Omni Governance Control Plane

Replace disjointed standalone monitoring tools. The AegisMesh Omni Governance Control Plane brings native Kafka management, PII data exploration, drift tracking, and cost optimization under a single dashboard.

Continuous Drift Detection

AegisMesh continuously synchronizes your live physical infrastructure against your declarative GitOps state. It instantly flags severe security and operational drifts, including:

  • Shadow ACLs: Detects undocumented Kafka users bypassing the mesh proxy Spiffe IDs.
  • Configuration Mismatches: Flags physical topic retention policies that deviate from your Kubernetes CRDs.
  • Missing Schemas: Identifies physical topics actively missing an Avro/Protobuf value schema.
  • Failed Connect Tasks: Alerts when Kafka Connect tasks crash despite the connector status reporting as running.
  • Orphaned DLQs: Flags dead-letter topics where the parent connector has been deleted.

Data Explorer & PII Guardrails

Allowing developers raw access to production message brokers is a massive compliance risk. The Omni Control Plane provides a secure, role-based Data Explorer.

Kafka messages are dynamically deserialized. If a user lacks the ROLE_SECURITY_ADMIN or ROLE_PII_VIEWER scope, the engine automatically intercepts and redacts sensitive JSON nodes including SSNs, Credit Cards, Passwords, and Emails (replaced with ****-REDACTED) before rendering the payload.

Consumer Rewind Time-Travel

Incident recovery often requires resetting Kafka offsets, which traditionally demands privileged CLI broker access. AegisMesh offers self-service "Time-Travel".

Operators can specify a target timestamp and instantly rewind specific Consumer Group offsets securely through the mesh. The platform natively verifies that consumer groups are in an EMPTY or DEAD state before allowing mutations, preventing offset conflicts with active applications.

Automated Topic Recommendations

Stop paying for wasted cloud storage. AegisMesh analyzes your cluster topography to provide actionable cost-saving recommendations. It actively flags:

  • Unused or orphaned physical topics.
  • Developer leftovers (test topics ending in UUIDs).
  • Over-partitioned Kafka Streams or Dead Letter Topics (DLTs).
  • Changelog topics missing required compaction policies.