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Implementing AI with confidence: can you protect your organisation without blocking progress?

6 min read 21 March 2025 By James Hampshire, David McGibbon, Priya Pandalaneni and Gavin Singh, experts in Technology and Cyber Security

Business leaders face a formidable challenge: how to harness the transformative power of AI while ensuring the organisation’s cybersecurity and resilience 

Artificial Intelligence (AI) is revolutionising operational efficiency and driving innovation at an unprecedented pace. As a result, it’s rapidly becoming the most desired technological capability for organisations’ digital operations – from cloud services and automated systems to data analytics and customer interactions. Organisations that fail to keep pace in adopting AI risk leaving business value on the table and falling behind competitors.   

New technology, new risks 

As AI adoption surges, governments are becoming increasingly alert to its risks. They are striving to strike a delicate balance – safeguarding their national security and economies, as well as individuals’ rights and freedoms, while preserving vital opportunities for growth. The recent emergence of the Chinese generative AI, DeepSeek, for example, has underlined the importance of understanding how third-party AI systems ingest data, where it is stored and what it is later used for. 

Business leaders face a similar dilemma. Under mounting pressure to embrace AI more rapidly, many are grappling with unease about the new risks it introduces. Is the organisation adequately protected as it pursues the benefits of AI? Are existing security and resilience frameworks sufficient to address the emerging challenges, particularly around protection of personal data and IP? And on the other hand, is fear, uncertainty and doubt blocking AI innovation at pace? 

Finding the right balance now between opportunity and risk is key to capitalising on AI effectively while protecting the organisation from unmitigated risks. 

Technology on steroids, but still technology 

The good news is that while this wave of AI solutions may be new and harder to understand than familiar digital technologies, AI is fundamentally just another technology that security teams must defend in a way that is aligned with the organisation’s risk appetite. The basic principles of risk management, resilience and cyber security remain the same. Business leaders should be confident these principles are being applied effectively in their organisations and are regularly tested and assessed.  

In addition, here are four ways leaders can help their organisations adopt AI with confidence: 

Balance security risk with business value 
Like any emerging technology, AI will never be completely risk free. This doesn’t mean security should be a barrier to hold back the tide or block the organisation from realising new opportunities. AI is already transforming industries – and organisations must embrace its transformative potential to compete successfully. For CISOs, this means adopting a balanced, business-led mindset. Articulate the risks and controls clearly, but make sure security efforts are balanced with business goals and value. Informed and managed risk-taking can drive innovation and growth.  

Ensure you have enough knowledge to engage 
For business leaders, our advice is to take time to understand potential AI use cases and technologies – both in-house and externally-facing – and be clear on the associated security risks. A level of knowledge about different types of AI and their application is the table stakes required to engage in informed conversations about the pace of AI adoption. 

Engage in rich conversations about AI adoption 
Don’t assume someone else is managing AI security risks. Get involved in conversations about AI adoption and ensure security is being considered throughout the development and deployment life cycle of AI systems, right from the outset. Use frameworks and guidelines, such as these from the UK’s National Cyber Security Centre, to ensure AI systems are securely designed from the outset. 

Build AI security risks into your cyber security and risk management framework  
As organisations adopt AI systems, CISOs should apply the same principles as with any security or technology changes: the key is balancing security risk and business value. Incorporate AI security into the organisation’s wider cyber security and risk management framework. A holistic approach will help mitigate potential threats more effectively and enhance the overall resilience of AI-driven processes.   

Taking the lead on security and resilience 

AI has the power to reshape industries and revolutionise businesses. But even with the technology still in its infancy, it’s already clear that it brings a wave of new and rapidly evolving security risks. To ensure their organisations adopt AI safely, business leaders must take decisive action. By understanding AI systems and actively driving conversations around responsible AI adoption, leaders can unlock AI’s full potential while safeguarding systems, data and users against emerging threats. Protection and progress must go hand in hand. 

 

What AI security risks should you be aware of? 

AI brings exciting benefits, but it also introduces new vulnerabilities that can be exploited by malicious actors. Key security risks associated with AI systems include: 

Model manipulation – Attackers could manipulate models to give incorrect responses. For example, data poisoning can manipulate or corrupt the data used to train to make models generate flawed decisions, or return incorrect (or even offensive) data, compromising the initial intent of the service. 

Privacy violations – AI systems processing personal or sensitive data without proper consent, safeguards or data governance in place, can result in significant privacy violations or introduce bias based on protected characteristics. This can potentially lead to breaches of regulation – for example GDPR – causing huge reputational damage and eroding customers’ trust. 

Single points of failure – As organisations integrate AI into core business operations, AI models may become critical to key services. A failure in one AI system can lead to a cascade of issues such as market failures, data breaches and even disruption to critical national infrastructure.  

Insecure code – Software development teams are increasingly using AI to develop code more efficiently. However, if AI-developed code is flawed or not sufficiently scrutinised, it can introduce security vulnerabilities into systems, putting the organisation at risk. 

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