Network security breaches can effectuate huge regulatory fines and devastate once-revered brands. In 2011, hackers compromised personal information and credit card numbers from 77 million users of a prominent gaming network, costing the tech giant $170 million and indefinitely denting its data security credibility. Richard Power, editorial director of the Computer Security Institute, estimates that at large corporations, instances of hacking and consequent network downtime can cost firms upwards of $600,000 per hour—not including productivity losses incurred from siphoning engineering resources away from their daily routines to address the issues. In some cases, the consequences of compromised security can threaten an organization’s existence.
While legacy security solutions can intercept blatant security threats, many malicious activities go unnoticed. Multi-structured data analysis has become an increasingly popular tactic for information securitization. As an example, log data provides a treasure trove of information; companies able to efficiently store and analyze their network event logs have greater visibility into typical patterns of activity and can quickly identify anomalies. Most firms, however, are not only unprepared to capture the data due to the volume, velocity, and variety, but are also unable to analyze it in a timely fashion.
In one example, a financial services organization struggled with thousands of machines spitting out millions of structured and unstructured events daily. While the effort to collect and interpret the data was overwhelming, the concept of correlating this data with transactional and other structured data proved impossible. The technical team examined a number of market-leading analytics platforms to alleviate its pain. No relational database could fluidly manage the size or diversity of the data. Hadoop, an open-source platform with a more flexible data management structure, was well-equipped to store the data, but struggled to deliver critical interactive analysis.
The Use Case
Hadapt, the industry’s only data analytics platform natively integrating SQL with Apache Hadoop, provides unparalleled threat detection capabilities. Hadapt can continuously ingest terabytes of multi-structured data—device logs (PC, mobile, OS-level); communication logs (email, IM, phone); and browsing behavior (clickstream)—and consolidate them in one place for interactive, comprehensive analytics coupled with full text search capabilities.
Advanced persistent threats (APTs) are among the most pernicious and difficult-to-detect dangers to a company’s assets. Policing for APTs requires firms to monitor big data across many sources and extensive time frames. With Hadapt, companies can store virtually unlimited multi-structured data and, using simple SQL commands, perform complex pattern-matching analysis to pinpoint unusual network behavior.
Hadapt is a highly available system that scales linearly on commodity hardware. New data feeds can be seamlessly added to the cluster, with no downtime and automatic load balancing. Hadapt integrates natively with industry standard and in-house BI tools.
Safeguard your company against pernicious network attacks. Evaluate Hadapt.