is allowed to act.
- Policy decision services evaluate whether proposed actions should be allowed, restricted, or escalated. Orchestration and lifecycle services manage deployment, versioning, updates, and retirement.
- Evaluation and release controls help prevent untested agents or policies from reaching production. Telemetry and audit pipelines record agent activity, policy decisions, model calls, and tool use.
- Cost and capacity controls monitor usage, budgets, and resource consumption. Operator consoles and incident controls give teams a central place to investigate failures, suspend agents, and respond to security or compliance events.
Operational and compliance use cases
Refund and finance approvals
Consider a customer support agent who can investigate an order and issue a refund. The agent may read the order system, check delivery status, review previous refunds, and prepare a proposed resolution. The AI control plane can allow small refunds under defined conditions, require human review above a certain threshold, and block payments when customer identity or order data cannot be verified.
The control plane records which agent proposed the refund, the policy that applied, the data used, the approver, and the final transaction. The business gains faster resolution without granting the agent unrestricted access to payments.
Multi-agent research and reporting
A research workflow may use separate agents for discovery, data retrieval, analysis, fact-checking, and report generation.
The control plane orchestrates the permitted relationships among them. It can restrict research agents to approved sources, prevent sensitive internal material from being sent to public models, require citations for factual claims, and keep a record of which model and source supported each section.
Version control links the final report to the agents, prompts, policies, and datasets used to create it. This makes the output easier to review and reproduce than a document assembled from disconnected agent sessions.
Customer-support and sales agents
Customer support and sales agents often interact with CRM platforms, messaging systems, product databases, order tools, and customer records. Their permissions should depend on the channel, user, region, and business purpose.
A website support agent may view an order after customer verification, but should not have the same access as an internal account management agent. A sales agent may update an opportunity but needs approval before changing contractual terms or contacting a restricted account.
Centralized policy helps agents behave consistently across web, email, voice, and internal channels while preserving the access rules of the underlying enterprise systems.
Data and compliance operations
Data engineering teams can use agents to investigate pipeline failures, propose schema changes, generate queries, or move data between systems. The control plane can permit read-only diagnostics by default and require approval before an agent modifies production data.
In regulated industries, the same architecture can enforce residency boundaries, block unapproved model destinations, preserve audit trails, and show that human oversight occurred where policy required it.
Add as preferred sourceAI control plane scoring methodology
We compared 12 products across 22 criteria using publicly available sources, including vendor documentation, product pages, and technical posts. 19 of these criteria are included in the scores.
Criteria excluded from scoring: Deployment, enforcement model, and open-source availability are presented in the tables but are not assigned points. Deployment and enforcement models represent different architectural approaches, and the most suitable option depends on the customer’s existing infrastructure and requirements. Similarly, the importance of open-source availability varies according to each organization’s deployment, customization, and procurement needs.
Scores. ✅ = 1, Limited = 0.5, ❌ = 0. Each table is summed and normalized to 10 points so that tables with different criteria counts carry equal weight. The average equals the mean of the three.
Limitations: Vendors whose capabilities are spread across multiple products can be harder to assess as the relevant information is fragmented. The results also depend on how clearly and comprehensively each vendor describes its capabilities in publicly available documentation; limited or ambiguous information can make verification more difficult.
FAQs
An AI control plane is the management and governance layer that determines how AI models, agents, tools, and data connections may be used across an organization. It maintains the inventory, identities, policies, deployment state, telemetry, and lifecycle records needed to operate AI systems consistently.
An agent control plane is the agent-focused part of that architecture. It governs how agents are registered, authorized, deployed, monitored, updated, and retired. The broader term “AI control plane” can also encompass model access, prompt and data policies, AI gateways, evaluation services, and non-agent AI applications.
The idea comes from distributed systems. In Kubernetes, the control plane manages the desired state of a cluster, while worker nodes run the workloads. Applied to enterprise AI:
– The data plane is where agent execution happens. Agents reason, retrieve context, make model calls, invoke external tools, write outputs, and interact with enterprise systems.
– The control plane governs how that work is configured, authorized, observed, and changed.
The control plane sits above or alongside agent runtimes rather than replacing them. It may decide that a sales agent can read CRM records but cannot export a customer list, or that a finance agent may prepare a refund but must have it reviewed by a human before issuing it for amounts above a defined threshold.
This separation is important because an agent should not be responsible for deciding whether its own action is permitted. Instructions within a prompt can shape agent behavior, but they are not a reliable boundary for access control. OWASP identifies tool misuse, identity and privilege abuse, insecure inter-agent communication, and cascading failures among the main risks of agentic applications.1
Cite this research
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@misc{dilmegani2026,
author = {Dilmegani, Cem and Ermut, Sıla},
title = {{Top 12 AI Control Plane Tools for Regulated Deployments}},
year = {2026},
month = aug,
howpublished = {\url{https://aimultiple.com/ai-control-plane}},
note = {AIMultiple. Retrieved August 3, 2026}
}Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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