0–5Informal adoption
AI use is happening faster than evidence collection.
Start with the basics: AI inventory, named owner, data-access map, and written action boundary.
Move78 route: ACT-1 or free assessments.
Answer first
If your team is adopting Google AI tools, start with governance evidence before scaling usage. The minimum evidence set is simple: list the AI feature, name the owner, record the data it can touch, define what actions need approval, keep logs, document review steps, and know how to stop or roll back risky activity.
The problem is not that Google AI tools are “bad.” The problem is that fast adoption often leaves no clean record of who approved the use case, what data was exposed, what actions the agent could take, and what evidence exists if a customer, auditor, board, or regulator asks.
Use this checklist if one of these is true
Workspace AI
Gemini is spreading through work
Your team is using AI inside Gmail, Docs, Drive, Sheets, Meet, Chat, or connected business workflows.
Agents
AI can act, not only answer
You are testing agents that browse, call tools, update records, generate code, send drafts, or operate in a sandbox.
Governance evidence
You need records, not slogans
You need a practical path from AI adoption to inventory, ownership, approval, logging, and control evidence.
Google AI governance evidence matrix
Select the evidence maturity for each control. Use Evidence saved only when someone can find the record later, not when the control exists only in chat history, a meeting note, or team memory.
Scoring rule: Not started = 0. Partly documented = 1. Evidence saved = 2. Maximum score = 20.
Your score
0/20
Not ready to scale
Start by changing each row from Not started to the state that matches the evidence you can actually retrieve.
Recommended route
Start with ACT-1 or a free assessment before wider rollout.
| Governance control | Not started | Partly documented | Evidence saved |
|---|---|---|---|
| AI feature inventoryYou can list which Google AI tools, features, agents, or workflows are being used and by which team. | |||
| Business ownerEach use case has an accountable owner, not only an IT admin or enthusiastic user. | |||
| Data access mapYou know whether the AI tool can touch personal data, confidential files, customer records, source code, tickets, or sales data. | |||
| Action boundariesThe team knows what AI may read, draft, change, send, update, execute, or never do without approval. | |||
| Human review and approvalHigh-impact actions such as external messages, record changes, code changes, or public content have a defined review point. | |||
| Prompt and response protectionThe team has reviewed prompt injection, sensitive-data exposure, harmful output, unsafe links, and unsafe file risks before wider rollout. | |||
| Logs and retained evidenceApprovals, outputs, changes, incidents, and user decisions have a known retention location. | |||
| Incident routeThere is an escalation route if the AI tool exposes data, performs the wrong action, creates harmful content, or behaves unexpectedly. | |||
| User trainingUsers understand what they may paste into AI tools, when to review outputs, and when to stop and escalate. | |||
| Vendor and configuration evidenceYou retain relevant Google documentation, admin settings, configuration decisions, and internal approval notes. |
This is a first-pass implementation signal. It is not a certification score, legal opinion, audit result, or security assurance.
Result summary
Governance evidence snapshot
This is a first-pass implementation signal, not a compliance score. It shows which Google AI governance controls have saved evidence and which still need work.
0
Evidence saved
None yet.
0
Partly documented
None yet.
10
Not started
All controls.
Score: 0/20. Evidence saved: none. Partly documented: none. Not started: all controls.
Score guide
What the result means
Use the score to decide the next evidence step. The goal is not a perfect number. The goal is to find the controls that cannot yet be shown to a buyer, board, risk team, or internal reviewer.
6–10Partial documentation
Some controls exist, but records are probably scattered.
Convert informal controls into registers, approval records, logs, retained decisions, and vendor evidence.
Move78 route: ACT-2.
11–15Managed, not board-ready
The basics are in place, but evidence may not survive scrutiny.
Stress-test whether the records can be inspected by a buyer, board, risk team, or internal audit function.
Move78 route: ACT-2 or Implementation Sprint.
16–20Strong first pass
Evidence is mature enough for deeper rollout questions.
Check new agentic, coding, integration, and data-access use cases before wider deployment.
Move78 route: Implementation Sprint for complex rollout.
Turn the checklist into working evidence
The matrix shows where governance evidence is thin. The next step is to convert the weak rows into artifacts that someone can inspect, reuse, and update.
Starter path
ACT-1 Starter
Use when you need a clean first structure: inventory, ownership, basic risk notes, and evidence starters.
- Good for SMEs and first governance buildout.
- Best when the score is 0 to 5.
Professional path
ACT-2 Professional
Use when Google AI adoption must map into controls, registers, vendor evidence, board reporting, and cross-framework implementation.
- Good for buyers, boards, and internal reviewers.
- Best when the score is 4 to 8.
Hands-on path
Implementation Sprint
Use when the team needs help converting tool adoption into evidence, owners, approval rules, and management-ready decisions.
- Good for urgent rollout or messy ownership.
- Best when a buyer, board, or audit question is near.
Next agent page
Managed Agents Control Matrix
If your Google AI use case can call tools, execute code, browse the web, or write files, use the agent control matrix next.
- Good for agentic AI pilots.
- Best before wider rollout.
Tools create questions. Evidence packs create answers.
Move from a first-pass readiness signal into editable implementation records for AI governance, agentic AI, vendor diligence, board reporting, and AI risk management.
Source basis and limits
This page is based on public Google and standards sources reviewed on 2026-05-23. It is intended as operational implementation guidance, not legal, audit, certification, procurement, or security assurance.
- Google I/O 2026 announcements and developer updates were reviewed to identify relevant AI, agent, Search, and Cloud features.
- Google Cloud documentation was reviewed for Model Armor and agent-safety references.
- Google Search Central documentation was reviewed for AI Search and SEO framing.
- NIST AI RMF and ISO/IEC 42001 public materials were reviewed as general AI risk management and AI management system references.
Questions before using the matrix
Use it if your team is adopting Gemini, Workspace AI, AI Studio, Antigravity, Managed Agents, Model Armor, or similar Google AI services and needs a first-pass governance evidence view.
No. It is an implementation planning tool. It helps identify governance evidence gaps, but it does not provide legal, audit, certification, or regulatory advice.
No. The evidence matrix logic runs in the browser and does not submit the selected answers to Move78. Site analytics may load only if the visitor accepts analytics cookies.
Start with an AI feature inventory, assign owners, define approval boundaries, document logs, and map controls into ACT-1, ACT-2, or an implementation sprint depending on the level of evidence needed.