Multicloud gateway state management needs regional shared caches

At a glance

What this is: This is a multicloud gateway deployment guide showing how Kong uses managed Redis to add regional shared state for policy enforcement, caching, and AI token governance.

Why it matters: It matters because identity and access teams increasingly have to govern distributed gateway state, quota counters, and policy consistency across clouds, not just authenticate traffic at the edge.

👉 Read Kong's guide to multicloud gateway configuration with managed Redis

Context

Multicloud gateway architectures fail when policy is centralized but the state behind policy is not. If counters, caches, and session context are scattered across regions, enforcement becomes inconsistent even when the gateway configuration itself is correct.

For IAM and NHI teams, that makes shared state part of the identity control surface. Rate limits, token accounting, and request governance now depend on how state is stored, replicated, and accessed across clouds, especially when AI services and agents are in the path.

Kong’s configuration guide is a practical example of this pattern in operation. It is typical of the direction enterprise platforms are taking as application traffic, AI workloads, and regional data residency requirements all collide.

Key questions

Q: How should security teams govern shared state in multicloud gateways?

A: Security teams should first identify which controls depend on shared state, such as rate limiting, session management, and token accounting. They should then place that state close to the gateway that consumes it, validate consistency across regions, and test whether policy decisions remain stable when one cloud is degraded or delayed.

Q: Why does multicloud architecture make gateway governance harder?

A: Multicloud increases the number of places where policy can drift and state can diverge. A gateway may enforce the same rule everywhere, but if its counters, caches, or request context are not synchronized, the actual decision can differ by region. That makes consistency a governance problem, not only an infrastructure problem.

Q: When should organisations use regional caches instead of a global cache?

A: Use regional caches when the control depends on low latency or when the gateway must make local decisions that cannot tolerate cross-cloud round trips. Regional caches are especially useful for rate limits and AI usage counters. A global cache is only appropriate if the application can tolerate extra latency and tighter coordination overhead.

Q: How do AI gateways change identity and access governance?

A: AI gateways turn request governance into a runtime identity problem because token quotas, routing rules, and filtering decisions can change by session and region. Teams need to manage the state behind those decisions, not just the access policy itself, or they will lose visibility into how agentic or model traffic is actually controlled.

Technical breakdown

Why shared state matters in multicloud gateways

A multicloud gateway can enforce policy locally, but it still needs a common view of state to make decisions that are consistent across regions. Shared state covers counters, cache entries, session context, and quota records. Without it, one region may allow traffic that another region would block, which undermines rate limiting, AI token accounting, and response caching. In distributed identity terms, the gateway is only as reliable as the state it can trust at decision time.

Practical implication: design gateway policy with state locality and consistency requirements defined up front.

Managed Redis as a regional control layer

Managed Redis gives gateways a fast, regional persistence layer for short-lived operational data. Because Redis is placed close to each gateway cluster, it supports low-latency access for rate limit counters, cache lookups, and AI usage tracking. This is not a replacement for long-term data stores. It is an enforcement adjunct that keeps control decisions responsive while avoiding the cost and complexity of running separate cache infrastructure per environment.

Practical implication: use regional caches for enforcement data that must stay close to the gateway path.

Global policy, local enforcement, and AI token governance

Kong’s model separates control from execution. Policies are defined in Konnect once, then synchronized to gateways running in each cloud, while traffic enforcement happens locally. That pattern is especially relevant for AI gateways because token quotas, model routing, and request filtering often need shared governance with region-specific execution. In practice, this creates a hybrid identity model where central rules depend on distributed state to remain meaningful.

Practical implication: validate that AI quota and routing controls still behave correctly when enforcement is distributed.

NHI Mgmt Group analysis

Multicloud gateway governance now depends on state, not just policy. A centrally managed gateway can only enforce identity and traffic rules consistently if the state behind those rules is synchronized across regions. That shifts the security conversation from configuration alone to the reliability of counters, cache entries, and session data. Practitioners should treat distributed state as part of the control plane, not as an implementation detail.

