Analyst Report Cites Benefits of Identity-based Security for Fighting Insider Abuse and Data Theft
LOS ANGELES – Gurucul, the user behavior analytics and identity access intelligence company, today announced that information security analyst firm KuppingerCole has published a report on the benefits of the Gurucul Risk Analytics platform. The report cites the advantages of user behavior analytics (UBA) for detecting a range of threats including the use of compromised credentials, insider abuse, data exfiltration, access misuse and zero day attacks — which evade traditional perimeter security tools. The full report is available here: KuppingerCole Report: Gurucul Risk Analytics.
Gurucul will demonstrate the Gurucul Risk Analytics platform at RSA Conference 2016 Booth #S2415.
“Gurucul Predictive Risk Analytics provides an interesting approach that combines access governance, risk management and the detection of cyber threats,” said Mike Small, Fellow Analyst at KuppingerCole. “Unlike other solutions that focus on network traffic or technical vulnerabilities this solution focuses on identity, access and user activity to detect and prioritize risk.”
According to the report, “External attacks now involve a complex process, often including an element of social engineering, which exploits compromised or illicit user credentials to gain access to data. This is partly because of the strength of conventional network defences against direct frontal attack, and also because the use of apparently legitimate credentials bypasses other security controls like encryption. Furthermore, insider threats continue to be a real problem and these invariably involve the misuse of access rights. For these reasons identity and access controls have become the new perimeter.”
Gurucul is changing the way enterprises protect themselves against fraud, insider threats and external intruders, both on premise and in the cloud by analyzing identity as a threat plane and perimeter. First, the company’s identity access intelligence (IAI) technology uses machine learning algorithms and big data infrastructure to reduce the attack surface for accounts, unnecessary access rights and privileges. Second, UBA machine learning models and predictive anomaly detection are used to identify, predict and prevent breaches, insider threats, data exfiltration and access abuse. Gurucul’s identity-based hybrid user behavior analytics technology is being used globally by organizations to detect insider fraud, IP theft, external attacks and more.
“This report by KuppingerCole clearly spells out the importance of monitoring identities, access rights and user activity to detect insider threats, user account hijacking and advanced external attacks,” said Saryu Nayyar, CEO of Gurucul. “The Gurucul platform accomplishes this by analyzing massive amounts of data from a variety of sources using machine learning to expose risks that appear ‘normal’ to traditional security products.”
Gurucul is the only vendor to meet all five use cases and the compliance and fraud qualifications in the Market Guide for User and Entity Behavior Analytics (UEBA) recently published by Gartner, Inc.
Gurucul is changing the way enterprises protect themselves against cyber fraud, insider threats and external intruders on-premises and in the cloud. The company’s user behavior analytics and identity access intelligence technology uses machine learning and predictive anomaly detection algorithms to reduce the attack surface for accounts, unnecessary access rights and privileges, and to identify, predict and prevent breaches. Gurucul technology is used globally by organizations to detect insider threats, cyber fraud, IP theft, external attacks and more. The company is based in Los Angeles. To learn more, visit www.gurucul.com and follow us on LinkedIn and Twitter.