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Confronting the threat of Shadow AI
In the early days of cloud data storage, many CISOs and IT professionals applied bans on cloud usage across the board, resulting in the rise of Shadow IT. Could the same thing happen with AI? History says yes. The desire for competitive advantage means users will find a way to use AI whether or not the IT department sanctions it. Join this discussion on how AI services should be made available to users with solid guardrails to reduce risk from Shadow AI.
Private data versus public data Large Language Models (LLMs)
How does the quality of data inputs affect the quality of AI outputs?
What are the inherent flaws in business decisions based on public LLMs?
What are the ethical and potential future legal ramifications of using public LLMs?
Data protection in an AI-enabled organization
Where are the risks of inadvertent data leakage when using AI? Can these be quantified?
How do data classification and data loss prevention (DLP) capabilities come into play?
Is it possible to detect and reverse unwanted private data ingestion?
