Blog
0 min read

Best practices for protecting your data: Snowflake role hierarchy

Written by
Arman Babayan

One stolen password can bring down an entire enterprise. The 2024 Snowflake breaches revealed how fragile weak access controls are, with 165 organizations and millions of users affected. The breaches were not the result of advanced attacks. They happened because stolen passwords went unchecked, and multi-factor authentication was missing. As businesses move more of their data to the cloud and centralize it on platforms like Snowflake, a critical question emerges: who should have access, and how do you manage it at scale without slowing the business or weakening security?

In this article, we’ll break down the Snowflake Role Hierarchy, explain why it matters, and share best practices for structuring roles that support security, compliance, and day-to-day operations.

What is Snowflake’s role hierarchy?

Snowflake’s role hierarchy is a structured framework that defines how permissions and access controls are organized within the platform. In Snowflake, access to data and operations is governed entirely by roles. Using the Role-Based Access Control (RBAC) model, you grant privileges to roles, and then assign users to those roles, simplifying administration, ensuring consistency, and making audit access easier. RBAC is generally recommended for production environments and enterprise-level governance.

The hierarchy operates on a parent-child relationship model where higher-level roles inherit privileges from subordinate roles, creating a tree-like structure. This structure provides granularity, clarity, and reusability, but it requires thoughtful planning to avoid sprawl or over-permissioned users.

Core components of Snowflake RBAC

  • Roles: The fundamental building blocks that encapsulate specific privileges
  • Privileges: Defined levels of access to securable objects (databases, schemas, tables)
  • Users: Identities that can be assigned roles to access resources
  • Securable Objects: Entities like databases, tables, views, and warehouses that require access control
  • Role Inheritance: The mechanism allowing roles to inherit privileges from other roles

Understanding Snowflake's system-defined roles

Understanding the default role structure is crucial for building secure hierarchies:

ACCOUNTADMIN

SYSADMIN

  • Full control over database objects and users
  • Recommended parent for all custom roles
  • Manages warehouses, databases, and schemas

SECURITYADMIN

  • Manages user and role grants
  • Controls role assignment and privilege distribution
  • Essential for maintaining RBAC governance

Custom roles

  • Created for specific teams or functions within an organization (e.g ANALYST_READ_ONLY, ETL_WRITER).

Best practices for designing a secure Snowflake role hierarchy

A well-structured role hierarchy minimizes risk, supports compliance, and makes onboarding/offboarding easier. Here’s how one should do it right:

1. Follow the Principle of Least Privilege

Grant only the minimum required permissions for each role to perform its function. Avoid blanket grants like GRANT ALL ON DATABASE.

Do this:

  • Specific, targeted grants
  • Avoid cascading access down the role tree unless absolutely needed
  • Regularly audit roles to ensure they align with actual usage
GRANT SELECT ON TABLE SALES_DB.REPORTING.MONTHLY_REVENUE TO ROLE ANALYST_READ;
GRANT USAGE ON SCHEMA SALES_DB.REPORTING TO ROLE ANALYST_READ;
GRANT USAGE ON DATABASE SALES_DB TO ROLE ANALYST_READ;

Not this:

  • Overly broad permissions
GRANT ALL ON DATABASE SALES_DB TO ROLE ANALYST_READ;

Why does it matter?

Least privilege prevents accidental (or malicious) misuse of sensitive data. It also supports data governance and compliance with various regulations like GDPR or HIPAA.

2. Use a layered role design

Design your roles using a layered and modular approach, often structured like this:

  • Functional Roles (what the user does):
CREATE ROLE ANALYST_READ;
CREATE ROLE ETL_WRITE;
CREATE ROLE DATA_SCIENTIST_ALL;
  • Environment Roles (where the user operates)
CREATE ROLE DEV_READ_WRITE;
CREATE ROLE PROD_READ_ONLY;

Composite or Team Roles (Group users by department or team, assigning multiple functional/environment roles under one umbrella)

CREATE ROLE MARKETING_TEAM_ROLE → includes PROD_READ_ONLY + ANALYST_READ

3. Avoid granting privileges directly to users

Always assign privileges to roles and not users. Then, assign users to those roles.

