Among its many benefits to cybersecurity, Artificial Intelligence (AI) can identify patterns in massive amounts of data, enabling it to detect trends in malware features and make threat classifications much more rapidly than humans can. An AI-based virtual security operations (SecOps) analyst can rapidly detect and respond to security incidents, assisting human analysts and enabling them to operate at a higher level. AI-powered cybersecurity technologies such as this can be a boon to short-staffed security teams affected by the global cybersecurity skills gap.
While Machine Learning (ML) is the most common type of AI used in cybersecurity designed to solve linear problems e.g. perform a task more efficiently and effectively for a specific situation, Deep Learning (DL) is designed to solve larger complex, non-linear problems by modelling the operation of neurons in the human brain.
AI-based learning algorithms fall into three categories: supervised, reinforced and unsupervised. A supervised ML algorithm must be trained on a large dataset of samples labeled as either benign or malicious. In contrast, Deep Neural Networks (DNN), a Deep Learning model uses reinforced learning i.e. an award-based system of learning, during its pre-training and later transitions to unsupervised learning i.e. self-learning, that does not require a labeled dataset for training and maturity. More importantly, lies in its ability to correlate various category of datasets to make decisions.
A Virtual Security Analyst that can operate in unsupervised mode is a boon to lean SecOps teams that lack the experienced resources to analyze and investigate new threats fully within the shortest period of time. Because of DNN’s innate ability to self-learn, it continuously adapts to the evolving cyber threat landscape including AI-powered cyber attacks.