AI’s hype and promise in cybersecurity is balanced by trepidation and risk. Here’s where to focus., whereby I agree to provide Gartner with my personal information, and understand that information will be transferred outside of mainland China and processed by Gartner group companies and other legitimate processing parties and to be contacted by Gartner group companies via internet, mobile/telephone and email, for the purposes of sales, marketing and research.
Often what follows are months of trial and error followed by a retroactive assessment, financial write-off and sometimes a sacrificial executive departure . The larger impact comes later in the form of opportunities lost with the delayed rollout of generative capabilities. “Prompt fatigue” — too many tools offering interactive interface to query about threats and incidents
Ask “Is it worth it?” Set expectations for your investment and measure yourself against those targets. Evaluate efficiency gains along with costs. Refine detection and productivity metrics to account for new GenAI cybersecurity features. The fact that once raw data is put into a model, there is no easy way of removing it, short of rebuilding the model, which is not practical and extremely expensiveExternally hosted LLMs and other GenAI models increase these risks, as enterprises cannot directly control their application processes and data handling and storage. However, there is also risk in on-premises models hosted by the enterprise — especially when security and risk controls are lacking.
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