Presented by Darktrace
Even if we focus on the nuanced sophistication of external attackers, the reality of cyber security today is that the threat is already inside. Legacy approaches to cyber security, which rely on knowledge of past attacks, are simply insufficient to combat new and evolving attacks; no human cyber analyst can watch or react quickly enough. In order to avoid full-blown crises, a new fundamental approach to cyber defense is imperative in order to detect and investigate preexisting threats inside the network.
Self-learning systems represent a critical step-change in automated cyber defense. Organizations around the world rely on these systems, which can cover up to millions of devices. Based on unsupervised machine learning and probabilistic mathematics, these new approaches to security can establish a highly accurate understanding of normal behavior by learning an organization’s “patterns of life.” Hence, such security systems can spot abnormal activity as it emerges and even take precise, measured actions to automatically curb the threat.