From insiders to sophisticated 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 not sufficient to combat new, evolving attacks, and no human cyber analyst can watch so much or react quickly enough. A fundamentally new approach to cyber defense is needed to detect and respond to these threats that are already inside the network - before they turn into a full-blown crisis.
Self-learning systems represent a fundamental step-change in automated cyber defense, are relied upon by organizations around the world, and can cover up to millions of devices. Based on machine learning and probabilistic mathematics, these new approaches to security can establish a highly accurate understanding of normal behavior by learning an organization’s ‘pattern of life,’. They can therefore spot abnormal activity as it emerges and even take precise, measured actions to automatically curb the threat.
Discover why autonomous response and machine learning is the future of defense and how the ‘immune system’ approach to cyber security provides complete network visibility and the ability to prioritize threats in order to better allocate time and resources.
In this session, learn: - How new machine learning and mathematics are automating advanced cyber defense
- Why full network visibility allows you to detect and autonomously respond to threats
- How smart prioritization and visualization of threats allows for better resource allocation and lower risk
Real-world examples of unknown threats detected by ‘immune system’ technology