Why it matters?

This keeps access transparent and auditable. If a user leaves or changes teams, simply revoke or change the role. There’s no need to hunt down granular permissions.

4. Establish consistent naming conventions

Enforce naming conventions as consistent role and object naming makes automation and governance far easier to scale.

Recommended Naming Pattern:

  • Access Roles: {ENV}_{DATABASE}_{ACCESS_LEVEL} (e.g., PROD_SALES_READ)
  • Functional Roles: {FUNCTION}_{TEAM} (e.g., DATA_ANALYST, ETL_ENGINEER)
  • Service Roles: {SERVICE}_{PURPOSE}_ROLE (e.g., FIVETRAN_LOADER_ROLE)

5. Use separate roles for Administration vs. Operations

Split roles that manage infrastructure (e.g., warehouses, roles, users) from roles that access data.

  • Admins: SYSADMIN, SECURITYADMIN‍
  • Data teams: DATA_ENGINEER_ROLE, ANALYST_ROLE, etc.

Why it matters? This separation of duties limits the potential impact of security incidents and supports audit compliance. Administrators should not have access to sensitive data unless it's absolutely necessary for their role.

6. Secure the top-level roles

Roles like ACCOUNTADMIN and SECURITYADMIN should be assigned to the fewest people possible, protected with MFA, and monitored for any usage.

Implementation Checklist:

  • Limit ACCOUNTADMIN to 2-3 emergency users maximum
  • Enable MFA for all administrative accounts
  • Set up monitoring and alerting for admin role usage
  • Regular access reviews and privilege audits
  • Document and justify all administrative access

Monitoring, auditing & compliance: keeping your Snowflake hierarchy healthy

Even the best-designed role trees can get messy over time. Here’s how to maintain security:

1. Regular access reviews

Implement quarterly access reviews to maintain security hygiene:

  • Role Effectiveness Analysis: Identify unused or over-privileged roles
  • User Access Validation: Verify users have appropriate role assignments
  • Privilege Scope Review: Ensure roles maintain least privilege principles
  • Compliance Mapping: Document role mappings to business functions

2. Logging and monitoring

Enable Access History and Login History in Snowflake to track activity and implement automation tools for role assignments during employee transitions.

3. Onboarding/offboarding automation

Implement automation tools or scripts to efficiently manage role assignments during employee transitions.

4. Object Tagging for enhanced security

Use object tagging to classify sensitive data and control access accordingly.

Measuring RBAC Success: Key Performance Indicators

1. Security Metrics

  • Access Review Coverage: % of roles reviewed quarterly
  • Privilege Violations: Number of excessive privilege grants identified
  • Failed Authentication Attempts: Monitor for unauthorized access patterns
  • Role Utilization Rate: % of active roles vs. total created roles

2. Operational Metrics

  • User Onboarding Time: Average time to provision new user access
  • Role Management Efficiency: Time to modify/update role permissions
  • Audit Response Time: Speed of access review and remediation
  • Automation Coverage: % of role operations automated vs. manual

3. Compliance Metrics

  • SOC 2 Readiness: Role hierarchy documentation completeness
  • GDPR/Data Privacy: Data access control effectiveness
  • Industry Compliance: Sector-specific requirement adherence
  • Change Management: Role modification approval and documentation

Future-Proofing Your RBAC Strategy

The way you manage access today will define how secure and scalable your Snowflake environment is tomorrow. The strength of Snowflake’s RBAC model lies in its flexibility, but that power comes with responsibility. As AI features mature, as multi-cloud deployments become the norm, and as regulators tighten expectations around data privacy, static role hierarchies quickly fall behind. A poorly structured role hierarchy can lead to data leaks, audit failures, higher operational costs, and stalled innovation.

At Snowstack, we specialize in building RBAC strategies that are not only secure today but ready for what’s next. Our team of Snowflake-first engineers has designed role models that scale across continents, safeguard sensitive data for regulated industries, and enable AI without exposing critical assets. We continuously monitor Snowflake’s roadmap and fold new security capabilities into your environment before they become business risks.

