Applies Analytics to HR, Identity, Directory and other Data Sources to Detect Latent Risks at the Entitlement Level
LAS VEGAS, Gartner Identity & Access Management Summit – Nov. 29, 2016 – Gurucul, a leader in user and entity behavior analytics (UEBA) and identity analytics (IdA) for on-premises and the cloud, today announced it has added new capabilities to its Access Analytics Platform (AAP) and Gurucul Risk Analytics (GRA) that eliminate blind spots associated with privileged access. One of the leading security threats in large organizations are undocumented privileged access permissions, especially those attached to non-privileged accounts. Using big data enabled machine learning models, Gurucul scours identity, accounts, access and activity to discover and risk score privileged access down to the entitlement level across on-premises, cloud and hybrid environments.
Gurucul will demonstrate the Access Analytics Platform (AAP) and Gurucul Risk Analytics (GRA) with privileged access discovery capabilities this week at the Gartner Identity & Access Management Summit, booth #121, in Las Vegas.
According to Gartner, Inc.: “Identifying all systems and the corresponding privileged accounts is important, because every privileged account is a potential source of risk. However, this is a major challenge, as it is easy for privileged or default system accounts to be forgotten and left out. This is exacerbated by virtualization and hybrid environments that include cloud infrastructure. In such a dynamic environment, systems and accounts can easily fall through the cracks of privileged access management.”
In a typical enterprise, the scope of privileged access discovery is manually unfeasible. For example, an organization with 10,000 identities each having 10 accounts with 10 entitlements would equal 1 million entitlements. This often results in rubber-stamping certifications and cloning user access rights. Gurucul’s machine learning models automate the analysis of millions of entitlements, which includes reverse engineering roles and risk scoring access to identify excess, outlier and privileged access risks. On average, Gurucul customers have discovered that more than 50% of privileged access risks, including application privileges, are unknown to them and exist outside privileged access lists and vaults.
“Although many organizations are deploying privileged access management products to vault accounts with high risk entitlements, these tools may only perform discovery at the account level which creates blind spots and exposes companies to unknown security risks,” said Nilesh Dherange, CTO for Gurucul. “Gurucul is applying identity analytics and machine learning to discover privileged access that poses a security risk to the organization so that undocumented and unnecessary permissions can be eliminated.”
Closed Loop IAM Integration
Gurucul is also announcing Closed Loop identity and access management (IAM) integration for its AAP, which forwards accounts and entitlements with high access risk scores to IAM solutions for owner/manager certification. When an account and/or entitlement is revoked, the IAM system sends an update to Gurucul which removes the risk and re-scores the machine-learning models. Several customers, including one of the nation’s largest brokerage firms, have implemented Closed Loop IAM integration using Gurucul with Oracle Identity Manager (OIM) to automate the detection and remediation of access outlier risks.
Enhanced Metadata and Access Outlier Workbench
To further enhance AAP and GRA, Gurucul has expanded its access attribute metadata to include sub-attributes for increased accuracy within identity analytics to detect access outliers, excess access and privileged access risks. Meanwhile, a new Access Outlier Workbench also assists customers with defining variables to determine access outliers and optimal thresholds to trigger risk-based access alerts and certifications.
Gurucul Access Analytics Platform (AAP) and Gurucul Risk Analytics (GRA) with privileged access discovery, closed-loop IAM integration, access metadata, and access outlier workbench are available immediately at no extra cost as part of Gurucul GRA release v6.0 or higher.
Gurucul is changing the way enterprises protect themselves against insider threats, account compromise and data exfiltration on-premises and in the cloud. The company’s user and entity behavior analytics (UEBA) and identity analytics (IdA) technology uses machine learning anomaly detection and predictive risk-scoring 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 http://www.gurucul.com/ and follow us on LinkedIn and Twitter.
Marc Gendron PR