Google Cloud AI Governance Practice
Google Cloud AI Governance Consulting for Regulated Enterprises
Google Cloud gives a regulated firm real technical controls for AI. Kriv AI builds the inventory, risk tiering, validation and reporting program that a bank or health system puts on top of them.
Google Cloud 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 Google Cloud, then wiring those controls into services such as Vertex AI, IAM, Cloud Audit Logs and VPC Service Controls so the firm can show its program to an examiner.
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Google Cloud AI Governance Consulting for Regulated Teams
Banks, insurers and health systems that run AI on Google Cloud usually already have the platform. What they are 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 Google Cloud controls you already pay for, 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.
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What Google Cloud Provides and What It Does Not Decide
Google Cloud 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.
Identity and access
Google documents that its AI platform, which it previously called Vertex AI and now documents as Gemini Enterprise Agent Platform, uses Identity and Access Management to manage access to its resources, at the project level or the resource level. That gives a firm a place to enforce who can deploy, change or call a model. It does not tell the firm which roles a given risk tier should allow. Writing that rule down is governance work.
Audit evidence
Google Cloud services write audit logs that help answer the question of who did what, where and when. Google documents that Admin Activity audit logs cannot be disabled, while Data Access audit logs, which cover reads and writes of user-provided data, must be explicitly enabled. For a governed AI program this matters in a practical way: if nobody turned Data Access logs on, the evidence an examiner asks for may not exist. A governance review checks that setting before anything else.
Perimeters around data and models
Google documents that VPC Service Controls can help mitigate the risk of data exfiltration from its AI platform by placing resources inside a service perimeter. Among the artifacts Google lists as unable to leave a perimeter are models you created, requests for online inferences, results from batch inference requests, and training data for custom models. A perimeter is a control. Deciding which workloads must sit inside one is a risk-tiering decision.
Model lifecycle and monitoring
Google describes Model Registry as a central repository for managing the lifecycle of machine learning models, and Model Monitoring as a way to run monitoring jobs on a schedule and receive alerts when metrics pass a threshold you set, for tabular models; v2 is in Preview. Both support an inventory and a monitoring plan, but neither one creates the inventory entry, the owner, the validation record or the escalation path. Those are built.
Prompt and response screening
Google describes Model Armor as a service that screens prompts and responses for LLM applications, including attempts at prompt injection and jailbreaks, and that can scan for sensitive data using Sensitive Data Protection. It can be one layer of an output control. How strict to set it, and what a blocked response should trigger, is a policy decision a governance program has to make.
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Finserv on Google Cloud: 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 guidance is expected to be most relevant to banking organizations with over $30 billion in total assets; models used by banking organizations with $30 billion or less in total assets are generally excluded, though it may be relevant where they have significant model risk exposure.
In practice a bank on Google Cloud often runs a mix: a conventional scoring model, a machine learning model trained in its own pipeline, and a generative assistant. 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 a model inventory that includes models in the Google Cloud registry, track 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.
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Healthcare and Life Sciences on Google Cloud
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 Google Cloud service 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. Sensitive Data Protection, which Google describes as helping you discover, classify and de-identify sensitive data, can reduce how much an AI system needs to see in the first place.
For clinical research and regulated manufacturing, validation expectations come on top. Our separate page on GCP and GAMP 5 clinical AI validation covers that work in detail. This page covers the broader governance program that sits above any single validation effort.
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What a Kriv AI Google Cloud Governance Engagement Includes
The engagement follows four steps. Only the regulation and the Google Cloud services in scope change from client to client.
1. 1. Inventory and risk tiering
Every AI system on Google Cloud, including registry models, 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 Google Cloud service 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, testing for guardrail failures, and, for tabular models where Google Model Monitoring applies (v2 is in Preview), drift monitoring tied to alerts 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.
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Google Cloud Controls We Map To
The technical work is largely configuration and mapping rather than custom development. The table shows common governance needs and the Google Cloud capability that can carry each one.
| Governance Need | Google Cloud Capability | What It Handles |
|---|---|---|
| Identity and access control | Identity and Access Management | Project-level and resource-level access to AI platform resources |
| Audit logging | Cloud Audit Logs | Admin Activity logs always on, Data Access logs enabled by choice |
| Data exfiltration boundary | VPC Service Controls | Service perimeter around AI resources and data |
| Model inventory and lifecycle | Model Registry | Central repository for model versions and lifecycle |
| Performance and drift monitoring | Model Monitoring | Scheduled monitoring jobs with threshold alerts, for tabular models; v2 is in Preview |
| Prompt and response screening | Model Armor | Screening of LLM prompts and responses |
| Sensitive data discovery | Sensitive Data Protection | Discover, classify and de-identify sensitive data |
differentiation
How This Page Differs From Our Related Pages
This page covers the general governance program on Google Cloud. Our Azure and AWS pages cover the same work on those platforms. Our GCP and GAMP 5 page covers clinical validation specifically, and our Gemini training page covers teaching teams to use Gemini well. If your question is how to build governance across a Google Cloud 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 (April 17, 2026), supersedes and replaces SR 11-7
- SR 26-2 Attachment: generative AI and agentic AI models are not within the scope of the guidance
- Google Cloud documents audit logs for its AI platform, including that Data Access audit logs must be explicitly enabled
- Google Cloud documents VPC Service Controls for its AI platform and the artifacts that cannot leave a service perimeter
- Google Cloud documents access control for its AI platform using Identity and Access Management
- Google Cloud documents Model Registry as a central repository for managing the lifecycle of ML models
- Google Cloud documents Model Monitoring for scheduled monitoring jobs with threshold alerts
- Google Cloud documents Model Armor for screening LLM prompts and responses
- Google Cloud documents Sensitive Data Protection for discovering, classifying and de-identifying sensitive data
- The HIPAA Security Rule safeguards are codified at 45 CFR Part 164, Subpart C
Straight answers
Frequently asked questions about Google Cloud AI Governance Consulting for Regulated Enterprises
What does AI governance consulting on Google Cloud cover?
It covers the risk management program layered on top of Google Cloud 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. Google Cloud supplies access control, audit logging and perimeters. The program that uses them to meet a specific regulation has to be built.
Does Google Cloud turn on audit logging for AI workloads automatically?
Only in part. Google documents that Admin Activity audit logs are always written and cannot be disabled, while Data Access audit logs, which record reads and writes of user-provided data, must be explicitly enabled. A governance review should confirm the Data Access setting for any project running regulated AI workloads.
Does SR 26-2 apply to AI models running on Google Cloud?
For models within its scope, yes. The guidance is expected to be most relevant to banking organizations with over $30 billion in total assets; models used by banking organizations with $30 billion or less in total assets are generally excluded, though it may be relevant where they have significant model risk exposure. SR 26-2 is supervisory guidance about model risk management, not about infrastructure, so the cloud 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. Ask your own counsel how it applies to your institution.
What is the difference between a cloud security feature and an AI governance program?
A security feature such as a service perimeter or an audit log describes what the platform can do. A governance program records which models exist, how risky each one is, how it was validated, who owns it and what is monitored. An examiner or auditor may ask to see the second, and the client or a partner has to build it.
Can you help with generative AI as well as traditional models on Google Cloud?
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 Google Cloud 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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