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  • IT concepts

High Availability (HA)

High Availability

High Availability (HA) is a system design approach that ensures a specific degree of operational continuity during a given time period, typically measured in uptime percentages such as 99.9% or 99.99%. In workload identity and access management systems, HA ensures that authentication, authorization, and credential issuance services remain accessible even during infrastructure failures, network disruptions, or regional outages.

How It Works

High availability in identity and access management platforms operates through multi-region deployment with redundant components distributed across geographically separated data centers. Load balancers continuously monitor health status and route traffic away from failed instances. Automated failover mechanisms detect outages and redirect traffic to standby systems without manual intervention, with production implementations achieving failover detection in seconds. For example, HashiCorp Vault standby servers poll active node status every 2.5 seconds for failure detection.

In workload IAM systems specifically, HA implementation requires separating the control plane (which manages policies and configuration) from the data plane (which performs authentication and authorization). This separation allows authentication to continue during control plane maintenance or failures. Health check endpoints verify not just service availability but also the correctness of credential issuance and policy enforcement. For secretless architectures using just-in-time credential issuance, local credential buffering caches recently retrieved credentials to maintain availability during temporary network disruptions.

Why This Matters

Authentication and authorization infrastructure failures have disproportionate business impact because they affect multiple downstream services simultaneously. When identity systems go down, applications cannot verify users or workloads, APIs cannot authenticate requests, and automated processes cannot access resources. In cloud-native environments where workloads communicate machine-to-machine at scale, even brief authentication outages can cascade into widespread service disruption.

For enterprises deploying AI agents and hybrid workloads, high availability becomes critical because these systems operate autonomously without human intervention to retry failed operations. An AI agent that cannot authenticate to retrieve training data or API credentials will simply fail its assigned task. Hybrid workloads spanning on-premises data centers, multiple cloud providers, and edge locations require identity systems that maintain availability despite regional cloud provider outages or network partitions between environments.

Major cloud providers establish availability commitments ranging from 99.9% to 99.99% for their identity services. These translate to annual downtime of approximately 8.76 hours (99.9%), 4.38 hours (99.95%), and 52.56 minutes (99.99%). Security frameworks including NIST SP 800-53 Revision 5 (particularly the CP Family and controls SC-5, SC-24), SOC 2 Trust Services Criteria (Availability Criterion with five points of focus: data backups, disaster recovery planning, business continuity planning, redundancy, and capacity planning), and ISO 27001:2022 Annex A Control 5.30 mandate specific controls for maintaining availability of security-critical systems through redundancy, business continuity planning, and regular failover testing.

Common Challenges with High Availability (HA)

Identity State Consistency

High availability in IAM systems must balance security correctness with continuous operation. During outages or partitions, enforcement points often rely on cached policy decisions, an approach NIST SP 1800-35 highlights as necessary for availability but risky for revocation latency. Longer cache TTLs improve uptime but allow revoked identities or updated policies to remain valid until caches expire.
Different IAM architectures reflect this tension. Cloud providers use control-plane / data-plane separation, combining centralized consistency with regionally distributed availability. Systems like SPIFFE/SPIRE favor horizontal scaling with eventual consistency, while Vault uses an active-standby model that provides strong consistency at the cost of failover delays. When a workload is compromised, these models determine whether revocations propagate instantly (strong consistency) or with delay (eventual consistency). Policy enforcement points, whether cloud-native or third-party, must preserve decision-making during outages, but cached decisions always introduce a consistency–availability trade-off.

Failover and Short-Lived Credentials

Dynamic, short-lived credentials tighten HA requirements. If a control plane fails longer than the credential’s TTL, workloads cannot refresh secrets and authentication breaks. Systems like Vault must fail over faster than the shortest issued TTL to prevent outages. Eventual-consistency architectures avoid write unavailability but may serve stale lease or revocation state until replication completes. In both models, lease tracking and automatic revocation must remain consistent through failover, or credentials can outlive intended lifetimes, creating both availability and security risks.

Data Residency Constraints

Regulations such as GDPR and industry-specific sovereignty rules restrict where identity data can reside, limiting HA options. Multi-region architectures typically rely on geographic distribution, but many organizations must keep IAM state within a single legal region. This forces regional rather than global redundancy, e.g., active-active deployments across zones within the EU instead of worldwide distribution. While compliant, these designs remain exposed to large regional outages and require tighter engineering around local resiliency.

