Intelligent CISO Issue 1 - Page 34

P RE D I C T I V E I NTELLIGEN CE 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 34  secure their networks. While these buzzwords are already in place, they have been defined by programmer- built 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 Issue 01 |