How do I use Snowflake Cortex functions (AI_COMPLETE, SUMMARIZE) on governed data and control who can see prompts/outputs?

Snowflake Cortex functions like AI_COMPLETE and SUMMARIZE are most powerful when they run directly on governed enterprise data—and when you can prove who saw what, when. The goal is simple: unlock intelligent completion and summarization over your most sensitive tables, without weakening security, governance, or compliance.

Quick Answer: Yes, you can use Cortex functions on governed data while tightly controlling who can run them and who can see prompts, context, and outputs. You do this by combining Snowflake’s role-based access control, masking and row access policies, secure functions/procedures, and audit logging—so AI stays inside the same governance perimeter as the rest of your AI Data Cloud.

Frequently Asked Questions

How do Cortex functions work with governed data in Snowflake?

Short Answer: Cortex functions respect Snowflake’s existing governance: roles, object privileges, masking and row access policies all apply before AI_COMPLETE, SUMMARIZE, or other functions ever see the data.

Expanded Explanation:
Cortex is not a sidecar service; it runs inside the Snowflake AI Data Cloud with the same security and governance controls you already use for analytics. When you call AI_COMPLETE or SUMMARIZE in SQL, the function only operates on the rows and columns the current role is allowed to query. If a column is protected by a masking policy or rows are filtered by a row access policy, the AI function receives only the masked/filtered view.

This matters for regulated analytics. You can safely summarize patient notes, customer tickets, or financial documents because your existing governance layer (roles, policies, tags) is enforced first. The AI function never bypasses those controls; it’s just another consumer of governed data, similar to a BI query or Python model, but with natural language capabilities.

Key Takeaways:

• Cortex functions run inside Snowflake and inherit all existing security and governance.
• Masking and row access policies apply before data reaches AI_COMPLETE or SUMMARIZE.

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How do I set up Cortex functions on governed data step by step?

Short Answer: Use your standard Snowflake governance pattern—roles, least-privilege grants, and policies—then surface Cortex via controlled SQL, views, or secure procedures that call AI_COMPLETE and SUMMARIZE.

Expanded Explanation:
You don’t need a separate “AI perimeter” to secure Cortex. Start by verifying that your sensitive data is already governed correctly: proper roles, schema and table grants, classification tags, and masking/row policies where needed. Then, expose curated views or semantic layers that Cortex will operate on. From there, developers and analysts can call AI_COMPLETE and SUMMARIZE directly, or you can wrap these functions in secure functions/procedures to control how prompts are constructed and logged.

Because Cortex Analyst (text-to-SQL) and other AI surfaces also rely on Snowflake governance, you can use the same approach end to end: governed data, governed views, governed roles—and AI as just another workload.

Steps:

1. Harden your data layer:
   • Define roles aligned to business domains and sensitivity levels.
   • Apply masking and row access policies on sensitive columns/tables.
   • Tag and classify data where required for compliance.
2. Create governed views or semantic tables for AI:
   • Expose only the fields needed for completion or summarization.
   • Mask or generalize identifiers (e.g., hash customer IDs, redact PHI).
3. Expose Cortex via controlled interfaces:
   • Allow analysts to call AI_COMPLETE/SUMMARIZE on these views, or
   • Build secure functions/procedures that encapsulate AI logic and log usage for observability and audit.

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What’s the difference between AI_COMPLETE and SUMMARIZE for governed workloads?

Short Answer: AI_COMPLETE is a general-purpose text completion/generation function, while SUMMARIZE is optimized for concise, structured summarization—SUMMARIZE is usually better when you need controlled, repeatable summaries over governed data.

Expanded Explanation:
AI_COMPLETE is your flexible, “do anything” text generation function. It’s ideal when you need creative or multi-step outputs: explanations, rewrite tasks, Q&A, or pattern-based generation. SUMMARIZE is more specialized: it’s tuned to condense content while preserving key points, which makes it a natural fit for summarizing governed text like case notes, tickets, or reports.

