Kong’s unified control plane and the rise of AI traffic governance

At a glance

What this is: This is Kong’s argument that API, microservices and AI traffic should be governed through one unified control plane because separate tools create drift, blind spots and inconsistent policy enforcement.

Why it matters: For IAM, NHI and platform teams, the issue is not just traffic management but whether identity, policy and audit controls stay consistent as AI calls join existing API and service flows.

By the numbers:

• The global API management market was valued at USD 7.44 billion in 2024 and is projected to reach USD 108.61 billion by 2033.
• The market is projected to reach USD 108.61 billion by 2033, reflecting a CAGR of 34.7%.
• Kong cites Salt Security’s 2025 State of API Security report, which found that 95% of organizations experienced an API security incident in the past 12 months.
• Kong cites the CNCF Annual Surveys, which show Kubernetes production adoption rising from 66% in 2023 to 82% in 2025.

Context

Modern enterprise traffic now spans three distinct patterns: external APIs, internal microservices and AI or LLM requests. Each pattern introduces different identity, policy and observability demands, yet many organisations still manage them with separate gateways and control planes.

That separation creates a governance problem, not just an architecture problem. When policy, logging and enforcement drift across API, service and AI layers, teams lose a consistent view of access, cost and risk across the application estate.

Key questions

Q: How should security teams govern API, service, and AI traffic together?

A: Security teams should govern these traffic types through a shared control plane that centralises policy enforcement, observability, and auditability. The goal is consistent identity and access handling across external APIs, east-west service calls, and AI requests. Without that alignment, teams inherit policy drift and fragmented evidence that make operations and compliance harder to defend.

Q: Why does gateway sprawl create identity governance risk?

A: Gateway sprawl creates identity governance risk because each gateway often introduces its own policy language, logging model, and exception handling. Over time, the same request can be authorised differently depending on the path it takes. That inconsistency weakens accountability, complicates audits, and makes it harder to prove that controls are working uniformly.

Q: What breaks when APIs, microservices and AI calls use different control planes?

A: The main failure is that identity, redaction and policy decisions stop lining up across requests that belong to the same application flow. Teams then lose a consistent view of enforcement, which makes troubleshooting, compliance evidence and risk review much harder.

Q: When should organisations centralise control for AI traffic?

A: Organisations should centralise control for AI traffic when model access, prompt handling, or data exposure decisions are being managed by multiple teams or tools. If the same organisation must prove who accessed which model, with what content, and under what rules, a shared governance layer becomes necessary rather than optional.

Technical breakdown

Why gateway sprawl breaks policy consistency

Gateway sprawl emerges when teams enforce similar controls through separate products or configuration models for APIs, service mesh traffic and AI requests. The technical issue is not merely duplication. It is that the same authorization, rate-limiting or logging intent gets implemented differently in each stack, so policy drift appears over time. In practice, that means audit evidence, security enforcement and operational tuning no longer line up across the traffic estate. A unified control plane centralizes policy intent and reduces the chance that one traffic path becomes an exception class.

Practical implication: standardise policy expression across traffic layers so one change reaches API, service and AI paths consistently.

How AI traffic extends API governance patterns

AI and LLM calls still travel as requests over HTTP, but they introduce operational variables that standard API gateways were not designed to emphasise. Token economics, semantic caching, model selection and prompt filtering make each request both a security event and a cost event. That matters because governance is no longer only about caller identity and throttling. It also includes prompt hygiene, PII handling and routing logic across multiple providers. The architectural shift is to treat AI traffic as governed application traffic, not as an isolated AI feature set.

Practical implication: apply request-level governance to AI traffic instead of embedding provider logic and controls inside individual applications.

Why observability becomes harder across three traffic domains

When a single user flow crosses external APIs, internal services and AI backends, troubleshooting depends on correlating logs, policy outcomes and request context across systems. Separate dashboards and telemetry schemas make that correlation slow and error-prone. The deeper problem is not just visibility loss. It is that the security and compliance story for the request becomes fragmented at the same boundaries. A unified control plane creates a common audit trail and a single place to interpret identity, policy and performance signals across the full request path.

Practical implication: align telemetry, audit and tracing models before AI traffic scales further.

