Snowflake’s Agentic Future Puts Security at the Centre Snowflake Summit 26 framed the rise of autonomous AI agents as a governed, auditable evolution anchored in enterprise data controls. Across keynotes, Snowflake executives stressed that AI agents should run where trusted data lives, operate under explicit identities and collaborate across organisations without leaking personal identifiable information (PII). Why it matters for cybersecurity AI agents are rapidly moving from experimental pilots into active production. As a result, security leaders will soon be accountable for the data access, actions and audit trails of these autonomous systems. Running models where the data is naturally reduces data exfiltration risks and simplifies overarching governance, but it simultaneously forces security teams to shift their focus toward granular policy, distinct identity and deep observability for non-human actors. Furthermore, as cross-company data collaboration expands, privacy-preserving controls and role separation are transitioning into first-class engineering requirements. Key security-centric announcements and themes To address these challenges, Snowflake positioned its autonomous agents as systems operating directly over enterprise data under existing governance frameworks, rather than inside opaque, external black boxes. A cornerstone of this strategy is a deepened partnership with Anthropic, allowing customers to run Claude models and deploy AI agents natively inside Snowflake environments through Cortex AI. This model-in-platform approach ensures sensitive data never leaves the existing perimeter, which significantly reduces data movement risk and helps maintain consistent controls. Snowflake also introduced the foundation of Agent Identity, which allows security and data teams to explicitly recognise when actions occur within an agent’s context and apply custom masking or visibility rules. As Christian Kleinerman noted, policies can now treat agent traffic differently by tightening or expanding access as needed. This governance extends to multi-party collaboration, where Snowflake highlighted capabilities that let multiple organisations analyse and activate shared datasets without exposing raw PII