Automated AI traceability for Snowflake Cortex

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More and more organizations are building AI where their data lives: in Snowflake. With Snowflake Cortex AI, teams can create agents, analysts and AI applications on enterprise data in minutes. But speed without visibility creates risk. In McKinsey’s 2026 AI Trust Maturity Survey, only about one-third of organizations report mature AI and agentic AI governance, and nearly two-thirds cite security and risk concerns as the top barrier to scaling agentic AI. Without clear lineage from data to decisions, teams can’t answer a basic question: what is this AI actually doing with our data?

Source: McKinsey, State of AI trust in 2026: Shifting to the agentic era (March 2026)

What’s new: Automated AI traceability for Snowflake Cortex AI

Collibra extends automated AI traceability to Snowflake Cortex AI, enabling organizations to automatically map relationships between data assets and the AI systems built on the Snowflake AI Data Cloud, including Cortex Agents and Cortex Analyst.

This capability connects AI use cases to the underlying models, agent instructions (system prompts), semantic views and data flows that power them, directly inside Snowflake. Technical metadata and lineage from Snowflake are automatically harvested into Collibra, and stitched together with governance context: ownership, policies, quality and certifications. Instead of manually documenting Cortex pipelines or reconstructing dependencies between agents, models and source tables, traceability is captured automatically through metadata integration. Combined with the traceability already available for Google Vertex AI, Amazon SageMaker and Databricks, organizations gain one unified, cross-platform view of how their AI systems operate, with Snowflake now a first-class citizen.

How automated AI traceability for Snowflake Cortex AI helps

AI built on Snowflake Cortex AI moves fast: an agent can combine multiple models, prompts, semantic views and governed datasets in a single workflow. As these systems reach production scale, understanding how data flows through them, and how decisions are produced, becomes difficult. Without automated lineage, teams must manually document Cortex pipelines and dependencies, which introduces governance gaps and limits the ability to audit AI systems effectively. Automated AI traceability for Snowflake Cortex AI solves for:

Limited visibility into how Snowflake data flows into Cortex Agents and Cortex Analyst
Difficulty tracing relationships between agents, models, prompts, semantic views and outputs
Fragmented metadata between Snowflake and the rest of the AI and data landscape
Limited transparency for governance and compliance teams into AI built inside Snowflake
Challenges auditing AI decisions and understanding model and data dependencies at production scale

How automated AI traceability for Snowflake Cortex AI works

Automated AI traceability for Snowflake Cortex AI harvests technical metadata and technical lineage from Snowflake and connects it with governance context in the Collibra Platform. Cortex Agents, Cortex Analyst experiences, models, prompts and semantic views are automatically registered and linked to their underlying Snowflake tables, views and data products, as well as to the policy frameworks that govern them.

The integration is bi-directional. Collibra-governed metadata, such as business descriptions, tags, ownership, quality scores and policies, flows into Snowflake, while Snowflake’s technical metadata and lineage flow back into Collibra. This keeps the enterprise system of engagement continuously updated with the latest technical reality of the Snowflake landscape, without manual documentation.

This traceability is represented through visual lineage diagrams in Collibra AI Governance, illustrating how AI systems operate end-to-end: from source datasets, through semantic views and models, to agents and their outputs. Because Snowflake Cortex AI joins the same traceability fabric as Google Vertex AI, Amazon SageMaker and Databricks, organizations that span multiple ML platforms get one consistent lineage view rather than one per vendor, a key Collibra differentiator.

*Full traceability from data to decisions in Snowflake: connecting datasets, semantic views and models*

Why you should be excited

Individuals across the AI and data governance lifecycle will find unique value in this launch, such as:

AI Governance Leaders: Gain end-to-end transparency into how AI built on Snowflake interacts with enterprise data and governance policies
Data Scientists/ML Engineers: Understand upstream data dependencies and downstream impacts of changes to models, prompts and semantic views in Cortex
Compliance & Risk Teams: Access clear lineage showing how Cortex-powered outputs are generated and which data sources influence decisions
Chief Data and AI Officers: Monitor AI pipelines across Snowflake and other ML platforms from one place, and ensure governance coverage across the AI lifecycle

Use cases

AI lineage visualization: Understand how a Cortex Agent connects to models, prompts, semantic views and source data before promoting it to production
AI risk analysis: Identify where sensitive or restricted Snowflake data flows into Cortex pipelines and assess governance implications before issues arise
Compliance reporting: Provide auditors with traceable evidence of how AI systems on Snowflake generate outputs, and which policies apply along the way

Key takeaways about automated AI traceability for Snowflake Cortex AI

Automated AI traceability for Snowflake Cortex AI enables organizations to understand how their AI systems operate on the Snowflake AI Data Cloud. By automatically connecting data assets, semantic views, models, prompts and governance context, organizations gain the transparency needed to monitor Cortex pipelines, scale agentic AI with confidence and support compliance requirements. Together with traceability for Vertex AI, SageMaker and Databricks, it reinforces the power of one unified governance platform for data and AI.