Imagine using a risk score to determine whether to grant a user access to an application, a system, a device. Wouldn’t it be a huge time-saver if you could auto-approve low risk access requests instead of manually granting such requests? On the flip side, wouldn’t it be great to automatically ensure that privileged access requests require multiple approvals?
With Behavior Analytics and Model Driven Security, our customers are changing their provisioning workflows to use automation instead of manually provisioning access. They are eliminating the typical approval process that most other organizations need to go through.
Model driven security relies on math and analytics to change controls on demand, as opposed to having people that are hands on a keyboard – changing something after the fact. It doesn’t give you the ability to really keep up with an attack.
Machine learning-based behavior analytics extracts context from big data, rather than relying on simple rule and policy-based security controls. This allows for continuous monitoring of user behavior during a session to dynamically assess and adapt risk scores to enable real-time responses to anomalies.
Watch this recorded session to learn more about how model driven security can help your environment and its application in real-world scenarios.