AI gateways centralize control for enterprise model and agent access

At a glance

What this is: This is an analysis of AI gateways as the control plane for enterprise model and agent access, with the key finding that governance should move from per-application implementation to centralized policy enforcement.

Why it matters: It matters because IAM, NHI, and emerging agentic AI programmes now need a shared way to control identity, policy, routing, and auditability across models, tools, and workloads.

By the numbers:

• Industry estimates place enterprise gateway adoption at roughly 10–25% in 2025, with sharp growth expected as multi-model and multi-agent deployments scale.
• Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap.

👉 Read Lakera's analysis of AI gateways for enterprise model and agent access

Context

AI gateways have emerged because enterprise AI no longer fits a single-provider, single-team model. As organisations connect copilots, RAG pipelines, and agentic workflows to more data and tools, the security problem shifts from model choice to control over access, routing, audit, and execution.

For identity and access teams, the key question is not whether AI can call tools, but where policy is enforced when it does. That is why the control plane discussion now sits alongside NHI governance, workload identity, and broader IAM design rather than inside application teams alone.

Key questions

Q: How should security teams govern access to AI models and tools?

A: They should govern AI access through a central control plane that applies identity, policy, budget, and logging rules before requests reach models or tools. That approach reduces duplicated controls across applications and gives security one place to enforce consistent entitlements, review denials, and track usage across teams.

Q: Why do AI gateways matter for IAM and NHI programmes?

A: They matter because AI systems increasingly behave like non-human consumers of policy, data, and tools. IAM and NHI teams need a way to control those requests consistently, especially when multiple teams, providers, and workloads are involved. The gateway becomes the point where identity context is translated into enforceable access decisions.

Q: What breaks when AI tool access is controlled only inside each application?

A: Policy drift breaks first, followed by inconsistent logging, duplicated permission logic, and uneven provider integration. Each team ends up implementing its own version of guardrails and access checks, which makes security reviews slower and governance harder to prove. Centralised enforcement avoids that fragmentation.

Q: How do organisations decide whether an AI gateway is necessary?

A: They should assess whether they have multiple providers, multiple teams, regulated data, tool execution, or complex audit requirements. If those conditions are present, provider-native controls usually stop being enough. The gateway becomes the architectural layer that keeps AI adoption governable as usage expands.

Technical breakdown

AI gateway as the control plane for model and agent access

An AI gateway sits between applications, agents, and upstream models or tools, and enforces policy at the request layer. It can inspect inputs, apply identity-based rules, normalise provider APIs, attach budgets, and log activity before the request reaches a model or external service. The architectural point is separation of concerns: applications stay focused on business logic while governance is centralised in one enforcement layer. That is why gateways are increasingly treated as infrastructure rather than a feature inside each AI application.

Practical implication: define gateway policy as a shared control plane, not as optional app code.

Identity, virtual keys, and scoped access in AI systems

AI gateways often issue scoped virtual keys or mediate access using identity context, which makes them relevant to both IAM and NHI governance. The real control value is not just authentication but the ability to constrain which models, tools, and data paths an identity can use. In practice, this is closest to least privilege at the execution layer. Without that layer, teams tend to duplicate authentication, permission checks, and audit logic across every AI service, which creates inconsistent enforcement and policy drift.

Practical implication: align AI access paths with identity policy, not with per-team shortcuts.

Tool execution, MCP, and policy enforcement boundaries

As agents begin calling search tools, CRMs, code runners, and data services, the gateway becomes the chokepoint that decides whether the call is allowed and whether it is visible to security. The article’s MCP discussion is important because standardised tool exposure does not equal safe tool use. A gateway can restrict tool sets, apply rate limits, and log invocations, but it does not replace the permission model inside the tool or data system itself. That boundary matters for architecture and accountability.

Practical implication: keep document and data permissions inside the target system, and use the gateway for request governance.

NHI Mgmt Group analysis

AI gateway governance is the emerging control plane for non-human and agentic access. The article shows that enterprise AI has crossed from isolated experimentation into shared infrastructure, which means identity control must move closer to execution. That shift aligns with OWASP-NHI and zero trust thinking because access, routing, and logging are now enforced on the path to models, tools, and data. Practitioners should treat the gateway as a governance boundary, not just a traffic layer.

Virtual keys are useful only when they reduce standing access, not when they hide it. Scoped keys and central policy can improve visibility, but they do not solve the underlying issue if applications still retain broad downstream permissions. The important distinction is between centralised issuance and actual privilege reduction across tools and data paths. Teams should evaluate whether the gateway narrows entitlement or merely repackages it.

