Once people are trusted with access, an entirely new risk matrix is added to the equation. The freedom of users grows with their access level. The more rights and privileges they have in IT systems, the more risk they carry for the company. Regulatory compliance, IT outsourcing, and advanced cyber attacks are forcing companies to increase supervision, especially of their privileged users. Attackers have shifted from brute force to deception based attacks targeted at users. These attack methodologies make the attacks harder to detect and allow attackers to remain undiscovered for extended periods of time. Every day, the security operations team is challenged to identify an attacker’s entry and lateral movement within the network.
Implementing a solution to mitigate the risks of privileged activities is one of the most challenging jobs for IT leaders today. Rule-driven correlation is still very effective at detecting the more significant portion of threats, but our opponents have found ways to outsmart this detection type. Manually detecting anomalies is humanly impossible, nor do we have the manpower to track this endless stream of data.
Machines are now on the front line to assist: with User Behavior Analytics, we provide you the insight to understand whether users are adhering to granted privileges and boundaries. It is based not only on acceptable use policies but also by inspecting behavior over time and comparing it to peer groups in your organization. Aside from predefined rules, machine learning assists by detecting unknown threats and anomalous behavior. This will empower security operations to investigate and manage these types of incidents quickly and efficiently.
Detect insider threats, targeted attacks, and financial fraud by looking at patterns of user and entity behavior.
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"Machine intelligence is the last invention that humanity will ever need to make."