1.What is your full name?

2.What company or organization do you represent?

3.What is your job title?

4.What is your email address?

5.Does your organization know how many AI tools are in use that were never formally approved?

No — we do not currently have visibility into unauthorized AI usage.

Partially — we rely mainly on employee surveys, policy attestations, or self-reporting.

Yes — we periodically identify unauthorized AI tools through technical monitoring (e.g., network or identity telemetry).

Yes — we continuously discover unauthorized AI usage and can bring it under policy or block it.

I don't know — I do not have enough information to answer with confidence.

6.Does your organization know who is using AI to create tools, apps, or automated tasks?

No — We don't know who is creating or using these AI tools.

Limited - We have some rules, but we don't consistently check whether employees follow them.

Managed — We know who is creating AI tools and review them before they are used.

Fully Managed - We track AI tools, check them for risks, and make sure someone is responsible for each one.

I don't know — I'm not sure how our organization handles this.

7.When your organization starts using a new AI tool or application, how quickly is it reviewed to make sure it follows company rules and security requirements?

Not at all - New AI tools can be used without any review or approval.

Sometimes - Some AI tools are reviewed, but we don't always know which ones are being used.

Managed - We keep track of approved AI tools and make sure they follow company rules.

Fully managed - Every new AI tool is automatically checked and follows company security rules from the start

I don't know — I'm not sure how our organization reviews or approves new AI tools.

8.How does your organization prevent confidential, privileged, or regulated information from entering AI prompts and agents?

No controls exist on what employees or agents can submit to AI tools.

Policy instructs people not to enter sensitive data, but nothing verifies compliance.

Inline technical controls apply on sanctioned tools, and violations are logged.

Controls are enforced across every path, including autonomous agents acting on their own.

I don't know — I do not have enough information to answer with confidence.

9.Which categories of sensitive data (for example, personal information, financial data, source code, or privileged communications) are masked or tokenized before reaching an AI model, and who is able to reverse that masking?

No masking or tokenization is applied anywhere in our AI usage.

Redaction happens by hand, on an ad hoc and inconsistent basis.

Masking is applied by data category, and coverage is measured.

Sensitive data is tokenized with controlled, audited reversal.

I don't know — I do not have enough information to answer with confidence.

10.How quickly does your organization find and fix vulnerabilities introduced by AI-generated code and AI-built agents?

AI-generated code is not scanned differently than other code, or is not scanned at all.

Scanning happens only at release; the remediation backlog is not tracked.

Continuous scanning is in place, with remediation targets that are actively tracked.

Remediation is automated and verified, with time-to-fix reported to leadership.

I don't know — I do not have enough information to answer with confidence.

11.Before AI-generated content reaches a client, regulator, or the public, what review does it go through?

Nothing sits between the AI model and the recipient.

Review happens only at an individual's discretion, inconsistently.

Mandatory review is required for defined categories of content, and it is logged.

Automated inspection occurs before delivery, with required human sign-off on high-risk content.

I don't know — I do not have enough information to answer with confidence.

12.How does your organization verify that AI-generated claims, figures, or citations are accurate before relying on them externally?

AI output is trusted as written, without verification.

Verification is left to the individual author's judgment.

Sources are required and checked for defined categories of content.

Automated grounding or verification checks run, with an evidence trail retained.

I don't know — I do not have enough information to answer with confidence.

13.If an AI system or agent began behaving unexpectedly, how quickly would your organization know, and could it intervene in time?

There is no monitoring of AI or agent activity.

Logs exist, but nobody regularly reviews them.

Activity is monitored with alerting and a defined response process.

Detection is real-time, with a tested mechanism and clear authority to stop a run in progress.

I don't know — I do not have enough information to answer with confidence.

14.Does your organization maintain a current inventory of AI agents — including which ones can access financial, customer, or production systems — and who is accountable for each?

No inventory exists; we could not produce this number today.

An inventory exists but is maintained occasionally and is likely out of date.

A current registry exists with a named owner, purpose, and system access for each agent.

The registry enforces access limits, expiration dates, and a working kill switch for every agent.

I don't know — I do not have enough information to answer with confidence.

15.Does the organization know its total AI-related spend — broken out by provider, business unit, and owner — including spend that does not run through major contracts?

Total AI spend is not known.

Spend on major contracts is known; departmental or expensed spend is not visible.

Spend is tracked by business unit and vendor and reviewed against budget.

Spend is attributed to named owners and use cases, with cost understood per workload.

I don't know — I do not have enough information to answer with confidence.

16.How does your organization measure the return on its AI investments, by use case or by agent?

Value is asserted, not measured.

Anecdotes and pilot enthusiasm substitute for measured results.

A defined benefit case exists per use case and is measured after deployment.

Return is tracked per agent or use case, and underperforming deployments are retired.

I don't know — I do not have enough information to answer with confidence.

17.If an AI-influenced decision were challenged by a regulator or in litigation, could your organization reconstruct exactly what happened — the prompt, the output, the model version, and who approved it?

No — relevant records are not consistently retained, or logs are overwritten.

Partially — some records exist, but reconstruction would likely be incomplete, and legal hold cannot currently be applied to AI systems.

Yes — prompts, outputs, model versions, and agent actions are retained on a defined schedule.

Yes — records are tamper-evident, legal hold has been tested, and a discovery playbook has been exercised.

I don't know — I do not have enough information to answer with confidence.

18.What AI-related risk or governance issue do you believe your board should understand better over the next 12 months?

Data Privacy & Security — Protecting company and customer information when using AI.

AI Accuracy & Reliability — Making sure AI provides trustworthy information and recommendations.

Employee AI Usage — Understanding how employees are using AI and the potential risks.

Legal & Compliance — Making sure AI use follows laws, regulations, and company policies.

AI Decision-Making — Understanding who is responsible when AI makes a mistake.

Other (please specify)