The idea of a data catalog as simply a system of record is dead, and so is the sheer manual effort required to create and maintain one. In the age of agents, copilots and autonomous analytics, you need a universal AI catalog — embedded, interoperable, resilient and built for machine-speed reasoning.
“Universal AI catalog” isn’t a fancy buzzword. “AI catalog” means a catalog that is intelligent, with contextual knowledge that allows both humans and AI agents to work faster and smarter. “Universal” speaks to interoperability, with a vantage point that reaches beyond individual platforms such as Snowflake, AWS or Microsoft to the entire data estate.
Required components for a universal AI catalog
A universal AI catalog has two defining elements:
Semantic layer: A business-friendly layer that sits between complex, raw data (stored in databases or data lakes) and the people or AI agents who need to use it.
Universal interoperability: The ability of a data catalog to orchestrate governance, security and metadata across a fragmented data estate, regardless of the underlying cloud, storage format or compute engine.
Let’s dig deeper into these concepts and see why they are inextricably linked.
Grammar for the machine: Why AI agents require a semantic layer
Machine intelligence requires context, often referred to as a semantic layer. While traditional catalogs provide raw data, such as column names, an AI-ready catalog provides knowledge through the semantic layer by defining what that data actually represents.
While humans can infer meaning from a column, AI agents are literal and context-blind. An agent might recognize “TX_LMT” as a number but can’t infer its currency or regional context — or it might guess that TX_LMT stands for "tax limit" when it actually means "tax local municipal total," introducing an unfortunate error. The semantic layer would provide the specific definition of the term, acting as a hard guardrail and forcing both agents and humans to abide by official business logic, context and definitions.
This layer is only as reliable as its underlying governance. By integrating sensitive data protection, lineage, data quality monitoring and policies such as role-based access control (RBAC) and attribute-based access control (ABAC), governance shifts from a static roadblock to a fluid shield. This helps ensure that data shared with humans and machines is accurate, traceable and architecturally bound by security policies that adapt to data sensitivity in real time.
Govern once, enforce everywhere: Why intelligence without interoperability falls short
While the semantic layer provides the depth (the meaning and knowledge), universal interoperability provides the breadth (the reach across your entire estate) for a universal catalog. Without both, your AI strategy is either a brain without a body or a body without a brain.
In a universal AI catalog, the security policies (masking, fine-grained access controls) are baked into the interoperable access path. If an AI agent accesses data via a third-party compute engine, the catalog’s semantic intelligence travels with it. The agent is governed by the knowledge of the catalog, so sensitive data remains protected regardless of which tool is being used.
When you combine a semantic layer with a universal, interoperable catalog, you have a control center for your businesses with advantages such as:
Scale: You can add new data sources or new AI models tomorrow without rebuilding your governance from scratch.
Agility: Because the semantic layer extends across the catalog, any update to a business definition is instantly reflected everywhere.
Trust: You move from hoping your employees and agents are complying with your policies to knowing they are, because the governance rules are inseparable from the data they consume.
The current enterprise data catalog market
For over a decade, traditional enterprise data catalogs centralized metadata, built glossaries and helped organizations search for trusted data. The goal was to build a "Google for data" so analysts could find a table and see who owned it.
AI has shifted the focus from human browsing to machine reasoning. Many catalogs fail this transition because they can only function as passive repositories rather than active, intelligent control planes.
For an organization to successfully deploy AI agents, it must move away from these disconnected inventories and toward a universal AI catalog such as Snowflake Horizon Catalog. This facilitates proactive risk reduction by embedding security controls into every query. It also fosters operational agility, allowing an organization to scale data sources or update AI models without rebuilding governance framework, keeping the enterprise resilient and innovation-ready.
Snowflake Horizon Catalog: A universal AI catalog for the entire enterprise
The semantic context layer
While traditional data catalogs excel at documentation, AI agents require more than a glossary of existing data — they need business context. LLMs are very good at generating SQL, but they struggle with relational semantics and are much less reliable at reasoning about grain, multi-hop joins and bridge tables, and at avoiding subtle double counting. A query can look perfectly reasonable and still be semantically wrong.
Horizon Catalog enables Semantic Views, which aren’t just description metadata. There’s a compilation engine in Snowflake that understands entities, relationships, metrics, dimensions and valid join paths — and can enforce that structure at query time. Instead of asking an LLM to infer business meaning from table names and foreign keys, we provide an explicit, governed semantic contract. It’s like giving the agent a GPS rather than a pile of paper maps: the agent follows governed paths to reach its conclusion, staying within the guardrails because the guardrails are part of the semantic definition.
This is even more powerful when you’re working with a catalog that raises the bar when it comes to governance. Horizon Catalog goes beyond using simple metadata by providing deep data lineage to track the flow of information and integrated data quality monitoring to ensure integrity. Data security is not a bolted-on feature but a foundational layer, with Trust Center and easy-to-use sensitive data protection, reducing the chance of PII being exposed to unauthorized eyes. By combining RBAC and ABAC, organizations can move from rigid, manual permissions to fluid, context-aware policies.
While Databricks has an existing semantic model concept, it requires manual work. Snowflake enables the automated creation of semantic models from existing context (BI models, SQL queries) and AI-powered suggestions for model improvement and evolution. This is more efficient because it allows you to get up and running with AI-powered analytics immediately, and then make sure that your semantic context evolves as your business changes. Snowflake also produces suggestions based on query history and usage data so the semantic view improves over time.
Easy-to-implement governance that follows data anywhere in your ecosystem
Many legacy catalogs were built for fragmented data estates — stitching together metadata from multiple tools and environments. That model assumes data lives everywhere and governance must be aggregated after the fact.
Snowflake flips that. The data, compute, governance and catalog are unified across clouds and regions in a single platform. As AI accelerates data creation, sharing and collaboration, organizations cannot afford brittle, loosely connected governance overlays. They need a unified intelligence layer that scales with machine-speed data interaction.
For example, Databricks Unity is optimized for the Databricks ecosystem – where it does a great job. But it lacks the universal reach of Horizon Catalog, which is compatible with any engine, any data format, anywhere – across native Snowflake objects, data in open table formats (Iceberg, Delta) that can be read or written by any engine, and data in relational databases (like SQL Server, Postgres). Horizon Catalog also works consistently across AWS, Azure and GCP, and offers the ultimately architectural flexibility with a path to migrate open source catalog such as Apache Polaris (incubating) at any time.
By contrast, the Snowflake Horizon Catalog has embedded Apache Polaris and Iceberg REST APIs to enable an open lakehouse architecture. With full bidirectional interoperability — including the general availability of external engine reads and public preview soon of external engine writes — governance follows the data across clouds and engines. Data protection policies such as row access and column masking are automatically enforced, even when data is accessed via external tools such as Apache Spark.
This means the governance follows the data — anywhere in your ecosystem. And you no longer need to put in manual effort to make sure this is the case: Cortex Code allows you to use natural language to find sensitive data and apply policies in minutes, with minimal technical expertise. Simply ask Cortex Code to scan a particular database for PII or to audit existing masking policies, and governance implementation goes from being a blocker to being a nonissue.
The unified control plane: Where meaning meets enforcement
AI success depends in part on trust; to generate that trust, you need a governance framework that is architecturally incorporated from start to finish. Universal AI catalogs such as Snowflake Horizon Catalog fill this role, acting as the connective tissue between complex business logic and diverse, disparate data estates.
When you combine semantic depth with universal interoperability, you move beyond mere data management and into the realm of agentic orchestration. Separately, these features are useful; together, they are the prerequisite for a functional AI strategy.
Learn more about Snowflake Horizon Catalog here.