AI gateway adoption signals that identity teams are being pulled into AI architecture decisions earlier. Once routing, model selection, budgets, and audit logs are centralised, IAM, PAM, and NHI governance can no longer be treated as downstream review steps. That makes the gateway a bridge between traditional access management and AI operating models. Practitioners should expect governance ownership to move from experimentation teams into security and identity governance.

Control at the execution layer is now the differentiator between governance theatre and enforceable policy. If policy is only written into individual applications, it will drift as teams add providers, tools, and agents. If it is enforced centrally, the organisation gets a repeatable control pattern across environments. That is the real market signal here: enterprise AI is becoming governable only when access control becomes architectural.

For autonomous systems, the gateway is part of the trust chain, not the trust answer. Even when an AI system is not fully autonomous, the moment it can select tools and trigger actions, the control problem expands beyond model access. That is why identity governance, NHI oversight, and execution-time policy need to be designed together. The practitioner takeaway is to map where decision authority actually sits before deciding where to enforce controls.

From our research:

• Companies are dedicating an average of 32.4% of their security budgets to secrets management and code security, with US organisations leading at 40.8%, according to The State of Secrets in AppSec.
• Only 44% of developers are reported to follow security best practices for secrets management, exposing a significant developer behaviour gap, according to The State of Secrets in AppSec.
• For the broader identity picture, see NHI Lifecycle Management Guide for how governance changes once identities, credentials, and access paths must be managed continuously.

What this signals

AI gateways are becoming the practical boundary where IAM, NHI, and application security finally meet. As enterprises standardise access to models and tools, the governance challenge shifts from who can log in to what identities can do at runtime. With only 44% of developers following secrets-management best practices in our research, the operational gap is already visible before AI workloads scale further.

Virtual keys are a signal of maturity only when they narrow the blast radius. If the gateway issues scoped access but downstream systems still accept broad permissions, the organisation has simply moved the problem rather than solved it. That is why gateway programmes should be reviewed alongside Ultimate Guide to NHIs , Lifecycle Processes for Managing NHIs and identity lifecycle controls.

Control-plane thinking will soon be the default architecture for enterprise AI governance. Teams that still treat AI security as application-specific configuration will struggle to prove policy consistency, audit coverage, and least privilege across providers. The governance model now needs to span access control, tool permissions, telemetry, and lifecycle management in one operational view.

For practitioners

• Centralise AI access policy in one enforcement layer Define which models, tools, and data sources each business function may use, and apply those rules in the gateway rather than inside every application. Keep authentication, budgets, logging, and routing decisions consistent across environments.
• Bind AI requests to identity context and scoped entitlements Pass identity context through the gateway so downstream systems can evaluate role, purpose, and policy before allowing model calls or tool execution. Use the gateway to issue scoped virtual keys only for the minimum request path required.
• Separate gateway governance from data-layer permissions Use the gateway to control request flow and tool invocation, but retain document-level ACLs, row-level policies, and search permissions inside the destination system. This avoids false confidence that the gateway alone can enforce data boundaries.
• Build auditability around model and tool execution Require unified logs for provider selection, tool calls, budget usage, and policy denials so security can reconstruct what the AI system did. Without that record, investigations rely on incomplete application logs and missed context.

Key takeaways

• AI gateways matter because they centralise policy, identity context, routing, and auditability for models and agents that would otherwise be governed inconsistently.
• The main risk is not AI itself but fragmented enforcement, where every application rebuilds access and logging controls differently.
• Practitioners should treat the gateway as an architectural control plane and verify that it narrows, rather than merely repackages, downstream privilege.

Key terms

• AI Gateway: A central enforcement layer that controls how applications, agents, and RAG workflows access models, tools, and data. It applies identity, policy, routing, budgets, and telemetry in one place so governance does not have to be rebuilt inside every AI application.
• Scoped Virtual Key: A limited-purpose credential issued for a specific AI request path or workload. In practice, it reduces standing access by constraining what the system can call, for how long, and under which policy conditions, but only if downstream permissions are equally narrow.
• Execution-layer Control: A control applied at the moment a system makes a request, calls a tool, or triggers an action. For AI systems, this is where identity, authorization, and logging must intersect if security wants to govern behaviour rather than just login events.

What's in the full article

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

• Request-by-request policy enforcement patterns for enterprise AI gateways
• How routing, failover, and budget constraints are implemented across multiple model providers
• Practical guidance for tool invocation governance in agent and RAG workflows
• Where gateway controls stop and data-system permissions still need to be enforced

👉 Lakera's full article covers the gateway architecture, tool governance, and execution-layer controls in more detail.

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 responsible for identity security strategy or governance in your organisation, it is worth exploring.