AI Checklist for SMEs: A Practical Guide to Safe AI Use

Artificial intelligence is already part of everyday business for many SMEs. Employees may be using Microsoft Copilot, ChatGPT, Gemini, AI features built into CRM platforms, transcription tools, meeting assistants, coding tools or dozens of other services without the organisation necessarily having made a formal decision to adopt AI.

That is not automatically a problem.

The risk appears when organisations do not know which tools are being used, what information is being entered into them, what decisions are being influenced by AI, or who is responsible when something goes wrong.

This checklist is designed to give SMEs a practical starting point for AI governance without creating an enterprise-sized bureaucracy around it.

TL;DR: Download the AI Checklist for SMEs.

1. Find out what AI your organisation is already using

Before writing an AI policy, establish what is actually happening.

That normally means identifying:

• approved AI platforms
• AI features built into existing software
• free or personal AI accounts being used for work
• AI meeting and transcription tools
• AI coding assistants
• AI used by suppliers or outsourced service providers

This is sometimes described as shadow AI: employees adopting AI tools without formal organisational approval or visibility.

The objective should not be to catch people doing something wrong. It is to understand what is already being used so that sensible controls can be applied.

A structured AI Exposure Review can help where the organisation does not have a reliable picture of current AI use.

2. Decide what information may be entered into AI tools

Different AI services have different contractual terms, privacy settings, retention arrangements and enterprise controls.

Employees therefore need clear guidance about what information can and cannot be entered into each approved service.

Consider categories such as:

• public information
• internal business information
• customer information
• personal data
• financial information
• commercially confidential material
• security information
• source code and intellectual property

Do not rely on a blanket instruction such as “never put confidential information into AI”. Define what is acceptable for the particular tools your organisation has approved.

3. Understand how your AI providers use your data

Do not assume all AI services handle business data in the same way.

For each significant service, understand:

• whether prompts and outputs are retained
• whether customer content is used to train models
• where data is processed or stored
• whether administrators can control retention
• whether enterprise privacy protections differ from consumer accounts
• which subprocessors are involved

The answer may be perfectly acceptable. The important thing is that somebody has checked rather than assuming.

4. Review access permissions before deploying tools such as Microsoft Copilot

AI assistants connected to internal business data can make existing information much easier to discover.

That means weak permissions which previously went unnoticed may suddenly become much more visible to users.

Before broad deployment, review:

• SharePoint and Teams permissions
• OneDrive sharing
• legacy groups
• guest accounts
• overly broad access
• sensitive document locations

The problem is not that Copilot ignores permissions. The problem is that AI can make information users already have access to much easier to find and combine.

5. Define where human review is mandatory

AI output can be useful and still be wrong.

Decide which activities require human review before the output is relied upon or published.

Examples might include:

• legal or regulatory interpretation
• customer-facing advice
• financial calculations
• security decisions
• technical configuration
• employment decisions
• formal business commitments

For low-risk activities such as brainstorming or summarising non-sensitive material, lighter controls may be entirely reasonable.

The level of review should match the potential consequence of getting the answer wrong.

6. Identify AI-assisted decisions

AI used to write a first draft of a marketing email is very different from AI used to influence whether somebody receives a service, job, loan or other significant outcome.

Document where AI:

• makes decisions
• recommends decisions
• scores individuals
• profiles customers or employees
• influences access to products or services

Higher-impact use cases normally require greater oversight, testing and documentation.

7. Check data protection implications

Existing data protection obligations still apply when AI is involved.

If an AI system processes personal data, consider whether you understand:

• the purpose of the processing
• the lawful basis being relied upon
• what information is being shared
• how long it is retained
• whether international transfers are involved
• whether a DPIA is appropriate
• how individuals’ rights can be supported

AI does not create a separate universe of data protection law. It creates new processing activities that need to be considered under the rules that already apply.

8. Give employees a usable AI policy

A useful SME AI policy does not need to be 40 pages long.

It should answer practical questions such as:

• Which AI tools may I use?
• Which accounts should I use?
• What information can I enter?
• What information must never be entered?
• When must I check AI output?
• Can I use AI-generated material externally?
• Who do I ask if I am unsure?
• How do I report an AI-related mistake or incident?

If staff cannot understand or remember the policy, it is unlikely to control behaviour effectively.