NHI Mgmt Group analysis

Unified control planes are becoming an identity governance requirement, not a convenience layer. When API traffic, service traffic and AI traffic are governed separately, teams end up with three different trust models for what is functionally one application estate. That fragmentation weakens policy consistency and obscures accountability. Practitioners should treat convergence as a governance design choice, not a tooling preference.

AI traffic creates a new trust boundary for NHI and workload identity governance. AI calls inherit many API patterns, but the introduction of token cost, provider switching and prompt handling means access governance now extends into request semantics. That makes the request itself part of the control surface. The implication is that IAM, API security and AI governance can no longer be planned as separate programmes.

Gateway sprawl is an identity blast radius problem. Each additional control plane expands the number of places where authN, authZ, logging and redaction can diverge. The more divergent those controls become, the larger the blast radius when a policy exception or blind spot appears. Practitioners need to think in terms of consistent enforcement domains, not product silos.

Consistent governance across APIs, microservices and AI traffic will shape the next platform standard. The market is moving toward one operational model for request handling, because teams cannot keep adding separate layers for each traffic type without creating overhead. That does not eliminate specialised controls. It means the control plane has to absorb them without fragmenting oversight. Practitioners should expect platform engineering and security architecture to converge around shared policy infrastructure.

For identity teams, the key question is where access decisions are authored versus where they are enforced. If authorisation, observability and redaction are implemented in different stacks, then governance becomes distributed in ways that are hard to certify. The practical consequence is that policy ownership must follow the traffic path, not the organisational chart.

What this signals

AI traffic governance will converge with platform governance. As organisations add AI requests to existing API and service paths, the control question shifts from whether a gateway can route traffic to whether one policy model can cover all request types without drift. That is now a platform architecture issue as much as a security issue.

Unified control planes reduce identity ambiguity across request layers. When the same policy language governs APIs, service-to-service calls and AI backends, practitioners get a cleaner enforcement model and a more defensible audit story. Fragmented traffic governance, by contrast, turns every exception into a potential control gap.

For practitioners

• Map all request paths Inventory where external APIs, internal service calls and AI or LLM requests are currently governed by different tools or teams. The goal is to identify policy drift, duplicate enforcement and audit blind spots before the architecture becomes harder to rationalise.
• Consolidate policy expression Define common rules for authentication, authorisation, throttling, logging and redaction that can be enforced across every traffic type. Keep the policy intent central even if the runtime enforcement points differ.
• Unify audit and tracing Require a single request trace that can follow a transaction from the front door API through service-to-service calls and any AI backend interaction. This is what makes cross-domain troubleshooting and compliance review practical.
• Treat AI requests as governed traffic Apply the same governance discipline to AI calls that you already expect from APIs, including identity verification, redaction, routing constraints and usage visibility. Do not leave these controls to application code alone.
• Re-evaluate platform sprawl costs Measure the overhead of operating separate gateways in terms of duplicated policy work, slower incident triage and inconsistent control outcomes. Use those findings to prioritise consolidation where the risk and operational cost are highest.

Key takeaways

• Modern enterprise traffic now spans APIs, microservices and AI calls, and separate gateways make consistent governance harder to sustain.
• The operational risk is not just complexity but drift, because policy, observability and security controls diverge when each traffic pattern is managed differently.
• Practitioners should prioritise a shared control plane and common audit model so AI traffic does not widen existing identity and policy gaps.

Key terms

• Unified Control Plane: A unified control plane is an identity architecture where discovery, access governance, audit, and response operate across humans, machines, and AI agents together. It reduces blind spots caused by siloed tooling and gives security teams context for decisions about permissions, data, and containment.
• Access Sprawl: The gradual accumulation of permissions across users, services, and integrations until no one can easily explain why access still exists. In NHI environments, it often appears when machine identities keep inherited rights long after their original business purpose has changed.
• AI Traffic: Machine-generated requests that may come from scripts, automations, or autonomous agents. In identity governance terms, AI traffic is not trusted because it is automated. It must be classified by authority, purpose, and scope before it is allowed to perform sensitive actions.
• East-west traffic: East-west traffic is communication that moves between systems inside an environment rather than entering or leaving it. In microsegmentation programmes, it is the traffic most likely to expose hidden trust assumptions and is therefore the main target for workload-level policy.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity lifecycle are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are building or maturing an IAM programme, it is worth exploring.