EDITOR ’ S QUESTION

The potential benefits – streamlining operations , enhancing customer experiences and driving innovation – are undeniable . Yet , Generative AI is a minefield of risks , particularly when it comes to security , ethical decision-making and ensuring the truthfulness of its results . To navigate this landscape effectively , CIOs need to address several critical considerations .

First and foremost is data . The adage ‘ garbage in , garbage out ’ has never been more relevant . Generative AI is not a magical solution ; its outputs are only as good as the data it ’ s fed .

If the input data is incomplete , outdated , or lowquality , the AI will inevitably produce flawed or even harmful results . Fragmented or siloed data – a common issue in many organisations – further compounds this challenge . strategy enables AI to provide actionable insights and meaningful results .

Another critical consideration is security and compliance . Generative AI models , if not managed correctly , can introduce vulnerabilities . Organisations must ensure that their AI implementations comply with regulatory requirements to avoid legal and financial repercussions .

For instance , leveraging Retrieval-Augmented Generation ( RAG ) systems that are permissionsaware can help safeguard data integrity and maintain compliance . These systems ensure that sensitive information is handled appropriately while allowing AI to access and utilise only the necessary data .

Generative AI is a minefield of risks , particularly when it comes to security , ethical decisionmaking and ensuring the truthfulness of its results .

When data is scattered across departments or locked within disparate systems , AI cannot form a comprehensive , accurate view of the business . This leads to suboptimal outcomes , misinformed decisions , and , in some cases , reputational damage .

The solution lies in ensuring our data is unified , highquality and updated in real time . This effort isn ’ t about amassing more data but about gathering better data . A global data repository that connects and integrates all organisational data into a seamless infrastructure is essential .

Think of this as laying the groundwork for a solid foundation . Without this step , deploying Generative AI risks amplifying existing data chaos rather than solving problems . By contrast , a well-structured data

Ultimately , integrating Generative AI can create immense business value , but success hinges on laying the right groundwork . Organisations that invest in robust data infrastructure and prioritise security and compliance will be better positioned to harness AI ’ s potential . Conversely , those that neglect these foundational elements risk inefficiencies , inaccuracies and vulnerabilities .

The companies that will thrive in this new era are those that recognise the importance of quality over quantity when it comes to data . By addressing these challenges proactively , CIOs can ensure that Generative AI delivers true value and drives sustainable growth . It ’ s not just about adopting the latest technology ; it ’ s about doing so thoughtfully and strategically to unlock its full potential . p

ARON BRAND , CTO OF CTERA

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