Apono Partners with Databricks to Govern Privileged Access Across the Lakehouse

Databricks has become the data and AI platform of record for a large and growing share of the enterprise market. Unity Catalog gives teams unified governance over data assets, AI models, and agentic workflows, controlling what exists, who can use it, and what policies apply. But there’s a layer sitting underneath all of that governance which Unity Catalog was never built to address: the standing privileged access that engineers, data scientists, and admins hold to reach the environment in the first place.

Apono is partnering with Databricks to close that gap. The integration brings Just-in-Time (JIT) and Just-Enough-Access (JEA) provisioning to Databricks Groups, giving joint customers a way to eliminate standing privileges across their lakehouse without adding friction for the teams who work in it every day.

The problem: standing access underneath a governed platform

Engineers who configure Databricks environments, manage clusters, and support data pipelines typically hold standing access to workspace admin roles and cloud IAM credentials. Those credentials persist regardless of what task someone is actually performing, which means the access outlives the reason it was granted.

AI agents only compound the problem. As agents proliferate inside Databricks environments, they inherit the same standing access pattern at scale: service accounts and IAM roles with broad, persistent permissions that no governance layer is actively revoking. Unity AI Gateway governs the model and tool layer, while the infrastructure access underneath it remains largely ungoverned.

How it works together

Apono connects to Databricks and continuously discovers Groups as they’re created, making them available inside Apono’s access workflows. A data analyst who needs access to a table containing customer PII can request it through Slack, Teams, Jira, or the CLI, get provisioned into the right group automatically, and have that access revoked after an hour, meaning no standing memberships, manual tickets, or forgotten grants.

Every request carries a complete record: who asked, what they asked for, who or what approved it, when it was granted, how long it lasted, and a full log of what happened during that window. When the session ends, access is revoked automatically.

The same model extends to AI agents operating inside Databricks. Access is scoped to the agent’s immediate task and monitored in real time. If an agent’s actions deviate from what it declared it needed to do, Apono blocks the action or routes it to a human for approval before anything executes.

That split of responsibility is the core of the partnership: Databricks governs the assets and the AI interactions running on top of them, and Apono governs the privileged access used to reach them, with the request history and audit trail to prove it.

Part of 1Password Unified Access

Going forward, Apono will be part of the 1Password Unified Access platform alongside two other products that further enhance the strength of the Databricks integration:

• 1Password Enterprise Password Manager stores and manages the credentials your people use.
• 1Password Credential Broker delivers secrets to workloads at runtime.
• Apono (aka 1Password Privileged Access) governs what those identities are permitted to do in the systems they connect to, when, and for how long.

For an organization with a full privileged-access mandate, the three work together to cover the credential, its runtime delivery, and the access rights in the target system. For teams already standardized on 1Password, extending that same trust model to Databricks means one trusted vendor for identity and access across people, machine workloads, and AI agents, without ripping and replacing existing IAM infrastructure.

What this means for customers on both sides

Any organization running Databricks at scale has already invested in governing their data, which means the security and compliance stakeholders are already engaged and the privileged access risk is concentrated in a place everyone can see. Layering Apono on top turns that existing governance investment into a complete access story: right-sized permissions, automatic revocation, and audit logs that turn access reviews into a report instead of a research project.

Organizations get:

• Granular, resource-level access: Requests are scoped to the specific Databricks Group a person or agent actually needs, not the broadest role that happens to be available.
• Operational speed: Requests that used to sit in a queue for a platform or security team to review get resolved automatically, based on policy, through the tools engineers already use.
• A single governance model for every identity: The same Zero Standing Privilege enforcement applies whether the requester is a data engineer, a data scientist, or an autonomous agent running a pipeline job.

Getting started

Integrating Apono with Databricks takes three steps: connect Apono to your Databricks environment, build an access workflow around your existing Groups, and let your team start requesting access on demand. Full setup details are in the Apono docs for Databricks.

If you’re running Databricks today and want to see what Zero Standing Privilege looks like across your lakehouse, book a demo with the Apono team.