Introducing AI Securely
Artificial Intelligence (AI) is changing how organisations work, make decisions and deliver value. This promises improved productivity and better customer service. It can also introduce new cyber, operational, legal and reputational hazards. How can businesses reap the benefits of AI without unacceptable risks?
Start with Business Priorities
Secure AI adoption programme begins by understanding what really matters to the organisation: its critical services, sensitive data, customers, intellectual property, regulatory obligations and strategic objectives. This mirrors a mature approach to cyber security, where the focus is on protecting the business outcomes that matter most, rather than deploying technology for its own sake. AI should be introduced where it clearly supports business priorities and where the associated risks can be understood, owned and managed.
Equally, leaders should define where AI should not be used because the risks are unacceptable.
This prioritisation stage cannot be a purely technical activity. It must involve business leaders and process owners. The aim is a shared understanding of AI risk and opportunity. Rather than beginning with a list of controls, the organisation should agree the outcomes it wants to achieve.
Assess AI Risk in Context
Official guidance, including the NCSC Guidelines for Secure AI System Development, the NIST AI Risk Management Framework, ISO/IEC 42001 and the EU AI Act, emphasises the importance of managing AI across the full life cycle. This includes considering traditional cyber threats, such as unauthorised access, data leakage, supply chain compromise and insecure integrations, alongside AI-specific risks such as prompt injection, model manipulation, hallucination, over-reliance on automated decisions and inappropriate use of confidential information.
For each proposed AI use case, the organisation should document the purpose of the system, the data it will use, the users who will access it, the decisions it may influence, the controls required and the level of human oversight needed. High-risk use cases, such as those involving personal data, regulated decisions, critical operations or customer-impacting outcomes, should receive deeper review before approval. Lower-risk productivity uses may be permitted more quickly.
Establish Risk Ownership and Governance
As with the business prioritisation stage, AI governance cannot be owned by IT or cyber security alone. A practical governance model might include an AI steering group or oversight forum, clear approval routes for new use cases, named business owners, risk acceptance thresholds, escalation routes and review cycles.
A governance model should also explicitly address shadow AI. This is the unmanaged, unapproved use of AI tools by employees, which is now one of the most common ways organisations lose visibility of where sensitive data is going.
Protect Data and Secure AI Use
Data security is central to safe AI adoption. Organisations that already have good classifications in place can safely adopt AI more quickly. Those that have not would benefit from doing this foundational work first, to inform what data is appropriate for AI tools and what cannot be entered into public or third-party services. Because AI models are typically physically located and trained abroad, often in low-income countries, it is important to consider legal protections too, such as the UK GDPR and the Data Protection Act 2018, and whether workarounds such as the UK-US Data Bridge are applicable.
This will often require updates to supply chain security, to make sure that AI-specific risks are captured, including the foundation model creator, model training providers, training data providence and sub-processors. As Satya Nadella, the CEO of Microsoft has said, another risk to consider is data leakage or ‘paying the AI supplier twice’, once for tokens and a second time with information, ultimately making your AI supplier a competitor.
Manage Change, Culture and Ways of Working
As with any other business change, implementing AI successfully requires corresponding cultural change. Employees need to understand both the opportunity and the limits of AI. Staff should not be made to feel that AI is being imposed on them; they should be involved in identifying good use cases and improving processes.
New ways of working should include human review of important outputs, transparent labelling where AI has materially contributed to content or decisions, clear rules for record keeping and periodic review of effectiveness. Incident response scenarios should include AI-specific breaches: for example, a hallucinated output feeding into a client deliverable, not just conventional data breaches.
Continually Evaluate and Improve
AI adoption is not a one-off implementation. Organisations should review AI use cases regularly, measure whether expected benefits are being achieved, monitor incidents and near misses, and update controls as risks change. Regular self-assessment workshops help business leaders identify gaps and assign maturity ratings. This helps to build ownership across the organisation for the required improvements identified.
The Cyber Maturity Accelerator™
By taking a business-led approach, organisations can introduce AI with confidence. The Cyber Maturity Accelerator™ gives structure to that journey, helping organisations prioritise the right opportunities, act with clear governance, cultivate secure behaviours and continually evaluate progress. The goal is not to slow innovation, but to make it sustainable and safe.
PACE sets out four steps that move an organisation from ambition to controlled adoption: business engagement (Prioritise), flexible governance (Align), behavioural change (Cultivate) and measured continual improvement (Evaluate). In the Prioritise stage, leaders identify where AI can create value, what information and services must be protected, and which use cases carry the greatest business risk, ensuring AI investment is aligned to strategic outcomes rather than driven by isolated experimentation.
A practical example
One Bee-net client, a private equity firm with £23 billion under management, had allowed shadow AI use to grow informally across teams, with different tools, suppliers and data sets operating without consistent oversight. This created rising cost, duplicated effort, unclear accountability and increasing cyber, legal and regulatory exposure. IT was under pressure to approve risky use cases while being blamed for slowing progress. Bee-net helped business leaders break the deadlock by clarifying where AI could create value, which use cases deserved priority and what legal, regulatory and contractual obligations applied. Applying our PACE methodology to the AI technologies already in use we produced a cyber risk assessment covering data exposure, access, supplier risk, and output reliability. This reduced unmanaged risk, improved business control and the CISO was viewed as a partner rather than an obstacle to rapid implementation.
Ready to bring your organisation's AI use under control? Talk to Bee-net about a Cyber Maturity Accelerator™ assessment for your AI adoption programme.