CASE STUDY security from having full coverage to detect all threats , autonomous threat response , AI hallucinations and the current marketing hype that leads customers into believing , because of AI , they don ’ t need to find , hire and train the best human capital to do the job .
Cybersecurity leaders in most organisations today face the real challenge of retaining their top talent due to the global shortage of skilled cybersecurity human capital . This problem is sometimes exacerbated by vendors who focus heavily on addressing this one single pain point amongst many others by over-selling and overpromising the autonomous vision .
This becomes a bigger problem when marketing and sales teams start to misinform their customers to a point where organisations become complacent or over reliant on third party ML-based solutions rather than focusing on educating and training employees on the proper use of AI in Cybersecurity .
Vendors who will survive this revolution and dominate this space in the market have already understood this concept and are working with their customers today to take them on the right path to achieve the desired results required to successfully leverage and deploy AI in cybersecurity . This is achieved by emphasising the importance of the human security analyst ’ s skills to utilise AI within a broader , complex socio-technical system and context .
What are some key security considerations for AI adoption ?
There have been extensive discussions about the origins of data used for the training and tuning of AI models . Organisations will need to make careful decisions about what data they make available and whether they can remain compliant with industry standards and business priorities . When deploying AI-powered security solutions , the organisation should evaluate a vendor ’ s approach to transparency regarding the construction of their models and its inputs , as well as the vendor ’ s risk management framework as explained below :
Oversight of outputs : AI hallucinations , data poisoning and data modification will continue to be serious concerns for any AI use case . The value of employees with skills for evaluating AI outputs will continue to rise .
Plugging into a virtuous feedback loop : Customer experience with AI models has the potential to greatly improve the performance of AI-powered tools .
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