How Access Analytics Solves Critical Security Controls
Companies are continuously being challenged with providing visibility into access risks. Access analytics uses machine learning and artificial intelligence on big data to automate the identification and clean up of access risks.
A key aspect of access analytics is Privileged Access Intelligence. With employees and contractors leveraging a lot of cloud-based SaaS platforms, as well as sanctioned and unsanctioned applications, discovering privileged access and accounts has come to the forefront. After all, privileged accounts are the keys to your cyber kingdom so securing and monitoring these accounts are paramount.
A significant concern for organizations is being able to discover any unknown privileged access on the basis of activity or events. A privileged access management (PAM) system will vault privileged accounts, but there is no privileged access discovery at the entitlement level. Every single PAM vendor operates from the function of a moat or barrier, to give users privilege for a period of time by checking out accounts from a vault. However, that’s inherently limited and flawed. The process must begin with an understanding of who has access with privileged entitlements that may have escalated after provisioning, or exist within applications and unstructured data. Gurucul uses machine learning models to detect privileged access risks at the entitlement level. Then, once identified, organizations can remediate any privileged access misuse, by vaulting some of the unmanaged privileged access, or by notifying an asset owner about the newly discovered privileged access.
One security concern is, how have organizations been handling the dynamic provisioning of temporary workers during the global COVID-19 pandemic? Now that most of the workforce is working remotely, how do you continuously ensure that employees and contractors have been provisioned with appropriate access to the right set of accounts? The struggle is to maintain a fine balance between guaranteeing users are onboarded quickly with the right access to necessary assets, and ensuring that all the security controls are maintained.
Gurucul offers an access analytics based solution for this called dynamic risk based provisioning or Dynamic Access and Role Modeling. Our solution allows you to have automated analytics-based decisions to drive some of those on-demand or policy-based provisioning workflows. The capability can determine access-control permissions and restrictions based on a user’s risk score. Risk scores take into account numerous points of context including user behavior, resource sensitivity, job role, access of the user compared to peers, and the configuration of the devices used to access resources.
Another security concern from an access standpoint is risky access discovery and clean up. At the onset of pandemic, a number of organizations had to change their operating model overnight. All of a sudden, they had to provision access to cloud applications for an entire remote workforce. This is on top of an already a broken system.
