CIO OPINION for a private business , both in terms of lost consumer trust and regulatory penalties .
Additionally , Generative AI brings in a host of concerns related to AI and data governance . Large language models , for instance , can lead to non-compliance with privacy and residency regulations , as well as hallucination effects emanating from subpar training data . In both these cases , data governance and AI governance become intertwined .
Now let us consider stakeholders . If we look at the stakeholders on the data and AI sides , we see significant differences . Database administrators , maintenance teams , IT administrators , compliance officers , and business users can all be considered stakeholders in the organisation ’ s data .
Key takeaways
• Leaders that exhibit attention to detail are commonly the most effective .
• Federal Decree Law No 45 of 2021 , referred to as the Personal Data Protection Law , is aimed at every aspect of data storage and handling .
• The UAE government has used its own tiller to navigate between nurturing innovation and protecting private individuals .
• Even the European Union has an AI Act that empowers the levying of significant penalties for non-compliance .
• Businesses must be aware of and navigate external rules as they digitalise their operations .
• Understanding where data governance ends and where AI governance begins is vital to remaining on the good side of regulators .
• When implementing data governance , look first to policies , centralised catalogues , and stewardship , and strive to maintain data quality .
• The effectiveness of AI governance will hinge on the level of observability of AI systems .
• It is in implementation where we can see differences between AI and data governance and AI frameworks should stand apart from cataloguing datasets .
• AI governance is about enforcing rules around implementation of solutions to avoid a range of errors during development and deployment .
• The effectiveness of AI governance will hinge on the level of observability of AI systems .
• In digitally competitive economies , a misstep could have implications for business , both in terms of lost consumer trust and regulatory penalties .
• It is vital that all people collaborate under common leadership and AI governance must be consistent , especially as new models emerge .
• Critical to the process is a platform that unites IT , data science , and business management through visibility into all projects .
• AI governance assures internal and external parties that models are being deployed , operated , and monitored fairly .
• As AI becomes enmeshed in everyday lives , AI governance will be the glue that holds everything together .
• Understanding how AI governance differs from data governance can prevent predictive and generative solutions from causing problems .
When we turn our attention to AI , though , we see a far broader range of experts and beneficiaries , from data scientists and machine language engineers to risk managers and lawyers , not to mention ethics specialists and line-of-business executives .
It is vital that all these people collaborate under common leadership . AI governance must be consistent , especially as new models and use cases emerge . Critical to the process is a platform that unites IT , data science , and business management through comprehensive visibility into all projects . This platform must be transparent about performance and status so that information is appropriately actionable .
As we have seen , AI governance and data governance are both essential and distinctive and yet one can have great impact upon the other . Data accuracy and security build trust in different ways ; the former relates to the trustworthiness of models , and the latter relates to guarantees of privacy .
AI governance assures internal and external parties that models are being deployed , operated , and monitored fairly , accountably , transparently and in a way that allows those with questions to get answers to those questions in a timely manner .
As time goes on , and AI becomes more and more enmeshed in our everyday lives , AI governance will be the glue that holds everything together . Understanding it today , and how it differs from data governance can prevent predictive and generative solutions causing problems tomorrow .
Organisations must strive to enhance the AI literacy of all their people and revisit the concept of governance for the good of all future projects . So , reach for the tiller and steer wisely . p
40 INTELLIGENTCIO AFRICA www . intelligentcio . com