Cyber-attacks are not confined to larger enterprises, and recent breaches have shown that mid-market businesses are just as likely to face severe disruptions leading to halted production lines and wreak havoc on supply chains.
With the examples of the Blue Yonder’s breach last year, exposing sensitive data and disrupting supply chains, to the more recent breach at Peter Green Chilled, a food supplier to major UK supermarkets, earlier this year, which led to halted orders and thousands of wasted products – incidents like this highlight why mid-sized businesses must strengthen their cyber resilience, and why AI is emerging as a critical enabler.
AI is already transforming cybersecurity strategies, from automating threat detection and responses, to reducing alert fatigue and streamlining routine security tasks. These capabilities help mid-market firms stay ahead of increasingly sophisticated attacks. However, while AI-powered security tools are designed to reduce risk, they can also introduce new vulnerabilities if not properly governed. Therefore, to fully realise AI benefits, businesses must implement and govern it with discipline across all functions.
AI and cybersecurity: A balancing act for mid-market organisations
Given the severity of this year’s cyber-attacks, every business leader should prioritise cybersecurity governance with a strong focus on how AI tools are implemented and monitored. This puts both internal and external suppliers in the spotlight.
Half of the security breaches in the UK stem from supply chain vulnerabilities, yet nearly half (44%) of mid-market businesses outsource their cybersecurity. While this may seem practical for reducing costs and freeing up internal resources, it often results in limited visibility into how data is being protected, increasing exposure to attacks.
With tighter margins and leaner teams, mid-market organisations must be agile and budget-conscious, while maintaining a competitive edge. If they’re unable to bring their security governance in house, it’s vital that they demand transparency from their suppliers, to ensure oversight into how their data and information is protected. Understanding what tools are being used, how they are governed, and what risks they might pose not only builds trust and strengthens defence strategies, it also helps employees understand how customer data is managed.
For organisations managing cyber security in-house, clear AI frameworks outlining company guidelines and best practices empower employees to use AI confidently and securely. By taking a strategic approach to upskilling, mid-market firms can strike the right balance in strengthening their cybersecurity posture while unlocking long-term resilience and growth.
Implementing robust AI Frameworks
To ensure secure AI use across the mid-market, clear guardrails must be established, which provide comprehensive guidance before rolling out AI tools. With the risk of shadow AI (employees using AI tools without formal approval), businesses need to put these in place sooner rather than later. These guidelines should define business priorities and usage policies, covering areas such as data governance, roles and responsibilities, and proof-of-concept initiatives. This helps lay a secure foundation that embeds risk management, security, and trust into everyday processes. While AI inevitably introduces new challenges, addressing them proactively enables a safer and more productive experience.
Departmental autonomy also plays a critical role. Leaders must identify where the most significant cybersecurity threats lie, and how AI intersects with those vulnerabilities. This includes evaluating risks within internal teams and fostering a culture of AI literacy, where employees feel confident using the technology responsibly. Establishing boundaries around autonomy before rollout helps manage risk, ensure compliance, and maintain control, laying the groundwork for secure and scalable adoption.
Navigating AI investment and employee understanding
Once businesses have laid out the foundation for AI implementation and trained employees on best practices, it’s crucial they continue to monitor its use through a security lens. Tools that give decision-makers visibility, such as automated threat detection systems, anomaly monitoring platforms, and intelligent access controls, ensure effective and secure deployment. In cases like Blue Yonder, AI-powered anomaly detection and automated responses could have flagged suspicious activity earlier, reducing the impact and preventing data exposure.
While frameworks guide initial adoption, they shouldn’t be seen as a final solution. Ongoing evaluation is key to staying ahead of digital transformation. Leveraging AI while maintaining oversight enables mid-market businesses to streamline security operations and enforce policies consistently.
As the technology evolves, AI education must remain a priority for business leaders across departments. Long-term success hinges on ingrained knowledge and habits across the workforce. Upskilling employees on best practices and strategic implementation ensures usage remains compliant, secure and aligned with business objectives. Investing in employee empowerment reinforces frameworks and reduces the risk of misuse or vulnerabilities.
Proactive vs reactive AI: Unlocking cybersecurity success
Leveraging AI is key for mid-market businesses to unlock their competitive advantage. These organisations are inherently agile, often able to move from concept to implementation in weeks, with fewer layers of bureaucracy. However, while speed is a strength, it must be balanced with strategic planning, governance, and long-term value to ensure AI is implemented securely and sustainably.
To stay ahead of increasingly sophisticated threats, UK businesses must invest in AI upskilling and prioritise cyber resilience across all departments. This means shifting from deploying reactive measures to prioritising and developing proactive, preventative strategies. AI is a powerful asset, but without the right strategy, governance and education, it can quickly become a liability.
