Platfora, a leading native big data analytics platform for Hadoop, announced Platfora Big Data Analytics for Security, a solution that delivers petabyte-scale analysis of enterprise data for use in security investigations of targeted attacks. The solution is self-service with powerful visualization capabilities, which enables security analysts to harness the power of analytics for security data to conduct security investigations.
Traditional security solutions are typically built to detect threats in real time and can only analyze historical data sets of up to 30 days in size, often missing key irregularities that may have occurred outside a single incident. With Platfora Big Data Analytics for Security, teams can now transform and connect unlimited amounts of raw data sets in Hadoop, and then visualize and analyze them to discover key identifiers linked to past or potential breaches that were once nearly impossible to detect.
Big Data Requires a Unique Approach to Security Analytics
In order to handle the growing challenges of working with big data in security environments, analytics platforms must be purpose-built for the Big Data Era. Platfora provides business context through the analysis of multiple security data sources and delivers the following key capabilities:
- Security Event Pattern Processing. Today’s security analysts must be able to connect the dots in security events across several months of data. Platfora provides advanced techniques to identify patterns in historical data that is indicative of malicious behavior.
- Comprehensive Analysis. Customers can use Platfora to analyze petabytes of data, including network, endpoint, user-access and IT data to form a cohesive picture over time based on the type of incident under investigation. Platfora’s speed and ease-of-use delivers this analysis more effectively, increasing success of the overall investigation.
- Multi-pane View, Single-pane Platform. Platfora’s approach complements existing security tools by combining data transformation, analytics and visualization in a single platform native to Hadoop, freeing up IT dollars and time typically spent by organizations trying to piece together this type of solution themselves. Multi-structured data can now coexist in a single repository or data lake and be correlated so that the outcome of the investigation is directly linked to business impact.
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