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23/08/2026 3:39 pm
TL;DR: Snowflake Summit 2026 underscored the shift to the Agentic Enterprise, but Trust3 argues that governance breaks down when agents, tools, and data span multiple environments rather than living inside one platform. The practical issue is not model choice alone, but enforcing least privilege, auditability, and policy continuity across multi-agent workflows.
NHIMG editorial — based on content published by Trust3: Unifying policy-driven governance and Snowflake MCP for the multi-agent, A2A future
By the numbers:
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes.
Questions worth separating out
Q: How should security teams govern AI agents that reason across multiple data platforms?
A: Security teams should govern the meaning layer, not just the access layer.
Q: Why do AI agents make non-human identity governance harder?
A: AI agents make governance harder because they can request tools, act autonomously, and change behaviour across sessions while still relying on machine credentials.
Q: What breaks when policy only exists inside one AI platform?
A: When policy only exists inside one AI platform, access control breaks as soon as an agent leaves that boundary.
Practitioner guidance
- Define agent identities separately from workload identities Assign each AI agent its own scoped identity, and avoid reusing the same service account or token across workflows, tools, and environments.
- Enforce runtime policy on every cross-tool call Require access checks at the moment an agent requests a tool, dataset, or connector, and record the decision with the action.
- Separate delegated access from human session access Do not let human authentication implicitly inherit into agent actions.
What's in the full article
Trust3's full analysis covers the operational detail this post intentionally leaves for the source:
- Integration detail for Snowflake-managed MCP and what it changes for policy enforcement inside that ecosystem
- How Trust3 models data-product-centric governance across Snowflake, Databricks, and lakehouse environments
- The architecture implications of agent-to-agent communication when context moves between systems
- The vendor's view of dynamic policy enforcement and unified audit trails in multi-model workflows
👉 Read Trust3's analysis of policy-driven governance for Snowflake MCP and multi-agent AI →
Multi-agent AI governance beyond one platform: what teams need now?
Explore further
23/08/2026 3:40 pm
Multi-agent AI governance is becoming a non-human identity problem. Once agents can pass context and invoke tools for one another, the question is no longer only model safety. It becomes identity, scope, and revocation across a chain of software actors. That is why IAM and NHI teams need to treat agents as governed identities, not just application features. Practitioners should design controls around delegated purpose and lifecycle, not around a single model endpoint.
A question worth separating out:
Q: How do organisations know if agent governance is actually working?
A: Agent governance is working when every agent is discoverable, owned, least privileged, and auditable at the action level. Look for reduced shadow AI, fewer embedded secrets, clean revocation on retirement, and logs that show which tools and data paths were used. If those signals are missing, governance is still partial.
👉 Read our full editorial: Policy-driven governance for multi-agent AI needs cross-boundary control
