Centrify service assesses user behavior to prevent breaches

February 21, 2017 - 

Centrify launched its new Analytics Service, which uses machine learning to assess and respond to risk based on user behaviour.

The Centrify Analytics Service constantly analyzes the user’s continually evolving behavior patterns and assigns a risk score.

The service then enforces an appropriate decision on whether the user is granted access, requires step-up authentication, or is blocked entirely.

Now available in Australia and New Zealand, the Analytics Service can be added on to Centrify Identity Service and Centrify Privilege Service.

“By tailoring security policy to each individual’s behaviour and automatically flagging risky behaviour, we’re helping IT professionals minimise the risk of being breached — with immediate visibility into account risk, without poring over millions of log files and massive amounts of historical data,” Centrify chief product officer Bill Mann said. “Thanks to our broad set of enforcement points that include endpoints, applications and IT infrastructure, we can enforce risk-based policy in real time at the point of access. This means high-risk threats can be blocked while low-risk users get authorised access to apps, privileged credentials or privileged sessions.”

The Centrify Analytics Service equips IT departments with the ability to prevent attacks that lead to data breaches by breaking the cycle of account exploitation and impersonation.

The service stops anomalous access requests in real time while flagging and alerting potentially compromised accounts to the attention of IT departments. This speeds up the analysis process and significantly reduces the effort needed to assess risks from across the hybrid IT environment.

Equipping IT departments with machine learning technology ensures that they no longer need to manually create policy across all their endpoints, apps, sites, services and resources.

The risk-based access enabled by the Centrify Analytics Service provides IT departments with new insights through risk scoring for end users and privileged users to control policy and determine the appropriate action for a given risk level.

Examples of risk-based access include single sign-on (SSO) to applications, multi-factor authentication (MFA) for password checkout, and MFA for outsourced IT.

As part of the Centrify Identity Services Platform, Centrify Analytics Service customers can implement risk-based policy across their hybrid enterprise of endpoints, cloud applications, IaaS, and IT servers and resources, for more detailed heuristics and more effective policy.

As a natural extension of Centrify’s adaptive multi-factor authentication, the service adds machine learning capabilities that both simplifies configuration for IT departments and eliminates constant MFA challenges related to end user access.

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About Justin Lee

Justin Lee has been a contributor with Biometric Update since 2014. Previously, he was a staff writer for web hosting magazine and website, theWHIR. For more than a decade, Justin has written for various publications on issues relating to technology, arts and culture, and entertainment. Follow him on Twitter @BiometricJustin.