Don’t wait for the next breach to expose the cracks in your access controls. Let’s design an RBAC strategy that keeps you secure, compliant, and future-ready.

‍

‍

FAQs

RBAC provides scalability and centralized control by granting privileges to roles, which are then assigned to users. UBAC allows privileges to be assigned directly to individual users and is intended for collaborative scenarios like building Streamlit applications.

Follow the "Role of Three" principle: create Access Roles (data-centric), Functional Roles (business-centric), and Service Roles (system-centric). This approach avoids role explosion while maintaining necessary granularity.

Always assign privileges to roles and not users. Then, assign users to those roles. This keeps access transparent and auditable.

Design with hierarchy in mind: role ownership and grant structure should align with your intended control model. Map business functions to role layers and ensure clear inheritance paths.

Create emergency "break-glass" roles with elevated privileges that are heavily monitored and logged, require additional approval workflows, automatically expire after a set period of time, and immediately notify the security team when activated.

Conduct comprehensive access reviews every quarter, perform monthly spot checks on high-privilege administrative roles, service accounts, recently modified permissions, and roles tied to employees who have left the company.

Yes, automation is critical for scaling. Create stored procedures for role provisioning, use CI/CD pipelines for role deployment, and integrate with identity providers for user lifecycle management.

Classify and tag sensitive data, enforce row-level security and column masking, maintain detailed audit logs with supporting access documentation, run regular compliance assessments and gap analyses, and document the business justification for every access role granted.

Learn more about Snowflake from top experts

Join data leaders who get Snowflake insights and updates delivered straight to their inbox.

Thanks for joining us!

We’ll keep you posted with fresh updates and resources.

Oops! Something went wrong while submitting the form.
Insights

Learnings for data leaders

Blog
5 min read

Snowflake Intelligence via REST API: how developers build with it, not just use it

Under the hood of Snowflake Intelligence (now CoWork): threads, tool-constrained agent prompts, full reasoning traces, and session monitoring through the Cortex REST API.

Read more

The first two episodes of True North showed Snowflake Intelligence from the front: plain-English questions, instant charts, and a contract workflow completed in a single conversation. Impressive, but if you are a developer or architect, the question that matters is different. Can I build with this, or am I locked into a chat window?

Episode 3 answers that. Everything you see in the chat interface is available programmatically through Snowflake's Cortex REST API. That means you can embed the same intelligence in your own products, internal tools, and automated workflows, with full visibility into how every answer is produced.

A note on naming: at Snowflake Summit 2026, Snowflake Intelligence was renamed CoWork. The demo was recorded under the original name. The API patterns shown apply to CoWork and the Cortex Agents it runs on. For what changed at Summit, read why your Snowflake agents give wrong answers on good data.

Key takeaways

  • Every capability in the Snowflake Intelligence (now CoWork) interface is accessible through the Cortex REST API.
  • Conversation threads are first-class API objects, so multi-turn context works in your own applications.
  • Agent requests are structured JSON, including which tools the agent may use, which lets you enforce boundaries in code.
  • Responses expose the full reasoning trace: generated SQL, the thinking chain, the chart specification, and the final answer.
  • The Agents panel in Snowflake gives admins a live view of every session and execution step.

Watch the demo

From the chat interface to the API

For this episode we used Insomnia, a REST API client, to call Snowflake directly. No custom application, no SDK wrapper, just HTTP requests against your account. That is deliberate: if you can do it in Insomnia, you can do it from any backend, workflow engine, or integration platform your team already runs.

What we demonstrated

1. Listing and creating conversation threads

The chat interface remembers what you asked, what came back, and what actions were taken. At the API level, that memory is a thread. We listed existing threads and created new ones, treating them as first-class objects. For developers, this is the foundation for any multi-turn experience: a patient services portal, an internal operations assistant, or an automated renewal flow that needs context across several steps.

