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22/08/2026 12:01 pm
TL;DR: Enterprises lack a single view of agent behaviour as Unity AI Gateway governs agents inside Databricks but cannot extend across Snowflake, Bedrock, Copilot Studio, CrewAI, or in-house systems, according to Trust3. The core issue is that access review and static authorization assume stable, reviewable privilege, while agents need just-in-time, task-scoped control and revocation tied to runtime purpose.
NHIMG editorial — based on content published by Trust3: Unity AI Gateway can't govern every agent because it only reaches as far as Databricks
Questions worth separating out
Q: How should security teams govern AI agents that can access enterprise systems?
A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring.
Q: Why do native agent controls fail in multi-cloud environments?
A: Native controls are usually scoped to the platform that hosts the agent, so they cannot consistently govern identity, purpose, or tool use outside that boundary.
Q: What breaks when agent access is not scoped to the task?
A: Standing privilege becomes the main failure mode.
Practitioner guidance
- Define an enterprise agent control plane Map every platform where agents run, then decide which layer owns discovery, policy evaluation, and revocation across the full estate.
- Bind agent grants to task completion Issue access with an expiry tied to the task, not the account lifecycle, and revoke it the moment the declared work is complete.
- Separate observation from authorization Capture prompts, retrievals, and tool calls for audit, but reserve the live decision for a control path that can block, mask, or approve in real time.
What's in the full article
Trust3's full analysis covers the operational detail this post intentionally leaves for the source:
- Platform-specific examples of how Unity AI Gateway governs Databricks agents and where that enforcement stops.
- The full discover, observe, secure workflow as implemented across agent registries, runtime traffic, and tool calls.
- Decision criteria for fail-static enforcement when a control plane is unreachable, including revocation paths and policy caching.
- The exact questions Trust3 says leaders should ask before approving vendor-neutral agent governance.
👉 Read Trust3's analysis of Databricks-bound AI agent governance limits →
AI agent governance across platforms: are native controls enough?
Explore further
22/08/2026 12:02 pm
Vendor-neutral agent governance is becoming the only workable enterprise model. Native controls can protect agents inside a single platform, but they do not solve multi-platform identity and access governance. Enterprises now run agents across cloud services, SaaS applications, and internal systems, so governance that stops at the vendor boundary is incomplete by design. The practical conclusion is that the control plane must be independent of the framework carrying the agent.
A few things that frame the scale:
- The average estimated time to remediate a leaked secret is 27 days, despite 75% of organisations expressing strong confidence in their secrets management capabilities, according to The State of Secrets in AppSec.
- Only 44% of developers are reported to follow security best practices for secrets management, which helps explain why governance often lags engineering reality.
A question worth separating out:
Q: Who is accountable when an AI agent acts outside its intended scope?
A: The organisation is accountable, but operational responsibility should sit with a named owner and a governance process that can explain the agent’s purpose, access, and recorded actions. Without that, autonomous behaviour becomes unassignable risk rather than managed automation.
👉 Read our full editorial: Vendor-neutral control planes for AI agents expose Databricks limits