Regional shared state is becoming the practical boundary for AI token governance. AI gateways do not fail only when authentication is weak. They fail when token counts, quotas, and request context diverge between clouds and enforcement becomes non-deterministic. That is a growing governance problem for NHI and agentic workloads because runtime behaviour depends on live state, not static entitlement alone. The implication is that AI governance must be measured at the state layer as well as the policy layer.

Identity blast radius is now shaped by cache placement and replication design. When gateway state is regional, a compromise or misconfiguration in one environment does not automatically spread everywhere, but a poor state model can still create inconsistent enforcement across clouds. This is the same governance lesson seen in machine identity and workload identity programmes. Practitioners should think in terms of where trust decisions are made and where they are persisted.

Central policy with local execution is the right multicloud pattern, but only if the shared state is disciplined. Konnect-style configuration consistency reduces drift, yet it does not solve runtime divergence on its own. The field should read this as evidence that multicloud identity governance is moving toward distributed enforcement with centralized intent. Security teams need to re-evaluate whether their current models can survive when identity decisions are stateful and regional.

From our research:

• 35.6% of organisations cite managing consistent access across hybrid and multi-cloud environments as their top NHI security challenge, according to The 2024 Non-Human Identity Security Report.
• 88.5% of organisations acknowledge that their non-human IAM practices lag behind or are merely on par with their human identity and access management efforts.
• That gap is why teams should pair regional enforcement with lifecycle governance, and the NHI Lifecycle Management Guide is the right next reference.

What this signals

State consistency is becoming a first-class IAM requirement in multicloud operations. As gateway policy spans AWS, Azure, and GCP, the meaningful control question is no longer only who may access a service, but where the decision state lives and how quickly it converges. That is why the operational boundary for identity governance is moving closer to the data plane.

Multicloud programmes should now measure enforcement drift, not just policy coverage. If the same request yields different outcomes across regions, the organisation has a governance defect even when its policy files are identical. Teams that already struggle with consistent access across hybrid and multi-cloud environments should treat cache placement, quota state, and regional failover as identity controls, not platform trivia.

For practitioners

• Map every gateway policy to its required state dependency Document which controls rely on counters, cache entries, sessions, or token usage data so you can see where regional state is mandatory and where stateless enforcement is sufficient.
• Place enforcement data in the same region as the gateway Keep rate limiting, cache, and quota state close to the data plane that consumes it to avoid latency spikes and inconsistent decisions across clouds.
• Test policy drift across cloud regions Simulate the same request path in AWS, Azure, and GCP to confirm that centralized policy produces the same outcome when regional caches and counters are involved.
• Treat AI token quotas as identity governance controls If gateways front model providers or agent orchestration platforms, classify token accounting and request filtering as governance mechanisms, not just cost controls.

Key takeaways

• Multicloud gateway governance breaks down when shared state is inconsistent, even if the policy itself is centrally managed.
• Regional caches, quota counters, and session data now sit inside the identity control surface for API and AI gateways.
• Practitioners should test for policy drift across regions and treat state placement as a core design decision, not an optimisation.

Key terms

• Shared State: Shared state is the operational data that multiple gateway instances consult to make consistent decisions, such as counters, cache entries, and session context. In multicloud environments, it becomes part of the enforcement path because policy can only behave reliably if the underlying state is current and trusted.
• Regional Cache: A regional cache is a cache deployed close to the gateway or workload that uses it, rather than in a single global location. It reduces latency and supports local decision-making, but it also requires careful governance so regional divergence does not create inconsistent access outcomes.
• Token Governance: Token governance is the set of controls used to limit, track, and shape AI token usage across systems and sessions. It covers quotas, counters, routing rules, and filtering. In gateway architectures, it depends on reliable state as much as on the policy definition itself.

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.

This post draws on content published by Kong: Configuring Kong Dedicated Cloud Gateways with Managed Redis in a Multi-Cloud Environment. Read the original.