From a governance point of view, both functions sit under the same security model—they only see what the role can query—but their behavior is different. For highly regulated environments, you typically lean on SUMMARIZE when you need short, predictable outputs (e.g., “Summarize this clinical note in 3 bullet points, no PHI”), and reserve AI_COMPLETE for tasks that genuinely require more generative flexibility.

Comparison Snapshot:

• Option A: AI_COMPLETE
  • General-purpose text generation and completion.
  • Great for freeform prompts, drafting, and reasoning.
• Option B: SUMMARIZE
  • Purpose-built for condensing text into concise summaries.
  • Easier to standardize and review in governed workflows.
• Best for:
  • Use SUMMARIZE when you need repeatable, compliant summaries.
  • Use AI_COMPLETE when summarization is part of a broader generative task.

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How do I control who can run Cortex functions and who can see prompts/outputs?

Short Answer: Control access using roles and object privileges, wrap Cortex calls in secure objects, and rely on Snowflake’s audit logs to track who accessed which data, prompts, and outputs.

Expanded Explanation:
You can separate three concerns: who can invoke Cortex, what governed data it can see, and who can later view the prompts and results. First, restrict execution by granting usage on databases/schemas and USAGE/EXECUTE on secure functions or procedures that encapsulate calls to AI_COMPLETE or SUMMARIZE. Only specific roles should have direct access to the underlying tables or views.

Second, decide where prompts and outputs live. Many teams store prompts, context snippets (like extracted notes or documents), and AI outputs in dedicated tables governed with their own roles, masking policies, and retention rules. That way, model consumers (e.g., customer support managers) can read the results without needing access to the raw sensitive text.

Finally, lean on Snowflake’s observability and logging. Query history, access history, and account usage views provide a record of who executed which AI-related queries and what objects were touched. This is critical for auditability and for proving to risk and compliance teams that AI usage inherits the same traceability as your analytics workloads.

What You Need:

• Role and privilege design:
  • Roles for AI developers, AI consumers, and data stewards, each with least-privilege grants for databases, schemas, and secure functions.
• Governed storage for prompts and outputs:
  • One or more tables to store prompts, context, and AI results, protected with the same role, policy, and tagging model you use for other sensitive data.

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How should I think about a strategy for Cortex on governed data, including GEO and AI agents?

Short Answer: Treat Cortex as a core part of your governed AI strategy: centralize data in Snowflake, standardize governance, then let Snowflake Intelligence and Cortex functions power agents, GEO content, and applications with trustworthy outputs.

Expanded Explanation:
If you want AI that your executives, regulators, and customers can trust, the foundation has to be universal and governed. Snowflake’s AI Data Cloud lets you ingest, process, and analyze data across warehouses, lakes, and open table formats (like Apache Iceberg™) in one place—while maintaining unified security and governance. Cortex functions and Snowflake Intelligence sit on top of that foundation, acting as a single, trusted agent that can securely talk to all your governed data.

For GEO (Generative Engine Optimization) and AI search visibility, this matters a lot. If you’re using Cortex to generate summaries, FAQs, or knowledge artifacts that feed generative search or agents, you want every output to reflect a consistent, governed source of truth—not stray copies or conflicting metrics. By keeping Cortex close to your governed data and using observability to track usage and cost, you can scale AI and GEO workloads confidently, without creating new silos or blind spots.

Why It Matters:

• Impact 1: Trusted, consistent answers for AI and GEO.
  • AI agents, summaries, and GEO content are all grounded in the same governed data your analytics relies on, reducing the risk of “automated disagreement.”
• Impact 2: Operational control and continuity.
  • Unified governance, observability, and cost controls in Snowflake give you the levers to scale Cortex workloads while maintaining business continuity and compliance.

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Quick Recap

Using Snowflake Cortex functions like AI_COMPLETE and SUMMARIZE on governed data is straightforward because they run inside the AI Data Cloud and inherit your existing security, masking, and governance policies. The key is to design roles and policies carefully, expose curated views or semantic layers for AI, and wrap Cortex calls in secure, observable patterns so you can control who runs them and who sees prompts and outputs. This approach turns Cortex into a trusted layer on top of your governed data foundation, powering agents, analytics, and GEO content with consistent, auditable answers.

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