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09/06/2026 11:19 pm
TL;DR: Enterprises gained visibility into Snowflake Cortex AI agents, one-click control over sensitive data access, and natural-language risk analysis across Snowflake data estates as Cyera and Snowflake expanded integrations, according to Cyera. The bigger issue is not agent adoption itself, but whether security teams can govern what agents can see, touch, and exfiltrate at runtime.
NHIMG editorial — what this means for AI and NHI governance
By the numbers:
- Cyera says it discovers and classifies exabytes of data with 95%+ precision.
Questions worth separating out
Q: How should security teams govern AI agents that access sensitive data in Snowflake?
A: Security teams should treat AI agents as non-human identities with bounded data access, then enforce least privilege at the column level where queries execute.
Q: Why do AI agents complicate existing data access governance?
A: AI agents complicate governance because access is no longer just a user permission problem.
Q: How do you know if AI agent access controls are actually working?
A: You know controls are working when sensitive columns are masked or blocked at query time, agent identities are mapped to their real data scope, and access drift is visible before it becomes widespread.
Practitioner guidance
- Map every Cortex AI agent to its data-touch boundary Inventory each agent, the Snowflake identities it uses, and the sensitive columns it can reach.
- Enforce column-level least privilege natively Use Snowflake native policies and dynamic masking to restrict sensitive fields at query time, rather than relying on broad dataset permissions or downstream manual review.
- Treat AI-SPM as a continuous control Monitor agent inventory, data classification, and access drift on an ongoing basis so new agents or new data paths do not appear outside governance.
What's in the full announcement
Cyera's full research covers the operational detail this post intentionally leaves for the source:
- How Cyera's integrations map Snowflake Cortex AI agents to the sensitive records they can access.
- How one-click remediation works in Snowflake Access Governance for risky access paths.
- How natural-language risk analysis in Cortex Analyst surfaces exposure, compliance posture, and executive reporting.
- How the AI Guardian preview inventories Cortex services and maps Snowflake identities at scale.
👉 Read Cyera's analysis of AI agent visibility and data controls in Snowflake →
Snowflake Cortex AI agents: what visibility and controls change?
Explore further
10/06/2026 1:36 am
Agent visibility is now a data-governance problem, not just an AI problem. When AI agents operate inside Snowflake, the real question is which identities can reach which sensitive fields and whether those paths are visible in time to matter. That moves the control conversation from model risk into NHI and data governance, where classification, entitlement scope, and policy enforcement must line up. Practitioners should treat agent access as part of the data security plane, not a separate AI pilot.
A few things that frame the scale:
- Only 1.5 out of 10 organisations are highly confident in their ability to secure NHIs, compared to nearly 1 in 4 for securing human identities, according to The State of Non-Human Identity Security.
- That confidence gap is wider than most platform teams expect, and it shows up when AI agents begin inheriting broad data access across analytics workflows.
A question worth separating out:
Q: What is the difference between visibility and governance for AI agents?
A: Visibility tells you what an AI agent can reach. Governance tells you whether that access is classified, bounded, and enforced by policy. A platform can expose agent activity without preventing overreach, so practitioners need both inventory and runtime enforcement to claim control.
👉 Read our full editorial: Cyera and Snowflake sharpen AI agent data controls for enterprises