Network Partitions and Split-Brain

Distributed IAM systems must withstand situations where regions lose connectivity with each other but remain reachable by clients. Without safeguards, multiple components may believe they are primary, causing inconsistent authentication state. HA designs rely on quorum-based consensus, witness services, or strict leader election to avoid split-brain. Because IAM demands consistent decisions everywhere, architectures must monitor replication lag, limit blast radius (e.g., cell-based isolation), and ensure failover completes within credential TTL windows to avoid authentication gaps.

Monitoring and Observability

HA depends as much on visibility as on redundancy. Organizations must track not only node health but replication lag, cross-region latency, token issuance correctness, and revocation effectiveness. Many outages arise not from failure but from partial degradation that monitoring does not catch. Mature IAM operators conduct regular failover drills, maintain documented playbooks, and verify revocation behavior during simulated outages. Without this operational discipline, even robust infrastructure cannot reliably achieve high availability.

How Aembit Helps

Aembit provides enterprise-grade high availability through multi-region AWS deployment with automatic failover across both availability zones and regions. The platform implements health-based routing that dynamically shifts front-end and back-end services to unaffected zones during localized failures, minimizing the impact of geographic disruptions without manual intervention. This architectural approach ensures that workload authentication and credential issuance remain available even during infrastructure failures or regional cloud provider outages.

The Agent Controller component eliminates single points of failure by deploying multiple controller instances behind highly available TCP load balancers. Health monitoring continuously checks the /health endpoint, and traffic automatically routes to healthy instances during failures. Organizations can run multiple controller and proxy deployments simultaneously in both primary and disaster recovery environments to support active-active redundancy patterns, providing flexibility in balancing availability requirements against operational complexity.

Aembit Edge components implement local credential buffering that caches recently retrieved credentials locally, mitigating the impact of temporary network disruptions to Aembit Cloud (as documented in Aembit’s Building Trust page). This design preserves workload authentication capability during brief connectivity issues while maintaining the security benefits of short-lived credentials. The platform implements segmented control and data planes with encrypted databases and hardened applications, which enhances fault isolation by separating policy management operations from credential issuance processing.

Health monitoring capabilities include a standardized health check API endpoint returning detailed status information, a public status page at status.aembit.io with historical uptime data, and support for Prometheus-compatible metrics enabling real-time monitoring and alerting. The platform maintains ISO 27001 and SOC 2 Type 2 certifications, providing independent validation of availability controls and operational processes.

Related Reading

FAQ

You Have Questions?
We Have Answers.

What happens to authentication during an IAM system failover?

Behavior depends on architecture. In active-passive systems like Vault, failover detection (about 2.5–5 seconds) can cause brief authentication errors until the standby becomes active. Workloads may continue using cached credentials, but new credential issuance is unavailable during the transition. Active-active architectures route traffic to healthy nodes automatically, minimizing interruption. To avoid outages, failover time must remain shorter than the shortest credential TTL, and clients should implement retry logic with exponential backoff.

Active-active systems serve requests concurrently across replicas, offering high availability but requiring conflict resolution for policy updates and consistent handling of revocation and session state across regions. Active-passive architectures simplify consistency by designating a single writer with hot standbys but introduce short failover windows and higher idle resource cost. For authentication, where strong consistency matters, active-passive with fast failover is often preferred. Authorization systems that perform read-heavy policy evaluation can safely use active-active with cached or eventually consistent reads, aligning with NIST Zero Trust guidance that differentiates strong-consistency requirements for credential issuance versus tolerance for cached policy decisions at enforcement points.

Multi-AZ architectures do not protect against regional outages, as seen in the 2011 AWS US-East-1 failure where multiple AZs went down simultaneously. Modern identity platforms mitigate this risk by deploying across independent regions or even clouds, e.g., Auth0’s regional failover patterns and Okta’s cell-based distribution. IAM is a global dependency; regional redundancy alone cannot guarantee authentication continuity.

 

Identity systems must fail secure, not degrade functionality. Controls like NIST SP 800-53 SC-24 require that authentication never bypass or loosen verification during failures. Short-lived credentials tighten this requirement: if failover exceeds credential TTL, authentication fails outright. Unlike stateless web applications, IAM combines credential freshness, revocation correctness, and distributed policy enforcement, making HA both more complex and less tolerant of partial failure.