PREDICTIVE INTELLIGENCE
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Once a threat actor is inside an organisation ’ s network , they are unable to distinguish between real and fake user identity credentials .
begin to tap the power of artificial intelligence and machine learning , to secure their networks . While these buzzwords are already in place , they have been defined by programmerbuilt algorithms , limiting the amount of self-learning . Machine learning applied to cybersecurity has traditionally been driven by algorithms that give instructions on the types of malware and their associated behaviour inside internal networks . Now machine learning will be replaced by deep learning applied to cybersecurity .
With deep learning techniques , cybersecurity applications are aided by self-learning technologies . User behaviour is monitored over a period of time using deep learning technologies and a user behaviour profile is established . This profile is a dynamic one and deep learning technologies continue to add usage patterns , till the profile becomes intrinsic to a particular user . Deep learning applications develop highly granular patterns and analysis of end user activities .
The presence of a threat actor inside a network using an assumed credential , will have a deviant user pattern . This divergent pattern of accessing the network , monitored by behavioural analytics , will trigger a security remediation alert without delay . Examples of such proactive and rapid approach to securing convergent and transformative networks , will take behavioural analytics applied to cybersecurity to a new level .
With these intuitive gains around the corner , cybersecurity vendors will
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