Most organizations focus their threat detection and prevention strategies on external actors, but internal threats can cause just as much harm. These threats are not always launched by malicious employees with the intent to inflict harm, but they can be caused unintentionally by accidentally compromising data to threat actors through lack of security awareness.
Access control systems play a vital role in protecting an organization against insider threats. Being able to identify whether users attempt to log in or successfully log in outside of business hours will help organizations detect threats and effectively identify security risks.
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Since the pandemic, remote working has become much more common around the world. While working from home brings new flexibility and adaptability to work processes, it also exposes individuals and businesses to a host of cybersecurity risks.
Remote work has opened up access possibilities for users, with employees now able to connect to devices anytime, anywhere. With a large number of individual users to manage, it can be difficult to monitor dispersed employees and identify unusual behavior.
To create a complete picture of user activity, organizations should define custom rules to gain visibility into when remote users are accessing the network. Organizations need tailored security policies to detect and respond to login activity that took place outside of normal working hours.
Detecting a possible incident or abnormal internal activity should be a simplified and streamlined process.
Many security teams run their security operations center (SOC) with limited resources and need support to optimize their threat hunting activities. They need the support of knowledgeable security professionals to tailor their deployed solutions to their individual security needs and maximize the rules they define within their security platforms.
LogRhythm’s analytical co-piloting services allow organizations to establish a behavioral and statistical baseline, as well as continuous alarm tuning to better understand potential risks. By implementing a specific rules-based approach, our co-pilots enable organizations to take control of their IT environments and meet their security needs.
By tracking authentication success and setting day and time criteria, organizations can monitor any access deemed suspicious by users who are not expected to log in outside of normal business hours. company.
Organizations can also fine-tune their rules to check login types for users who log in after hours. Connection types describe the ways users can connect to a system.
A user who left their machine connected would continue to generate a type 3 (network) connection, where a user accesses a computer from the network, or type 4 (batch), when a scheduled task is about to start. be started. However, you should not expect to see login type 2 (interactive), when a user logs on to a computer locally, 7 (unlock) when a user unlocks a previously locked workstation, or 10 (interactive remote), when a user connects to a remote computer using Terminal Services or Remote Desktop. These types of logins would trigger alarms allowing security teams to determine if that user was accessing outside of business hours and why.
Additionally, organizations can further strengthen their security posture against insider threats by leveraging machine learning (ML) techniques. Security teams can embed ML into technologies to identify abnormal connection patterns in their data, allowing them to make faster and more accurate decisions.
With ML, security teams can gain additional insights through an approach that doesn’t require prior knowledge of known patterns. Security technologies using ML can learn typical activity patterns in a network environment to recognize anonymous behavior. This can indicate potential threats and identify risks earlier in the cyberattack lifecycle to prevent future incidents and reduce the impact of a breach.
Organizations can stop potential insider threats in their tracks by deploying deterministic and ML rules to identify abnormal login behaviors. With this holistic approach, they can effectively capture valuable insights into user behavior and quickly combat malicious and negligent insider activity.
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The post Detecting and Monitoring Abnormal Login Activities with a Deterministic, Rules-Based Approach appeared first on LogRhythm.
*** This is a syndicated blog from LogRhythm’s Security Bloggers Network written by Natalie Pinner. Read the original post at: https://logrhythm.com/use-cases/use-case-co-pilot-uncovering-insider-threats/