Written by Tim Deakin

26th November 2025

• Cyber Security

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Artificial intelligence has moved from a future concept to an everyday business tool. Even those people who aren’t yet relying on AI for their own day-to-day tasks are almost certainly interacting with solutions on a daily basis that are reliant on this incredibly powerful innovation. From chatbots to online search to workflow automation, AI is everywhere in our daily lives now.

But with rapid adoption comes new risk. And any security-conscious businesses needs to get a handle on what it means to implement AI safely.

As cyber security consultants, we see first-hand how quickly AI can introduce vulnerabilities if it’s deployed without a clear strategy. This guide outlines the practical steps every organisation should take to adopt AI securely and confidently.

Start with a risk assessment 

Before selecting an AI tool, understand:

• What data it will access
• Which processes it will influence
• How critical those processes are to your organisation
• What the impact of misuse, manipulation or failure would be

AI tools are not inherently risky but the context they operate in may be. Classification of data, threat modelling and impact assessments should happen before any procurement discussions get underway.

Understand where your data goes

Many AI tools (particularly cloud-based and generative AI systems) send data to external servers and store queries or outputs for model training. Not only that but the use of third-party APIs and reliance on infrastructure outside the UK can mean that a UK business has very little visibility over the way data is managed, monitored or utilised. To handle this issue adequately, businesses must ensure:

• Data processing agreements (DPAs) are in place
• The AI provider meets UK GDPR requirements
• Sensitive or regulated data never enters public AI models
• Data retention and deletion policies are clearly defined

If you cannot confidently answer “Where does our data go?”, your organisation isn’t ready to deploy that tool.

Restrict access and apply the principle of least privilege

AI systems often require elevated access – whether to emails, databases, documents or internal apps. Poorly controlled access is a major threat vector. For this reason, organisations have a responsibility to implement:

• Role-based access control
• Multi-factor authentication
• Segregated access for developers, admins and end-users
• Regular permission reviews

No employee or AI process should have more access than absolutely necessary.

Validate and monitor AI outputs

No AI tool is infallible. In fact, all language models on the market today have been shown to provide incorrect data, fabricated facts, biased results and vulnerable code under certain circumstances. 

To combat this issue, businesses have a responsibility to introduce:

• Human-in-the-loop validation for critical decisions
• Code review gateways for AI-generated software
• Output audits to ensure compliance
• Clear accountability for approving and using AI recommendations

Protect your organisation from prompt injection and manipulation

AI models can be tricked by carefully crafted inputs designed to reveal confidential data, override internal instructions and deliver privileged information to unauthorised users. This attack vector can be countered by:

• Using guardrails and model constraints
• Implementing input sanitisation
• Training employees to avoid risky or ambiguous prompts
• Prohibiting public models from interacting with sensitive systems

Prompt injection is one of the fastest-growing AI attack techniques and should therefore be treated as seriously as phishing.

Next steps

If your organisation is looking to adopt AI in a responsible and mature fashion, here are a few steps you can take right now:

Create clear internal AI use policies

A strong policy should outline:

• Approved AI tools
• Prohibited data types
• Rules for handling confidential information
• Requirements for documentation and audit trails
• Expectations for staff behaviour
• Procedures for evaluating new AI technologies

Without a policy, staff will adopt AI informally — often in insecure or non-compliant 

Secure the AI supply chain

AI relies on a combination of third-party vendors, open-source models, algorithm libraries and external APIs. Each of these introduces a layer or risk, which requires businesses to:

• Perform supplier due diligence
• Assess model provenance
• Ensure patching and updates are maintained
• Review the vendor’s security certifications
• Require contractual assurances around model integrity and training data

AI systems evolve, learn and interact with new data continuously. That means security must be continuous too. With that in mind, be sure to establish a process for:

• Ongoing monitoring of performance and anomalies
• Logging of both inputs and outputs
• Alerts for suspicious activity
• Routine re-evaluation of the model’s behaviour
• Periodic re-testing against emerging threats

By approaching AI deployment with the same discipline you apply to any other critical system – risk assessment, validation, monitoring and policy – your organisation can innovate confidently without exposing itself to unnecessary risk.

If you’re reading to improve your business’ defences, you’re in the right place. Our dedicated cyber security consultants can help to protect your organisation against ever-growing threats. Get in touch here.