2. Sending agent prompts with tool constraints in structured JSON

A request to the agent is not just a text string. It is a structured JSON payload carrying the prompt, the thread it belongs to, and the tools the agent is allowed to use. That last part matters in healthcare. You might allow read-only analytics in a member-facing tool while reserving write actions, like the contract generation in Episode 2, for an internal workflow with tighter controls. Tool constraints let you express those boundaries in code, on top of the Snowflake roles and policies already governing the data.

3. Inspecting the full reasoning trace

This is the part that resonates most with technical teams. The API response does not just return an answer. It returns the steps behind it: the SQL the agent generated, the thinking chain it followed, the chart specification it built before rendering, and the final answer composed from all of it. That is real observability. You can see not only what the agent said, but how and why. For applications where auditability is non-negotiable, this is a requirement, and it comes natively at the API level with no extra instrumentation.

4. Monitoring sessions from the Agents panel

Finally, we switched to the Agents panel in Snowflake, where every session, prompt, and execution step is visible in one place. When agents run across multiple teams and applications, this is how data and IT leaders keep oversight: what ran, when, under which role, and with what outcome.

What this unlocks for builders

Episodes 1 and 2 made the case for business users. Episode 3 makes the case for builders. Each capability shown in the interface becomes a building block you can compose:

  • A patient or member portal that answers coverage questions in plain English, restricted to read-only tools.
  • An automated contract renewal flow triggered by expiration events, with a human approval step before any document is issued.
  • An internal operations tool that surfaces service gaps weekly and posts ranked recommendations to the teams that own them.
  • Evaluation pipelines that replay known questions and compare generated SQL and answers against expected results before each release.

Because everything runs inside Snowflake, you inherit the security, governance, and compliance controls you already rely on. You are extending a platform you trust, not adding a new data path to secure.

Production checklist for healthcare teams

Moving from a demo to production is mostly about the layers around the agent:

  • Accurate definitions. A governed semantic layer so every metric means the same thing to every agent and every user. This is the core of AI-Ready Data Governance.
  • Scoped identity and tools. Dedicated roles per use case, least-privilege grants, and tool constraints that match each workflow's risk.
  • Evaluation before trust. A set of known questions with expected answers, run against every change to the semantic model or agent configuration.
  • Ongoing ownership. Someone has to keep definitions, permissions, and quality checks current as the business changes. That is what Platform Team as a Service covers.

Where to start

If you are planning to embed Snowflake agents in a product or workflow, the fastest safe start is a fixed-scope assessment through our Snowflake consulting practice. We review your data, definitions, and access model, identify which use cases are ready, and design the guardrails before you build. If the platform needs groundwork first, we deliver it as an AI-Ready Snowflake Platform. For industry context, see our Healthcare & Pharma page.

The full healthcare series

Episode 1: ask your patient data questions in plain English. Business users get instant, visualized answers with no SQL.

Episode 2: from expired contract to signed PDF in one conversation. The agent executes a real workflow, not just a query.

Episode 3: everything is programmable, with full tool control, reasoning transparency, and operational oversight.

Blog
5 min read

Snowflake Intelligence for healthcare: from expired contract to signed PDF in one conversation

Snowflake Intelligence (now CoWork) goes from analytics to action: finding expired payer contracts, surfacing coverage gaps, and generating a new contract PDF in plain English.

Read more

Analytics tells you what is happening. In healthcare operations, the expensive part is what happens next. A contracts manager spots a batch of expired UnitedHealthcare contracts. Someone pulls the patient records. Someone else reviews the service breakdown. A third person drafts a new contract in a separate system, cross-checks the coverage, formats the document, and routes it for approval. One simple insight turns into a multi-day, multi-team process.

Episode 2 of True North, Snowstack's healthcare series on Snowflake Intelligence, asks a different question: what if everything after the insight happened in the same conversation that surfaced it?

A note on naming: at Snowflake Summit 2026, Snowflake Intelligence was renamed CoWork. The demo was recorded under the original name. Everything shown applies to CoWork, and existing deployments migrated automatically.

