Securing AI procurement and third-party models: a practical guide for UK SMEs

Third-party AI tools can be useful, but they also change the way your business handles data, makes decisions, and depends on suppliers. For many UK SMEs, the risk is not the model itself. It is the way the tool is bought, connected, configured, and monitored after go-live.

A quick trial can turn into a business dependency before anyone has checked where data goes, who can access it, or what happens if the supplier changes its terms. That is why AI procurement needs its own security review. It does not need to be heavy or slow, but it should be deliberate.

This guide sets out a practical approach for UK SMEs that want to adopt third-party AI services without creating avoidable security, privacy, or operational issues.

Why AI procurement needs its own security review

How third-party AI services change your risk profile

Buying an AI tool is not the same as buying standard software. Many services process prompts, documents, images, or customer data in ways that are not always obvious to the user. Some tools also connect to email, file storage, customer relationship systems, or code repositories, which increases the amount of information they can see.

That means the risk profile changes in three main ways. First, data may leave your direct control. Second, outputs may be inaccurate, incomplete, or biased, which can affect business decisions. Third, the supplier may update the model or service behaviour without much notice, changing how the tool performs.

For SMEs, the practical issue is not whether AI is good or bad. It is whether the specific tool is suitable for the specific business use case, with controls that match the sensitivity of the data involved.

What UK SMEs should consider before buying or adopting AI tools

Before you approve an AI service, ask a simple question: what problem are we trying to solve, and what would go wrong if the tool behaved badly or exposed information? That question helps you focus on business impact rather than hype.

Common areas to consider include customer data, employee information, commercial plans, source code, financial records, and any regulated or confidential material. If the tool will be used by staff, also think about whether it could create unsafe shortcuts, such as copying sensitive information into a public service because it is convenient.

Define the business use case and acceptable risk

Match the AI tool to a clear business need

Start with the use case. Is the tool for drafting marketing text, summarising meetings, helping with customer support, analysing documents, or assisting developers? The more clearly you define the purpose, the easier it is to judge whether the tool is proportionate.

A low-risk use case might be a public-facing content assistant that only handles non-sensitive text. A higher-risk use case might involve customer records, internal strategy, or anything that feeds into important business decisions. The same supplier can be acceptable for one use case and unsuitable for another.

It also helps to name the owner of the use case. In smaller organisations, that may be the business lead, with support from IT or an external adviser. Someone should be accountable for deciding whether the tool is needed and whether the risk is acceptable.

Set boundaries for data use, access, and outputs

Once the use case is clear, define the boundaries. Decide what data the tool may process, who may use it, and what it must never be used for. Keep the rules short and practical so staff can follow them.

For example, you might allow a tool to help draft internal communications but prohibit the upload of customer identifiers, payroll data, or confidential contracts. You might allow read-only access to a document library but not access to shared mailboxes or admin accounts. You might also decide that any output used in a business-critical process must be checked by a person before it is relied upon.

These boundaries are not just policy wording. They help shape procurement, configuration, training, and monitoring.

Assess the supplier before you commit

Questions to ask about security, privacy, and model training

Supplier due diligence does not need to be elaborate, but it should be consistent. Ask the supplier how the service is secured, what data it processes, and whether customer content is used to train or improve models. If the answer is unclear, treat that as a concern rather than a minor detail.

Useful questions include: where is data stored and processed, what authentication methods are supported, how are administrative actions logged, how are vulnerabilities handled, and what happens if the supplier uses subcontractors? You should also ask whether the supplier can separate your data from other customers, and whether there are settings to reduce data retention or training use.

If the supplier offers different service tiers, check whether the security and privacy terms change between them. A consumer-style service and a business-grade service may look similar on the surface but offer very different controls.

What to look for in assurance evidence and product documentation

Look for evidence that is specific to the service you are buying. Product documentation, privacy notices, security pages, and service terms are all useful, but they should be read together. Marketing material alone is not enough.

Good signs include clear documentation on data handling, admin controls, logging, access management, incident handling, and retention. If the supplier provides independent assurance reports or security summaries, use them as supporting evidence, not as a substitute for your own review. The key is whether the evidence answers your questions about the actual service you plan to use.

Where possible, keep a short record of what you reviewed and why the supplier was accepted. That makes future reviews easier and helps if the business later expands the use of the tool.

Review data handling and information flows

Understand what data is sent to the model and where it goes

AI services often move data through several places before returning an answer. A prompt may be sent to the model, stored temporarily for processing, logged for support, or passed to connected services. If the tool is integrated with other systems, the flow can become more complex.

Map the information flow in plain English. What is entered by the user, what is pulled from your systems, what is sent to the supplier, what is stored, and who can access it? This does not need to be a formal architecture exercise for every tool, but it should be clear enough that you can spot where sensitive data might travel.

If the supplier cannot explain the flow in simple terms, that is a warning sign. You do not need perfect detail, but you do need enough clarity to make a sensible decision.

Check retention, deletion, and training settings

Retention matters because data that is kept for longer than necessary creates more exposure. Check how long prompts, outputs, logs, and uploaded files are retained. Also check whether you can delete content, and whether deletion applies immediately or after a delay.

Training settings are equally important. Some services allow customer content to be excluded from model training, while others may use it in ways that are harder to control. Make sure the default settings match your intended use, and do not assume the safest option is enabled automatically.

For UK SMEs, a sensible rule is to minimise what is sent to the service in the first place. If a task can be completed without personal data or confidential material, keep it that way.

Build security requirements into the contract

Key clauses to cover support, incidents, and subcontractors

Contracts should reflect the risks you have identified. At a minimum, they should cover support arrangements, incident notification, subcontractor use, data deletion, and what happens when the service ends.