For organisations that need something more formal, an AI Governance & Guardrails framework can provide a structured policy and control model.

9. Approve AI tools deliberately

Not every AI tool needs a lengthy security assessment, but organisations should have a proportionate way to approve services before sensitive business information is entrusted to them.

Useful checks include:

• supplier identity and reputation
• contractual terms
• privacy arrangements
• security controls
• authentication options
• administrative controls
• data retention
• integration permissions

A free browser-based writing assistant clearly does not require the same assessment as an AI platform connected to your customer database.

10. Control integrations and plugins

The AI model itself is only part of the risk.

AI tools may also connect to:

• email
• cloud storage
• CRM systems
• source-code repositories
• calendar data
• customer records
• internal APIs

Review what access these integrations request and apply least privilege wherever possible.

An AI assistant with read-only access to a narrow dataset presents a very different risk from an autonomous agent with permission to change systems or send messages externally.

11. Decide who owns AI governance

Somebody needs to be responsible for AI use across the organisation.

That does not necessarily mean hiring an AI officer.

In a smaller organisation, responsibility might sit with an existing director, CTO, CISO, data protection lead or another appropriate senior person.

They should at least know:

• which significant AI services are approved
• where higher-risk use cases exist
• who can approve new tools
• who owns policy updates
• who coordinates an AI-related incident

12. Train people on the risks that actually affect them

AI awareness training should be practical rather than theoretical.

Employees generally need to understand:

• not to trust AI output automatically
• what information they may enter
• how to recognise confidential or personal information
• which tools are approved
• how hallucinations and incorrect output occur
• how to report problems

Technical staff may require additional guidance around source code, APIs, AI agents and automated actions.

13. Think about AI security as well as AI governance

Governance answers questions such as who may use AI and for what purposes.

Security also asks what an attacker could do with the technology.

Depending on the system, consider risks such as:

• prompt injection
• data leakage
• excessive permissions
• insecure plugins and integrations
• unsafe automated actions
• exposed API credentials
• untrusted content influencing AI behaviour

These become increasingly important as organisations move from simple chat tools towards agents capable of taking actions in business systems.

14. Keep records for higher-risk AI use

You do not need a complicated register for every spelling suggestion generated by an AI tool.

For more significant use cases, however, it is useful to record:

• the system being used
• the business purpose
• the owner
• the data involved
• key risks and controls
• whether human review is required
• supplier information
• review date

This gives you an AI inventory without turning the exercise into paperwork for its own sake.

15. Review AI use periodically

AI services change quickly.

New features appear, integrations are added, employees discover new tools and suppliers change terms or functionality.

Periodically revisit:

• your AI inventory
• approved tools
• high-risk use cases
• access permissions
• supplier arrangements
• AI policy
• training

The objective is not constant governance activity. It is preventing yesterday’s assumptions from quietly becoming tomorrow’s risk.

A simple AI governance checklist for SMEs

• We know which significant AI tools our people use
• We know what information may be entered into them
• We understand how important AI suppliers handle our data
• We have reviewed permissions before connecting AI to internal information
• We know where human review is mandatory
• We understand which use cases involve personal data
• Employees have clear, usable AI guidance
• New AI services have a proportionate approval process
• Integrations are granted only the access they need
• Somebody owns AI governance
• Employees receive practical AI awareness training
• Higher-risk AI use cases are recorded
• AI use is periodically reviewed

How much AI governance does an SME actually need?

Usually less than a large enterprise, but more than nothing.

A 20-person company using ChatGPT for drafting content does not need the same governance framework as an organisation deploying autonomous AI agents against sensitive customer data.

The useful principle is proportionality.

Understand the use case, understand the information involved, understand what the AI can do, and put controls around the consequences that actually matter.

When specialist help is useful

External support may be useful where:

• AI use has grown without anybody having a clear inventory
• Copilot or another enterprise AI platform is being rolled out broadly
• AI systems process sensitive or regulated information
• AI agents are being given access to internal systems
• customers are asking for evidence of AI governance
• existing security or privacy frameworks need to incorporate AI

The aim should be proportionate governance that allows useful AI adoption rather than a pile of documents designed to make AI look frightening.

If you need help understanding your current exposure, see my AI security consultancy services.