Date(s) - Feb 10, 2017
11:45 am - 1:15 pm
Self-Learning Defense – Identifying Early-Stage Threats with and Enterprise Immune System
From insiders to sophisticated external attackers, the reality of cyber security today is that the threat is already inside. A fundamentally new approach to cyber defense is needed to detect and investigate these threats that are already inside of the network, before they turn into 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 unsupervised machine learning and probabilistic mathematics, these new approaches to security can establish a highly accurate understanding of normal behavior by learning and organizations “pattern of life.” They can therefore spot abnormal activity as it emerges and even take precise, measured actions, to automatically curb the threat.
Participants in this session will learn:
- How new machine learning and mathematics are automating advanced cyber defense;
- Why full network visibility allows you to detect threats as or before they emerge;
- How smart prioritization and visualization of threats allows for better resource allocation and lower risk; and
- Real world examples of unknown threats detected by “immune system” technology.
Molly currently works on new client implementations for Darktrace and has worked with over 60 organizations across North America. Prior to Darktrace, Molly worked in the vulnerability management space as well as in product and application development. Molly holds degrees from The University of Texas at Austin and a CSP Certificate from Stanford University.
11:45 AM Arrival, Check-in & Networking & Lunch
12:00 PM Chapter President Message, Sponsor message, upcoming meetings
12:10 PM Speaker presentation
1:10 PM Prize Drawing & Wrap up
Parking: Free on-site