Snowflake has introduced new governance skills within its Cortex Code AI agent to enable enterprises to manage data access, classification, and compliance directly through AI-driven workflows. By integrating these skills directly into the Snowflake platform, the company aims to eliminate traditional bottlenecks associated with manual data governance and specialized SQL requirements.
Current practices often rely on manual classification, disconnected catalogs and reactive monitoring, which can lead to incomplete or outdated governance metadata. The new governance skills allow roles, from data engineers to compliance officers, to manage these tasks using natural language, reducing reliance on complex manual coding.
Snowflake launched Cortex Code earlier this year as an AI-driven agent designed to accelerate data engineering, analytics, and machine learning workflows inside its platform. Unlike generic coding assistants, Cortex Code operates natively within Snowflake, understanding enterprise data context, schemas, and access controls.
With the introduction of governance skills, Snowflake is extending Cortex Code to include data protection, monitoring, and compliance workflows directly within the AI interface, ensuring actions align with existing policies and controls.
Analysis
What This Means for ERP Insiders
Governance shifts from oversight to embedded execution. Organizations can operationalize governance within day-to-day data workflows rather than relying on separate controls. This reduces gaps between policy definition and enforcement, improving consistency across distributed enterprise data environments.
Governance built directly into the data platform
Raja Balakrishnan, senior manager of product management at Snowflake, said in a blog post that governance capabilities are embedded within the same platform where enterprise data resides. Core functions such as classification, masking policies, row-level access controls, lineage tracking and data quality monitoring are unified within Horizon Catalog.
This integration ensures that governance metadata remains continuously up to date and connected across workflows. For example, when data is classified, that context is immediately reflected in lineage views, while policy coverage and data quality issues can be traced through the same system to identify root causes without relying on external tools or delayed synchronization.
Cortex Code extends this model by enabling users to interact with governance through natural language. Its Data Governance Skills are built on a library of validated query patterns designed specifically for Snowflake’s governance framework, allowing users to perform complex tasks such as access audits or sensitive data analysis without requiring deep SQL expertise.
Analysis
What This Means for ERP Insiders
Data governance becomes accessible beyond technical teams. Natural language interfaces allow business and compliance users to directly engage with governance tasks. This expands accountability across functions and reduces dependency on specialized data engineering resources for routine oversight.
Core Governance Capabilities within Cortex Code
Rather than requiring users to manually select governance tools, Cortex Code determines which governance skill to apply based on the request. Key capabilities include:
- Automated data classification: Identify sensitive or regulated data across datasets
- Policy-aware query execution: Ensure all actions respect role-based access controls (RBAC) and security policies
- Monitoring and auditing: Track data usage and integrity within the Snowflake environment. This helps companies track activity, catch issues early and prove compliance during audits.
- Natural language governance workflows: Enable non-technical users to perform governance tasks without SQL expertise.
Because these functions operate directly within Snowflake, data does not need to be moved or replicated, helping to reduce risk and maintain compliance boundaries.
The update also signals Snowflake’s broader platform direction: integrating intelligence, automation, and governance within a single, AI-native data environment.
Analysis
What This Means for ERP Insiders
AI-driven data development becomes enterprise-ready. Embedding governance into AI agents addresses a key barrier to scaling AI in regulated environments. Enterprises can accelerate data and analytics initiatives while maintaining the control, auditability, and compliance required for ERP-linked operations.
