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What does it mean for their organization?
What are the plans for its use?
And how quickly can they be enacted?
As such it has been mainly concerned with questions of compute power, infrastructure, storage, skills etc.
Chief Technology Officer at Zscaler.
Data dilemma 1: What data should you be using?
However, its not as simple as that, raising issues aroundprivacy, bias and inequality.
But while private data might be more valuable as a result, it is also more sensitive and confidential.
So what is the incentive to do so?
Data dilemma 2: How long should you be keeping your data?
So, what if GDPR tells you to delete older data?
Or someone asks to be forgotten?
So how do you balance the two?
Data dilemma 3: How do you train GenAI to avoid the use of confidential data?
Security vs. categorization
By law companies must secure their data or face significant fines for failing to do so.
And this is where the difficulty sets in.
Confidential data should be given the highest possiblesecurityclassification and kept well clear from any GenAI engines as a result.
Or worse, ignore them at the expense of being able to move more quickly with their GenAI implementations?
Given this, how should companies start taking charge of their GenAI approach?
Competitively this might be to your detriment, but ethically it is far too dangerous not to.
Bear bias in mind restrict AIs which conclude based on biased public information and do not verify their content.
Validate your own results.
Existing regulations must take priority ensure GDPR rules and right to be forgotten practices are observed.
This will mean considering how often to reapply your AI processing engine and factoring this into plans and budgets.
Sometimes the answer isnt GenAI.
But they need to find ways of doing so without compromising the dilemmas weve introduced above.
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