Cybersecurity threats are constantly evolving and becoming more complex than ever. Cybersecurity professionals are always looking for new tools and methods to add to their cyber arsenal. Cisco predicts that there will be 27.1 billion networked devices in 2021, up from 17.1 billion in 2016 (Cisco Global Insights 2021). The Internet of Things (IOT) continues to create massive needs and problems, as even our toasters are coming online. Data breaches and ransomware attacks are on the rise and are becoming more aggressive in their approach.
In industries outside of cybersecurity, where there is an enormous amount of data or entities, one approach to combat this nefarious tsunami is to apply analytics with a touch of automation. For example, sophisticated customer analytics are being utilized by many retail companies which allows them to understand the individual characteristics of their customers. Companies can segment users by demographics, interests, and behaviors, among many other categories. Companies can also view customer journeys, which allows them to make customer purchase predictions with a high degree of accuracy. The same type of analytics and automation can be used in the cybersecurity realm to predict, assess, and triage cyber threats followed by automated correction and/or mitigation actions. This can make the life of a cyber professional more productive when confirming and investigating threats.
Advanced analytics algorithms for large-scale data processing have become commoditized and therefore affordable to most organizations. Frameworks like Apache Hadoop, combined with reasonably priced hardware, would allow for a data solution that collects, stores and analyzes massive amounts of data. Security professionals can combine real-time and historical data to analyze and surface new security incidents that might be tied to historical ones. Analytics and automation solutions can aggregate data from abundant sources that include endpoint data, operating system logs, firewalls, routers, business applications, virus scanners, and external threat intelligence. The key to these products is to implement pattern identification and automated workflows to optimize cybersecurity solutions.
Cyber professionals that utilize data collection, aggregation and analysis capabilities to perform cybersecurity detection activities can create an environment whereby early attack identification is not just possible but often successful. These automated advanced analytics can identify those signals that truly require the attention of a cyber professional versus having analysts chase ghosts in the machine.
Speaking of Arsenals and Data Analytics – Please visit Charles Givre at Black Hat USA for his Arsenal demo on how to uncover insights in raw network data as well as how to rapidly join multiple security data sets without ETL or coding. One of the major challenges any security analyst faces is working with data. Apache Drill is an extremely powerful, but not well-known tool which enables an analyst to rapidly query and join data sets using standard SQL and without moving the data.
Contact us today to learn more about automating advanced analytics in cybersecurity.
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