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Leveraging behavior analytics and machine learning algorithms in your PAM strategy

The growing number of security breaches and insider attacks involving privilege misuse calls for a more dynamic approach to how privileged access is managed and monitored in an enterprise. An effective privileged access management (PAM) program should deliver strict governance of administrative access to critical IT systems as well as help build a proactive stance, enabling SOC teams to spot hidden privilege-based threats even before they take shape. While capabilities like credential vaulting, policy enforcement, user access policies, and privilege elevation controls allow for robust protection of privileged access entitlements, it is equally crucial to implement detection mechanisms that can continuously monitor privileged user activity, analyze audit logs, and single out abnormal behavior.
Modern technologies like machine learning (ML) algorithms can introduce a forward-thinking outlook to PAM and enable enterprises to predict emerging access risks in real time. ML-based anomaly detection systems can deeply analyze raw data collected around privileged activity, profile standard user behavior patterns, and then surveil future operations to detect any deviations from the norm, such as server logins after office hours. Moreover, such anomalies can be instantly flagged with alert systems and escalated to accelerate incident response times.
On that note, this webinar will educate you on the benefits of including threat analytics in your PAM program and how it can improve your risk mitigation strategy.

Key takeaways

  • How to leverage ML to build context-aware intelligence around privileged access data
  • A brief introduction to how ManageEngine's IT analytics platform, Analytics Plus, works with our PAM solutions to provide actionable insight into unusual privileged user behavior
  • Practical use cases showcasing real-time application of threat analytics in a PAM program