Hackers are exploiting gaps in AI oversight, IBM’s latest report reveals, even as the global cost of data breaches falls to $4.44M. Weak access controls and shadow AI practices leave sensitive data and models exposed.

Sources: IBM | Help Net Security.

doodled the sona for pride month

80% of employees use AI tools that their IT department has not approved. Shadow AI adds $670,000 to the average breach cost. Only 37% of organisations have AI governance policies. For founders building AI products for enterprise, this data determines what you must build — not what you want to build.

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5 AI Use Rules Every Business Should Document

AI governance does not have to start with a complicated framework.

It can start with a few simple documented rules.

Here are 5 AI use rules every business should document:

1. Approved tools only Define which AI tools are allowed and who can approve new ones.

2. Data limits Document what data can and cannot be entered into AI tools.

3. Output review Require human review before AI-generated content, analysis, or recommendations are relied on.

4. Disclosure Set rules for when AI use should be disclosed internally, to clients, or in deliverables.

5. Ownership Assign a person or team responsible for each AI use case.

These simple rules can help reduce shadow AI risk and create a stronger starting point for AI governance.

AI Acceptable Use & Disclosure Toolkit

AI use needs clear rules, approval records, and disclosure guidance.

Employees may already be using AI tools to draft emails, summarize notes, rewrite documents, analyze spreadsheet data, create content, or support everyday business tasks.

That can be useful.

But without documented rules, AI use can become inconsistent, hard to review, and difficult to explain.

A practical AI acceptable use and disclosure process helps answer questions like:

Which AI tools are approved? Document the tools employees may use for business work.

What uses are prohibited? Clarify activities that should not be done with AI tools.

What data is restricted? Identify information that should not be entered into public or unapproved AI tools.

When is human review required? Define when AI-generated output needs review before use.

When should AI use be disclosed? Set expectations for customer, client, internal, or reviewer disclosure.

Who owns the final output? Make accountability clear even when AI assists the work.

The goal is not to make AI use complicated.

The goal is to make it visible, approved, reviewed, and documented.

The AI Acceptable Use & Disclosure Toolkit is designed to help businesses create a more structured starting point for employee AI use, review rules, disclosure expectations, and accountability records.

What Is an AI Acceptable Use Policy?

An AI acceptable use policy explains how employees may and may not use AI tools for business work.

It helps answer practical questions like:

• Which AI tools are allowed? • Which AI tools are not approved? • What data should never be entered? • When does AI use need review? • When should AI use be disclosed? • Who owns the final output? • Who approves exceptions?

This matters because AI use can spread quickly inside a business.

An employee may use AI to draft emails, summarize notes, rewrite documents, analyze spreadsheet data, create code, review vendor files, or prepare customer-facing content.

That can be useful.

But without clear rules, AI use can create risk around:

Security Sensitive information may be entered into public or unapproved tools.

Privacy Customer, employee, vendor, or regulated data may be exposed.

Accuracy AI-generated output may be wrong, incomplete, or misleading.

Compliance There may be no approval record, review process, or evidence trail.

Accountability No one may clearly own the final result.

A practical AI acceptable use policy should cover:

Allowed tools Which tools employees may use.

Prohibited data What information should never be entered into AI tools.

Review rules When human review is required before using AI output.

Disclosure rules When employees should disclose that AI helped create or support the work.

Ownership Who is accountable for accuracy, confidentiality, and final use.

AI use does not need to be complicated.

But it does need documented rules.

Save this definition if your business is starting to organize AI governance.

Shadow AI Is Already in Your Business

Shadow AI is not just an enterprise problem.

Small businesses can have Shadow AI too.

It happens when employees use AI tools without a clear policy, approval process, data handling rules, or record of what is being used.

That could look like someone using AI to:

• Summarize meeting notes • Draft customer emails • Rewrite policies or procedures • Analyze spreadsheet data • Review vendor documents • Generate code or process ideas • Upload screenshots, files, or business information

The risk is not always that someone is doing something malicious.

Most of the time, employees are just trying to save time.

The problem is that the business may not know:

Which AI tools are being used What data is being entered Who approved the use Whether outputs are reviewed Whether sensitive information is exposed Who owns the risk Where the documentation lives

That is why Shadow AI is not just an IT issue.

It is an AI governance, documentation, privacy, security, and accountability issue.

A simple starting point:

1. Inventory AI tools Know what tools are already being used.

2. Define approved uses Clarify what employees can and cannot use AI for.

3. Restrict sensitive data Document what should never be entered into public or unapproved AI tools.

4. Require human review AI output should be checked before it is used in customer communications, compliance documents, business decisions, or published work.

5. Assign ownership Someone should be responsible for AI policy, approvals, reviews, and updates.

Shadow AI becomes easier to manage when it is visible, documented, reviewed, and owned.