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 or proprietary information.

By establishing distinct contributor and analyst roles – a method already being adopted by early practitioners like Netflix to build collaboration “room” – organisations can share insights securely.

Snowflake Co-Founder Benoit Dageville argued that placing AI and data on a single platform ultimately improves governance, lowers operational complexity, and avoids siloed AI stacks that inherently forget governance while inflating risk and cost. 

For CISOs, Snowflake’s zero-maintenance, fully managed foundation and decoupled compute-and-storage architecture translate into a shared-responsibility posture featuring centralised policy surfaces and fewer bespoke integrations to harden.

Product updates and real-world adoption

Several product updates directly tie into these security implications. 

Snowflake CoCo – formerly known as Cortex Code – is an AI coding assistant for data engineers that generates pipelines and applications from prompts, raising the bar for code governance and secrets handling within generated workflows. 

Similarly, Snowflake CoWork, formerly Snowflake Intelligence, provides a conversational layer over enterprise data to build team-specific agents. 

CoWork leverages existing platform controls to prevent the creation of parallel, under-governed data stacks, ensuring that data is protected without requiring extra security setups.

Real-world adoption signals indicate that enterprises are already moving in this direction. 

For example, Under Armour is using Snowflake’s architecture to democratise data and has launched an internal AI agent with Elementum to query data and automate workflows. 

This deployment underscores the urgent practical need for clear agent identities, scoped permissions and strict auditability as usage broadens across the workforce.

What CISOs and security teams should validate

As organisations scale these technologies, CISOs and security teams must rigorously validate several core domains, beginning with identity and access management. 

Security leaders need to ensure that agents are treated as first-class principals in identity and access management, verifying whether they can enforce least privilege, attribute- or role-based access control, and conditional access for an agent’s context versus a human user’s context. 

Teams must also confirm that masking and row- or column-level policies can adapt dynamically based on the specific agent identity and the task at hand.

Audit, monitoring and forensics require equal scrutiny. 

Every agent prompt, tool call, data access and autonomous action must be fully logged with a clear lineage back to source datasets and original policies, backed by anomaly detection capable of spotting agent lateral movement or data overreach.

Data residency and movement demand careful oversight as well. When using Claude within Snowflake, teams must confirm exactly where inference executes, how model inputs and outputs are retained, and what the default logging and scoping parameters look like per cloud region.

For multi-party collaboration controls, security teams should verify how roles are separated between data contributors and analysts, ensuring that guarantees exist to protect raw PII and that differential privacy or equivalent policies are enforced where needed.

Finally, supply chain and model governance protocols must be established to handle the versioning, approval, and rollback paths of models and agents. 

This includes setting clear limits for autonomous actions – such as separating read-only capabilities from write or act privileges – and maintaining a strict human-in-the-loop requirement for sensitive operations.