Guardrails, not gatekeepers:

The standard approach for Kafka in enterprise environments is to lock it down and have engineers file tickets. Platform teams do that because traditional engineering tools (CLIs, UIs and native ACLs) have no enterprise governance, so you cannot grant access you can scope, revoke or prove later.

That’s the Kafka ticket desk: the week is access requests instead of platform work, engineers wait days to ship and Kafka adoption scales only as fast as the ticket queue moves.

In this session, we take a request you would currently ticket and put a unified self-service governance layer in front of it. You keep the policy: enterprise authentication, granular IAM and one access model across the estate. Engineers can see metadata for the whole Kafka estate, but only the topics, payloads and application lineage that they are permitted to see. Sensitive fields are masked in place, every operation is audited and that holds across the entire estate, not one cluster at a time.

The same authentication and IAM model is future-proofed for AI agent access via Lenses MCP server.

Join the session and you’ll walk away with:

• What developer self-service looks like day to day: restarting a connector, resetting a consumer group offset, reading topic data with PII masked in place, inspecting a schema.
• How to give engineers scoped Kafka access (granular IAM, auditable, unified access) without adding platform headcount.
• A process for safely delegating that access to engineering AI agents: scoped permissions, inherited data masking and an audit trail showing what the agent did on whose behalf.
• The metrics that matter: time-to-first-topic, not reduced ticket volume.