Snowflake AI Governance Practice
Snowflake Enterprise AI Governance Consulting for Regulated Firms
Snowflake gives a regulated firm real controls for data and AI. Kriv AI builds the inventory, risk tiering, validation and reporting program that a bank or health system puts on top of them.
Snowflake enterprise AI governance consulting is the work of building a model inventory, risk tiers, validation plans and audit evidence around the AI systems a regulated firm runs on Snowflake, then mapping those controls to Snowflake features such as masking policies, access history, tags and the Model Registry so the firm can show its program to an examiner.
snowflake ai governance
Snowflake Enterprise AI Governance Consulting for Regulated Teams
Banks, insurers and health systems that keep their data in Snowflake often start running AI where the data already lives. What they are usually missing is the governance program around it: who owns each model, how risky it is, how it was tested, and what evidence exists that it is being watched.
Kriv AI works as a governance layer, not a systems-integration team. We do not rebuild your data platform. We build the model risk management discipline, map it to the Snowflake controls your edition includes, and name the gaps where no native control exists and a manual process has to fill in.
The same shape of engagement applies whether the firm is a bank reading SR 26-2, a health system answering its compliance office, or a life sciences company with validated systems. The regulation changes. The work of inventory, tiering, validation, monitoring and reporting does not.
snowflake adds change
What Snowflake Provides and What It Does Not Decide
Snowflake documents a set of controls that are useful building blocks for AI governance. None of them decides how risky a model is or who must sign off before it goes live. That judgment belongs to the institution.
Column-level protection
Snowflake documents Dynamic Data Masking as a column-level security feature that uses masking policies to selectively mask plain-text data in table and view columns at query time, and documents that column-level security requires Enterprise Edition or higher. For an AI program that reads from sensitive tables, this is one way to limit what a model pipeline or an analyst can see. Which columns need a policy, and for which roles, is a risk decision that has to be written down.
Row-level protection and its limits
Snowflake documents that row access policies support segregation of duties, allowing governance teams to define policies that limit sensitive data exposure, and documents that row access policies also require Enterprise Edition or higher. Snowflake also documents that row access policies do not currently prevent rows from being inserted, or prevent visible rows from being updated or deleted. A governance review reads limits like that one and decides what compensating control, if any, the firm needs.
Tags for classification
Snowflake describes a tag as a schema-level object that can be assigned to another Snowflake object, and documents that tags are inherited, so a tag set on a table is inherited by all columns in that table. Tags can carry a data sensitivity label or a model risk tier. Deciding what the labels mean, and who may change them, is governance work and not a feature.
Audit evidence
Snowflake documents that the Access History view in Account Usage can be used to query the access history of objects such as tables, views and columns within the last 365 days, and that Access History requires Enterprise Edition or higher. If a firm needs evidence older than a year, or runs on a lower edition, a governance review finds that out before an examiner does.
Model lifecycle and monitoring
Snowflake documents its Model Registry as storing and managing model versions, metrics and metadata, managing access with role-based access control, and monitoring performance and drift using Snowflake ML Observability. Snowflake also documents that models trained using ML Functions, for example FORECAST, do not appear in the model registry. A firm that treats the registry as its model inventory would miss those models, so the inventory has to be built across both.
Access to generative AI functions
Snowflake documents that to call its Cortex AI Functions a role needs the USE AI FUNCTIONS account-level privilege and one of the CORTEX_USER or AI_FUNCTIONS_USER database roles. That is an access control a firm can tie to a risk tier. Snowflake also documents that credits, tokens and request metadata such as model name, request ID and user ID for most AI features are recorded in Account Usage views, and that trace delivery for AI Observability is best effort, so totals computed from the event table can under-report consumption. Evidence from best-effort sources needs a stated reliability.
finserv snowflake model
Finserv on Snowflake: Model Risk Under SR 26-2
SR 26-2, the revised interagency guidance on model risk management issued by the Federal Reserve, the OCC and the FDIC, supersedes and replaces SR 11-7. Its footnote states that generative AI and agentic AI models are novel and rapidly evolving and are not within its scope, while its principles apply to traditional statistical and quantitative models and non-generative, non-agentic AI models. The letter and the attached guidance say it is expected to be most relevant to the larger banking organizations, defined by total assets in its applicability note, and generally excludes models used by smaller ones, though the guidance may be relevant to a smaller bank with significant exposure to model risk.
In practice a bank on Snowflake often runs a mix: a conventional scoring model, a machine learning model logged in its own registry, and a generative assistant calling Cortex functions. Where a bank is within its size scope, SR 26-2 speaks directly to the first two. For the generative tools, the guidance leaves the bank to apply its own risk management and governance practices, and a defensible program says in writing how it does that.
Concretely, we help banks keep an inventory that spans registry models and SQL-level AI functions, treat prompt and configuration changes as controlled events rather than silent edits, and connect monitoring output to something a validation team reviews. The guidance is not an enforceable standard by its own terms, so each bank decides how far to go and documents the reasoning. Not being enforceable standards does not remove supervisory exposure: the guidance notes that supervisory action may still result for violations of law or unsafe or unsound practices stemming from insufficient management of model risk.
healthcare snowflake model
Healthcare and Life Sciences on Snowflake
For a health system or a life sciences company, the questions shift toward access to sensitive records, audit trails and validation. The HIPAA Security Rule, codified at 45 CFR Part 164, Subpart C, sets administrative, physical and technical safeguards, and an AI workload belongs inside the risk analysis that rule expects.
The governance question is rarely whether a Snowflake feature can be configured securely. It is whether the organization can show which model touched which data, when, and under whose authority, and whether the people responsible reviewed it. Masking policies and tag-based classification can reduce how much an AI system needs to see in the first place, but someone has to decide the rules and review them.
