SUNNYVALE, CA--(Marketwired - Feb 25, 2016) - Niara, a provider of security analytics for attack detection and incident response, announced today that the latest release of its security analytics platform analyzes any data source and supports the customization of its behavioral analytics modules, providing organizations with complete visibility into attacks and risky behaviors inside their environments.

Organizations can now derive security insights from data sources by combining Niara's built-in behavioral analytics with custom modules unique to their particular environment. As a result, organizations benefit from more comprehensive entity risk profiles and extensive information about attacks due to the breadth of Niara's multi-dimensional analytics. This includes privilege escalation, password sharing, credential violations, lateral movement, internal reconnaissance, abnormal access to high-value resources, command and control and exfiltration activities.

"By making behavioral analytics modular and data-agnostic, Niara enables data sources, machine learning models and features to be easily combined to better isolate attacks and identify risky behaviors," said Sriram Ramachandran, CEO and co-founder of Niara. "That flexibility results in much richer Entity360™ risk profiles, allowing attacks to be detected with greater precision and dramatically reducing the time and skill needed to investigate and respond to security events."

Niara's behavioral analytics modules are already delivering results at a number of Fortune 500 companies, using a full spectrum of machine learning techniques such as supervised, semi-supervised and unsupervised to detect and more reliably link anomalies to malicious intent. With Niara's latest release, security analysts continue to have full confidence that they are focusing on the threats that matter.

Additional Resources

  • Visit Niara at booth #3135 at RSA and schedule a 1:1 meeting
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About Niara
Niara's security analytics platform automates the detection of attacks that have bypassed an organization's perimeter defenses and dramatically reduces the time and skill needed to investigate and respond to security events. The solution applies machine learning algorithms and forensics to data from the network and security infrastructure to detect compromised users, entities, and malicious insiders, speed threat hunting efforts, and reduce the time for incident investigation and response by focusing security teams on the threats that matter. Headquartered in Sunnyvale, Calif., the company is backed by NEA, Index Ventures and Venrock. For more information, visit

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Media Contact:
Ashley Womack
LEWIS for Niara