t cht lk be using is already a challenge . Departments or even individuals building or adapting large language models , LLMs will make it even harder to manage and track data movement and risk across the organisation .
t cht lk be using is already a challenge . Departments or even individuals building or adapting large language models , LLMs will make it even harder to manage and track data movement and risk across the organisation .
The fact is , it is almost impossible to have complete control over this , but putting processes and training in place around data stewardship , data privacy , and IP will help . If nothing else , having these measures in place makes the company ’ s position far more defendable if anything goes wrong .
It is not about being the progress police . AI is a great tool that organisations and departments will get enormous value out of . But as it quickly becomes part of the technology stack , it is vital to ensure these fall within the rest of the business ’ s data governance and protection principles .
For most AI tools , it is about mitigating the operational risk of the data that flows through them . Broadly speaking , there are three main risk factors :
Individuals building large language models will make it even harder to manage and track data movement and risk across the organisation .
• Security , what if an outside party accesses or steals the data
• Availability , what if we lose access to the data , even temporarily
• Accuracy , what if what we are working from is wrong
This is where data resilience is crucial . As AI tools become integral to your technology stack , you need to ensure visibility , governance , and protection across your entire data landscape . It comes back to the relatively old-school CIA triad – maintaining confidentiality , integrity , and availability of your data . Rampant or uncontrolled use of AI models across a business could create gaps .
Data resilience is already a priority in most areas of an organisation , and LLMs and other AI tools need to be covered . Across the business , you need to understand your business-critical data and where it lives . Companies might have good data governance and resilience now , but if adequate training is not put in place , uncontrolled use of AI could cause issues . What is worse , is you might not even know about them .
Ensuring data resilience is a big task – it covers the entire organisation , so the whole team needs to be responsible . It is also not a one-and-done task , things are constantly moving and changing . The growth of AI is just one example of things that need to be reacted to and adapted to .
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