Set the Foundation for Trusted AI

Set the Foundation for Trusted AI

Skip to content

Blog / Product and Technology / Set the Foundation for Trusted AI

JUN 02, 2026 / 8 min read Product and Technology Set the Foundation for Trusted AI

Nick El-Rayess +1

The gap between experimental AI and production AI is rarely caused by models. It’s an infrastructure problem — specifically, a lack of connected, governed infrastructure that can operate at the agentic enterprise’s new pace of business.

AI agents are redefining the way organizations secure, transform and serve their enterprise data. Data systems built for the cadence of human decision-making must evolve to meet the demands of agents that reason and act continuously across sensitive data at high speed, which means addressing three foundational elements:

Consistently governed data, no matter where it lives

Security designed for autonomous AI

High performance for all workloads

At Summit 2026, Snowflake unveiled a set of capabilities that answer each of these challenges by giving agents the right level of access to well-protected and easily understood data at a speed that allows them to take fast action — and allows you to deploy AI into production with confidence.

A trusted layer of context for AI

Your AI agents are only as good as the data they use for analysis and reasoning. And as agents speed up their queries and take on more autonomous actions, having consistent business logic and definitions across your data estate becomes a critical success factor. The metric "active customers" should return the same number whether it comes from a BI dashboard, a Snowflake Cortex AI agent or an analyst's SQL query. If business logic is scattered across tools and data outside of Snowflake, it may have inconsistent definitions — a semantic fragmentation problem that erodes trust and grows with every new agentic workflow.

Snowflake Horizon Context, a new capability in Snowflake Horizon Catalog , addresses this by embedding business logic into the platform, so every AI agent, BI tool and application inherits the same, consistent definitions. Business metrics, dimensions and relationships are defined once and enforced everywhere, with governance controls inherited automatically at the query engine level.

Unlike bolt-on semantic middleware, there’s no need for manual connections to every tool and query interface; Horizon Context out-of-the-box connectors mine context automatically from query histories, dbt models and BI logs, unlocking institutional knowledge and accelerating creation of semantic models.

You can then automate metadata enrichment with Semantic Studio (private preview) and Snowflake Semantic View Autopilot (generally available) to reduce the manual effort required to build and maintain a context layer. Whether you give Snowflake CoCo a SQL generation task or a higher-level data analysis task, it automatically retrieves the relevant context using Universal Search, a hybrid keyword and semantic search that uses signals like popularity to improve ranking, and access control policies to filter results.

To make the governed semantic foundation portable beyond Snowflake, Horizon Context taps into several Snowflake capabilities:

Out-of-the-box connectors: Connect to tools such as PostgreSQL, Microsoft SQL Server, Tableau, Microsoft Power BI and dbt that allow you to gather rich context — query logs, popularity, schemas and more — from many sources into one searchable catalog.

End-to-end column-level lineage: Lineage is key to understanding how data assets are related to one another. Horizon Context mines lineage information from Snowflake and external database query logs, BI systems and OpenLineage feeds, and stitches it all together to create a complete, end-to-end lineage graph.

Native Open Semantic Interchange (OSI) integration (private preview): Provides a vendor-neutral specification for metrics, dimensions and relationships, so business logic defined in Horizon Context can be read and understood across your ecosystem without translation.

Semantic Studio (private preview): Provides a visual modeling interface for building semantic views without writing SQL, making governance accessible to data teams and business analysts alike.

XMLA Endpoint for Semantic Views powered by AtScale (private preview soon): Will connect Power BI and Excel directly to the semantic layer, so enterprise BI users will be able to work from the same governed definitions as AI agents.

Advanced Semantics (private preview): Supports level-of-detail (LOD) calculations, composable definitions and user-defined materializations with automatic query rewrite.

Get more details about Horizon Context in our blog “Horizon Context: The Governed Context Layer for AI, BI and Apps.”

Along with the release of Horizon Context, Snowflake made enhancements to Horizon Catalog that underscore its role — and ease of use — as a central control plane for governance over your data estate inside and outside of Snowflake.

Just as Snowflake CoWork puts policy, classification and access controls within reach of every user, not just those fluent in SQL, new Intent-Driven Governance (private preview) capabilities convert plain-language templates into active Horizon Catalog policies. Business leaders and data practitioners can approve governance intent without having to learn Snowflake Governance primitives, and Snowflake automatically drives tag-based enforcement — including masking, access controls and data quality — across existing and future objects. The system continuously monitors for drift and generates detailed audit packages, directly tracing live configurations back to the approved human intent. In addition, agentic governance via new Snowflake CoCo skills (generally available) allows users to send natural language prompts to apply policies, fix bugs and monitor sensitive data across disparate resources.

Horizon Catalog now has full secure bidirectional read/write access from external engines (generally available), extending the policies you define in Snowflake universally to Apache Iceberg™ REST-compatible engines. Support for Apache Iceberg REST Scan Planning API (public preview) extends cross-environment enforcement to fine-grained data protections. Together, these capabilities enable customers to work off of a single governed copy of data that is securely accessible by every engine from Day 1, speeding execution for teams and agents by delivering an architecture ready to operate…