Key takeaways

  • Snowflake Intelligence (now CoWork) can execute workflows, not just answer questions, when it is given the right tools.
  • In the demo, one conversation moved from expired contracts, to a single patient's coverage, to 14 unsubscribed services ranked by copay, to a newly generated contract PDF.
  • The unsubscribed-services list is effectively a ready-made coverage opportunity list for revenue cycle teams.
  • Write actions raise the bar on governance: accurate definitions, scoped permissions, and an audit trail are prerequisites, not extras.
  • Humans stay in the loop. The agent removes the mechanical work, not the decision.

Watch the demo

From insight to execution

Episode 1 showed that plain-English questions on governed healthcare data return accurate, visualized answers in seconds. That alone removes a lot of friction. But the bigger gap in most organizations is not between a question and an answer. It is between an answer and the action it should trigger. Analytics happens in one tool, execution in another, and the space in between is filled with emails, spreadsheets, and manual re-keying.

This episode closes that gap end to end.

The demo, step by step

1. Filtering expired UnitedHealthcare contracts by tier and expiration date

The workflow starts with one instruction: show expired UnitedHealthcare contracts, filtered by tier and expiration date. The agent queried the contracts data, applied the filters, and returned a clean list. Normally that is a filtered SQL query or a report that may or may not exist. Here it is a sentence, and it is only the starting point.

2. Reviewing one patient's full active contract and services

From the expired list, we drilled into a single patient: show this patient's full active contract and service breakdown. The agent returned coverage tier, active services, copay values, and contract terms in one readable response. This mirrors how a contracts manager actually thinks: find the problem at the population level, then investigate at the individual level, without switching systems or rewriting a query.

3. Identifying 14 unsubscribed services, ranked by copay

Next, we asked which services this patient was not subscribed to, ranked by copay. The answer: 14 services, highest value first. That is a prioritized coverage opportunity list, showing the services most worth adding to a renewed contract, ordered by revenue impact. A revenue cycle team would usually commission this as separate analysis. Here it emerged mid-conversation as the natural next step.

4. One instruction: create the contract, add services, generate the PDF

Then the step that changes the category. We issued a single instruction to create a new contract for the patient, add the selected services, and generate the legal document. The agent created the contract record, attached the services, and produced a PDF ready for review and signature. From expired contract to new document, without leaving the conversation.

How actions like this work in Snowflake

Querying is only one of the tools an agent can use. In Snowflake, agents can also be given custom tools, such as stored procedures that insert records or render documents, alongside the analytics tooling that turns questions into SQL. The agent decides which tool fits each step, and every tool runs inside your Snowflake account under the role the agent is allowed to use.

That design is what makes write actions safe enough to consider in healthcare. It is also why governance matters more here than anywhere else in the series. An agent that can create a contract must be working from correct coverage data, consistent service definitions, and permissions scoped to exactly what it should touch. That foundation is the work behind AI-Ready Data Governance: source-to-report reconciliation, freshness checks, lineage, and a governed semantic layer, so the agent acts on numbers you would sign off on yourself.

What this means for healthcare operations

  • Faster renewals, fewer coverage gaps. Expired contracts are caught and replaced in one sitting instead of drifting for weeks.
  • Revenue you can see. Unsubscribed services ranked by copay give revenue cycle teams a concrete, prioritized list to act on.
  • Less manual handoff. Querying, cross-referencing, and document generation stop bouncing between teams and tools.
  • A clean audit trail. Because every step runs in Snowflake, the queries, actions, and outputs are traceable, which matters for compliance reviews.

None of this removes people from the process. The contracts manager still decides. Clinical and finance teams still review the output. The agent handles the mechanical work that slows them down. For more on how we apply this to providers, payers, and pharma, see our Healthcare & Pharma page.

Where to start

Agents that take action should be scoped carefully: which workflows, which tools, which data, and which approvals. A fixed-scope assessment through our Snowflake consulting practice maps your highest-value workflows, checks whether the underlying data is accurate enough to act on, and defines the guardrails before anything goes live. Once agents are in production, Platform Team as a Service keeps definitions, permissions, and data quality current as your contracts and services change.

