Snowflake has announced new capabilities across Snowflake Horizon Catalog to help enterprises govern, contextualize and secure AI systems as they move from experimentation to production-scale deployment.
The updates focus on providing a shared business context for AI agents, applications and teams, while strengthening security, governance and compliance controls for enterprise AI use cases.
As AI agents increasingly make decisions and take actions across enterprise environments, inconsistent data definitions and fragmented business logic can create risks. Snowflake said Horizon Context is designed to address this by creating a common context layer for AI and business intelligence, ensuring that data has the same meaning across systems.
Horizon Context brings together business logic from databases, data lakes, BI tools and other parts of the data estate. This allows users, tools and AI agents to work from consistent and governed business definitions. The capability is intended to reduce the risk of inaccurate AI outputs caused by inconsistent definitions or fragmented data logic.
The platform also includes capabilities to maintain business context over time. Features such as Semantic Studio allow teams to define shared business logic without requiring SQL expertise, while Semantic View Autopilot can create and refine semantic views. Snowflake said semantic views and data agents can also be created for shared datasets, including those from Snowflake Marketplace.
Snowflake is also extending trusted business definitions across external AI agents and BI tools. Horizon Context supports the Open Semantic Interchange, allowing business definitions to be used across different tools and systems.
The company also announced new security capabilities aimed at managing AI agents. These include Agent Identity, which gives AI agents verified identities before they access enterprise data or take action. The feature enforces role-based permissions and creates audit trails of agent activity.
Snowflake also announced updates to Snowflake Trust Center to help organizations monitor AI system security posture, investigate violations and respond to risks. These capabilities are intended to provide visibility into how AI systems access data and interact with enterprise environments.
The security updates also include controls to reduce risks such as unauthorized data exposure, compromised agents, data exfiltration and prompt injection attempts. Snowflake said these controls are designed to apply consistent security policies across AI workloads.
In addition, Snowflake introduced Adaptive Compute, which automatically optimizes compute and software resources in real time for AI and application workloads. The feature is intended to reduce the need for manual tuning and infrastructure management as AI workloads become more dynamic.
The updates reflect a broader enterprise shift toward governed and secure AI operations, where organizations need consistent business context, agent identity controls, security monitoring and scalable compute management to support AI systems in production.
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