Support matters because AI tools can fail in ways that are not obvious to end users. You want to know how quickly the supplier will respond, how issues are escalated, and whether there is a named route for security concerns. Incident terms should explain how the supplier will notify you if your data is affected, what information they will provide, and how they will support investigation and containment.

Subcontractors are worth checking carefully. If the supplier relies on other providers for hosting, analytics, or model delivery, you need to know whether those parties are covered by the same controls and obligations.

How to avoid relying on vague marketing claims

It is easy to be reassured by phrases such as enterprise-grade security or trusted by thousands of customers. Those statements may be true, but they do not tell you what controls are actually in place.

Focus on specifics. Ask for the actual features, settings, and commitments that matter to your use case. If the supplier says the service is secure, ask how that is achieved. If they say data is protected, ask what that means in practice. If they say they do not train on your data, ask whether that applies by default and whether it is contractually documented.

For SMEs, the aim is not to negotiate a perfect contract. It is to avoid blind spots and make sure the business understands what it is buying.

Control access and integration points

Limit who can use the tool and what they can connect to

Access should be based on need. Not every employee needs access to every AI tool, and not every user needs the same level of permission. Give access only to those who need it for their role, and review it regularly.

If the tool connects to internal systems, be careful about scope. A service that can read shared files, send emails, or query customer records can create more risk than a standalone chat tool. Keep permissions narrow and avoid giving broad access just because it is convenient during setup.

Where possible, use separate accounts for testing and production use. That helps prevent experimental use from affecting live data.

Reduce risk from API keys, plugins, and connected accounts

Application programming interfaces, or APIs, let systems talk to each other. They are useful, but they also create a route into your environment if they are not managed carefully. Treat API keys like sensitive credentials, store them securely, and rotate them if there is any sign of exposure.

Plugins and connected accounts deserve the same attention. Check what each integration can access, whether it can act on behalf of a user, and whether it can be disabled quickly if needed. If a tool offers optional add-ons, only enable the ones you genuinely need.

For many SMEs, the safest approach is to start with the least connected version of the service and only expand access after the business has seen how it behaves in practice.

Plan for monitoring after go-live

Track changes to model behaviour, terms, and supplier posture

AI services are not static. The supplier may change the model, update the interface, alter the terms, or adjust how data is handled. Any of those changes can affect your risk position.

Set up a simple way to notice change. That might include assigning an owner to watch for supplier notices, reviewing release notes, and checking whether the service still behaves as expected. If the tool starts producing different results, asking for more data than before, or offering new integrations, that should trigger a review.

It is also sensible to monitor the supplier’s wider posture. If there are major service issues, ownership changes, or policy shifts, you may need to reassess whether the tool still fits your needs.

Set a simple review cycle for higher-risk tools

Not every tool needs the same level of oversight. A low-risk drafting assistant may only need an annual review, while a tool that processes customer or operational data may need more frequent checks.

A practical review cycle should confirm that the use case is still valid, the settings still match your policy, and the supplier has not changed anything material. It should also check whether staff are using the tool in ways that were not originally approved.

If the tool becomes more important to the business over time, increase the level of oversight accordingly. Risk tends to grow quietly as adoption spreads.

Create a lightweight approval process for SMEs

A practical checklist for procurement, IT, and business owners

For most UK SMEs, the best approach is a short approval process that can be completed without delay. A simple checklist might cover the following: what the tool is for, what data it will process, who owns it, what supplier evidence was reviewed, what contract terms were checked, what access controls are in place, and when the next review will happen.

Keep the process proportionate. A low-risk tool should not need the same level of scrutiny as a service that handles customer records or integrates with core systems. The point is to make risk visible, not to block useful technology.

It can also help to publish a short internal rule for staff. If people know which tools are approved, what data they may use, and where to ask for help, they are less likely to make ad hoc decisions.

When to escalate for specialist advice

Some situations justify extra support. Escalate if the tool will process sensitive personal data, confidential commercial information, regulated data, or material that could affect important decisions. Also escalate if the supplier is unclear about data use, the contract is heavily one-sided, or the integration touches critical systems.

Specialist advice can help you test assumptions, compare options, and shape controls that fit your business. For SMEs, that is often more efficient than trying to solve every issue alone.

Securing AI procurement and third-party models is mostly about asking the right questions early. If you define the use case, check the supplier, understand the data flow, and keep control after go-live, you can adopt AI in a way that supports the business rather than surprising it.

If you would like help reviewing an AI supplier, setting a lightweight approval process, or aligning third-party AI use with your wider security controls, speak to a consultant.

Frequently asked questions

What should UK SMEs ask before buying an AI tool from a third party?

Ask what the tool will be used for, what data it will process, where that data goes, whether it is used for training, how long it is retained, what security controls are in place, and what happens if there is an incident or service change. You should also check whether the supplier uses subcontractors and whether the contract reflects the answers you receive.

How do we reduce risk when staff want to use external AI services quickly?

Give staff a short approved-use policy, define which data must never be entered, and provide a simple route for requesting new tools. Where possible, start with low-risk use cases and limit access to the smallest practical group. A quick approval process is better than no process, as long as it covers data handling, supplier checks, and basic access control.

Do we need a formal risk assessment for every AI tool?

Not necessarily. For many SMEs, a lightweight assessment is enough for low-risk tools. The depth of review should match the sensitivity of the data, the importance of the process, and the level of integration with your systems. Higher-risk tools deserve a more detailed review.

What is the biggest mistake SMEs make with third-party AI?

The most common mistake is treating the tool as a simple productivity app and not a supplier with access to business information. Once the service is connected to real data or used in important decisions, it should be handled as part of your wider third-party risk management.