The rest of the series

Episode 1: ask your patient data questions in plain English, with automatic charts and no SQL.

Episode 3: building with Snowflake Intelligence through the REST API, with threads, tool constraints, reasoning traces, and admin monitoring.

Blog
5 min read

Snowflake Intelligence for healthcare: ask your patient data questions in plain English

A live healthcare demo of Snowflake Intelligence (now CoWork): plain-English questions on patient, insurer, and copay data, answered in seconds with automatic charts.

Read more

Every healthcare organization runs on a steady stream of small, operational questions. Which insurance company covers the most patients? Which add-on services carry the highest copay? Which imaging service almost nobody uses, and is that a pricing problem or an access problem? None of these are hard questions. The data to answer them already sits in Snowflake. Yet in most organizations, getting the answer still means writing SQL, hunting for the right dashboard, or opening a ticket and waiting two days.

This post is the written companion to Episode 1 of True North, Snowstack's three-part healthcare series on Snowflake Intelligence. We connected it to a live Patients Management dataset, asked plain-English questions, and recorded exactly what came back.

A note on naming: at Snowflake Summit 2026, Snowflake Intelligence was renamed CoWork. The demo was recorded under the original name. Everything shown applies to CoWork, and existing deployments migrated automatically. For what changed at Summit and why agent accuracy now depends on context, read why your Snowflake agents give wrong answers on good data.

Key takeaways

  • Snowflake Intelligence (now CoWork) lets non-technical healthcare teams query governed Snowflake data in plain English, with no SQL and no BI tool in the loop.
  • It picks the right visualization on its own: bar charts for comparisons, pie charts for distributions.
  • In the demo, insurer coverage, top copay services, and the least-used imaging service were each surfaced in seconds.
  • Answer quality depends on the semantic layer underneath. Clean data is not enough; the agent needs consistent business definitions.
  • The fastest safe path to production is an assessment of your data, definitions, and governance before rollout.

Watch the demo

What Snowflake Intelligence actually does

It is easy to dismiss this category as a chatbot bolted onto a database. That undersells it. Snowflake Intelligence is an agent that runs inside your Snowflake account. When you ask a question, it works out which data is relevant, generates and runs the query, interprets the result, and decides how to present it: a single number, a ranked list, or a chart.

Three things make it different from a generic AI assistant:

  • It runs where the data lives. Queries execute inside Snowflake, under your existing roles and access policies. Nothing is exported to a separate tool.
  • It reasons over business meaning, not just tables. When a semantic model describes what your metrics and entities mean, the agent uses those definitions instead of guessing from column names.
  • It shows its work. Every answer is backed by generated SQL you can inspect, which matters a great deal in a regulated industry. We go deep on this in Episode 3.

The demo: a live Patients Management dataset

The dataset covers patients, their insurance companies, subscribed services, copay values, and imaging services. It is the kind of operational data that sits at the center of any provider or payer. We asked questions the way an operations manager would, with no schema explanation and no preamble.

1. Insurance companies and patient counts, in seconds

The first question: which insurance companies are in the system, and how many patients does each cover? In a traditional setup, that is a GROUP BY query, trivial for an analyst and out of reach for everyone else. Snowflake Intelligence returned a ranked answer in seconds, along with a bar chart it generated without being asked. It recognized that a ranked comparison across entities is best read visually.

2. Automatic charts, with no prompting

As the conversation continued, the agent kept making sensible visualization choices on its own: bar charts for comparisons, pie charts for distributions. Anyone who has spent an afternoon configuring axes and chart types in a BI tool will appreciate how much friction disappears when the person asking never has to think about the format.

3. The top 5 extra services, ranked by copay

Next, a revenue cycle question: which five additional services carry the highest copay? One sentence produced a ranked list ready to act on. In practice, this is the insight that informs service promotion, payer negotiations, and capacity planning, and it usually arrives days after someone first asks for it.

