Set the Foundation for Trusted AI and Data with Snowflake AI Security

This video was created and contributed by, Snowflake

Overview

Giving AI agents access to live data shouldn't mean exposing sensitive information. Snowflake AI Security builds consistent, cross-cloud guardrails around both your AI agents and your data estate. Admins can set role-specific controls in minutes, a job that usually takes months. In the demo, a frontline employee asks an agent for VIP fan contact details. Agent identity recognizes the restricted role and applies masking policies automatically, so the employee gets what they need and nothing more. When the agent then tries to export those records to an outside location, a platform-level data movement policy sees the PII tags on the underlying tables and blocks the exfiltration in real time. The attempt is logged and no data leaves the perimeter. In the Snowflake Trust Center, admins see every violation and detection, including who prompted the agent and what data it targeted, plus the AI security posture with out-of-the-box scanners watching for misconfigurations. The result is that autonomous workflows can be deployed across an organization in minutes, with less compliance work for security teams.

Key points

• Guardrails in minutes: Set role-specific controls for agents and data in minutes instead of months.
• Agent identity: Recognizes the role an agent is acting on behalf of and applies masking automatically.
• Data movement policy: Detects PII-tagged tables and blocks agent exports to external locations in real time.
• Full audit visibility: The Trust Center shows every violation, who prompted the agent, and what data was targeted.
• Posture monitoring: Out-of-the-box AI scanners watch for misconfigurations.
• Less compliance work: Security teams save weeks of manual effort on regulated workloads.