The treasure of data that security departments gather through their activities or systems is being utilized in new ways to identify and resolve security risks.
FREMONT, CA: Since the concept of a corporate security perimeter has all but disappeared in recent years thanks to the increasing adoption of cloud and mobile services, information security has experienced a profound paradigm shift from conventional perimeter protection tools towards monitoring and identifying malicious activities within corporate networks. Increasingly sophisticated attack means used by cybercriminals, and the increasing role of malicious insiders in several large scale security breaches indicate that conventional approaches to information security can no longer keep up.
At the core of the security, approach stands enhanced detection – and that is where big data analytics comes into play. Identification must be able to find changing use patterns, execute complex analysis quickly, close to real-time, and perform complex correlations across data sources ranging from server and application logs to network events and user activities. This needs advanced analytics beyond simple rule-based approaches and the potential to run analysis on large amounts of current and historical data – big data security analytics. Coupling the current state of analytics with security assists organizations in enhancing their cyber resilience.
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Improved by additional context data and external threat intelligence, this data is then analyzed using correlation algorithms to detect anomalies and thus find possible malicious activities. Unlike legacy SIEM solutions, such tools operate near real-time and generate a small number of security alerts ranked by severity according to a risk model. These alerts are empowered with additional forensic details and can greatly simplify a security analyst’s job and allow quick detection and cyberattacks mitigation.
The biggest technological breakthrough that made these solutions possible is big data analytics. The security industry has finally reached the point where business intelligence algorithms for data processing, previously affordable only to big corporations, have become commoditized. Using readily available frameworks and inexpensive hardware, vendors can build big data solutions to gather, store, and analyze vast amounts of unstructured data in real-time.