4. The least popular imaging service, and why it matters

The most useful question in the demo was about what deserves attention. We asked which imaging service had the lowest uptake. The answer itself is a single row, but it turns a vague feeling ("imaging seems to underperform") into a specific lead. Low uptake can point to pricing, scheduling, coverage tiers, or how the service is communicated to patients. Knowing exactly which service to investigate is the difference between data and a decision.

Why the answers were right: the semantic layer

A demo like this looks effortless, and that is exactly why it deserves a closer look. An agent that reads a schema perfectly can still answer confidently and incorrectly if it does not know what the data means. Does "patient count" include inactive members? Is copay stored per visit or per plan year? Which table is the source of truth for coverage?

Those definitions live in a governed semantic layer, alongside data quality checks, freshness monitoring, and lineage. That is the work behind AI-Ready Data Governance, and it is what turns an impressive demo into answers a clinical or finance leader can actually rely on. In healthcare, a wrong number is not just embarrassing. It can drive the wrong coverage or staffing decision.

What this means for healthcare teams

  • Faster decisions. Clinical operations, revenue cycle, and service managers answer their own questions in real time instead of waiting in a queue.
  • Broader access without weaker governance. The people who most need data are rarely the ones who write SQL. Plain-English access closes that gap while queries still run under Snowflake roles and policies.
  • Fewer ad hoc tickets. Every self-served question is one less item in the data team's backlog, freeing engineers for platform work that actually needs them.
  • One version of the truth. Answers come from live, governed data, not a stale export or a dashboard built for a different purpose.

It is not a replacement for data engineering, and it does not retire every dashboard. Scheduled executive reporting still benefits from a fixed, shared view. Where conversational analytics shines is exploratory, ad hoc work, which is exactly where most healthcare teams lose the most time. See how we approach this for providers, payers, and pharma on our Healthcare & Pharma page.

Where to start

Most healthcare teams are closer to this than they think. The data is usually already in Snowflake. What is missing is the layer that makes an agent accurate: consistent definitions, quality checks, and access controls tuned for PHI. Our Snowflake consulting engagements start with a fixed-scope assessment that tells you which questions an agent can answer reliably today, what needs fixing first, and what a production rollout looks like. If your platform itself needs groundwork, we build it as an AI-Ready Snowflake Platform.

Next in the series

Episode 2: from expired contract to signed PDF in one conversation. Snowflake Intelligence stops answering questions and starts executing a real workflow.

Episode 3: building with Snowflake Intelligence through the REST API, for developers and architects.

‍

Contact us to discuss your specific requirements!

‍

FAQs

Snowflake Intelligence is Snowflake's agent for asking questions of your enterprise data in plain English. It finds the relevant data, generates and runs SQL, and returns answers as numbers, lists, or charts. At Snowflake Summit 2026 it was renamed CoWork, and existing deployments migrated automatically.

Yes. CoWork is the new name for Snowflake Intelligence, announced at Snowflake Summit 2026. The product lineage is the same: a work agent that answers questions across structured and unstructured data inside your Snowflake account.

No. Users ask questions in plain English and the agent generates the SQL. Technical teams can still inspect every query it ran, which is important for auditability in regulated environments.

Queries run inside Snowflake under your existing roles, masking policies, and access controls, so the agent only sees what the user is allowed to see. Safe use still depends on how those controls and your semantic layer are configured, which is the focus of AI-Ready Data Governance.

Because it knows your schema but not your business definitions. Without a governed semantic layer, terms like patient count or copay can be interpreted incorrectly. A fixed-scope assessment through Snowflake consulting shows which questions an agent can answer reliably today.

Explore our latest blog posts for valuable insights.
View more insights
Stay up to date

Top data insights, delivered to your inbox

 Thanks for joining us!

We’ll keep you posted with fresh updates and resources.

Oops! Something went wrong while submitting the form.

Transform your data with Snowflake

You don't need to hire a data army or wait months to see results. Our Snowflake specialists will get you up and running fast, so you can make better decisions, cut costs, and beat competitors who are still stuck with spreadsheets and legacy systems

Learn more