For clinical research and regulated manufacturing, validation expectations come on top. Our separate page on GCP and GAMP 5 clinical AI validation shows how that work is approached. This page covers the broader governance program that sits above any single validation effort.
tiers
What a Kriv AI Snowflake Governance Engagement Includes
The engagement follows four steps. Only the regulation and the Snowflake features in scope change from client to client.
1. 1. Inventory and risk tiering
Every AI system on Snowflake, including registry models, models outside the registry, generative applications and third-party tools, is logged with its data inputs, decision impact, owner and a risk tier, so validation effort is proportional.
2. 2. Control mapping
Each expectation, such as access control, audit logging, data protection and output monitoring, is mapped to the Snowflake feature that supports it, with the gaps marked where a manual process is needed.
3. 3. Validation and monitoring design
For generative and agentic systems that cannot be backtested like a statistical model, validation means structured output sampling against a rubric and testing for guardrail failures. For registry models, drift monitoring is tied to alerts that someone owns.
4. 4. Governance structure and reporting
Ownership, review cadence and a committee reporting format are documented so the program survives staff changes and produces the evidence a regulator or auditor may ask for.
snowflake native controls
Snowflake Controls We Map To
The technical work is largely configuration and mapping rather than custom development. The table shows common governance needs and the Snowflake capability that can carry each one.
| Governance Need | Snowflake Capability | What It Handles |
|---|---|---|
| Column protection | Dynamic Data Masking | Masking policies applied at query time to table and view columns |
| Row protection | Row access policies | Policy-driven limits on which rows a role sees |
| Data classification | Object tagging | Schema-level tags with inheritance, usable for sensitivity or risk labels |
| Audit logging | Access History | Object-level access records for the last 365 days, Enterprise Edition or higher |
| Model inventory and lifecycle | Model Registry | Versions, metrics, metadata and role-based access for registered models |
| Generative AI access | Cortex AI Functions privileges | USE AI FUNCTIONS privilege plus CORTEX_USER or AI_FUNCTIONS_USER role |
| AI usage and trace evidence | Account Usage views and AI Observability | Credits, tokens and request metadata, plus traces delivered on a best-effort basis |
differentiation
How This Page Differs From Our Related Pages
This page covers the general governance program on Snowflake. Our Azure, AWS and Google Cloud pages cover the same work on those platforms, and our Databricks financial services page covers a different data platform. If your question is how to build governance across a Snowflake estate, start here.
engagement
How engagements are scoped and priced
Every engagement is scoped and priced after a short discovery call.
Sources
Cited sources
- Federal Reserve SR 26-2, Revised Guidance on Model Risk Management, supersedes and replaces SR 11-7
- SR 26-2 Attachment: generative AI and agentic AI models are not within the scope of the guidance
- Snowflake documents column-level security and Dynamic Data Masking
- Snowflake documents row access policies
- Snowflake documents object tagging and tag inheritance
- Snowflake documents the ACCESS_HISTORY view, its 365-day window and Enterprise Edition requirement
- Snowflake documents the Model Registry and what it does not manage
- Snowflake documents Cortex AI Functions and the privileges required to call them
- Snowflake documents AI Observability, Account Usage views and best-effort trace delivery
- The HIPAA Security Rule safeguards are codified at 45 CFR Part 164, Subpart C
Related resources
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Straight answers
Frequently asked questions about Snowflake Enterprise AI Governance Consulting for Regulated Firms
What does AI governance consulting on Snowflake cover?
It covers the risk management program layered on top of Snowflake controls: an inventory of every AI system, a risk tier for each, validation and monitoring design, and a governance structure with clear owners and reporting. Snowflake supplies masking, row policies, tags and access history. The program that uses them to meet a specific regulation has to be built.
Does the Snowflake Model Registry give me a complete model inventory?
Not on its own. Snowflake documents that models trained using ML Functions, for example FORECAST, do not appear in the model registry, and that some model types appear in the Snowsight UI but are not managed by the registry API. A complete inventory also covers those models and any generative functions called from SQL.
How long does Snowflake keep access history for audit evidence?
Snowflake documents that the Access History view in Account Usage can be used to query access history within the last 365 days, and that Access History requires Enterprise Edition or higher. If a firm needs a longer evidence window, it has to export and retain the records itself.
Does SR 26-2 apply to AI models running on Snowflake?
For models within its scope, yes. The letter and the attached guidance say it is expected to be most relevant to the larger banking organizations, defined by total assets in its applicability note, and generally excludes models used by smaller ones, though it may be relevant to a smaller bank with significant exposure to model risk. It is supervisory guidance about model risk management, not about infrastructure, so the platform a model runs on does not change whether it is in scope. The guidance states that generative AI and agentic AI models are not within its scope, so a bank governs those under its own policies.
Is SR 26-2 a legal requirement?
The guidance states that it does not set forth enforceable standards or prescriptive requirements. Each bank decides how to apply it to its own risks. That does not remove all supervisory exposure: the guidance notes that supervisory action may still result for violations of law or unsafe or unsound practices stemming from insufficient management of model risk. Ask your own counsel how it applies to your institution.
Can you help with generative AI as well as traditional models on Snowflake?
Yes. We tier generative applications alongside conventional models, then design validation for them that fits how they behave, such as output sampling against a rubric, testing for guardrail failures and monitoring. Because SR 26-2 places generative AI outside its scope, we write down which of the bank's own policies govern those systems.
How is a Snowflake governance engagement priced?
Scope depends on the size of the AI estate, the regulation involved and how mature the current program is. Every engagement is scoped and priced after a short discovery call.
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