A significant number of modern incidents begin with account compromise. Since initial access brokers have become a full-fledged criminal industry, it’s become much easier for attackers to organize attacks on companies’ infrastructure by simply purchasing sets of employee passwords and logins. The widespread practice of using various remote access methods has made their task even easier. At the same time, the initial stages of such attacks often look like completely legitimate employee actions, and remain undetected by traditional security mechanisms for a long time.

Relying solely on account protection measures and password policies isn’t an option. There’s always a chance that attackers will get hold of employees’ credentials using various phishing attacks, infostealer malware, or simply through the carelessness of employees who reuse the same password for work and personal accounts and don’t pay much attention to leaks on third-party services.

As a result, to detect attacks on a company’s infrastructure, you need tools that can detect not only individual threat signatures, but also behavioral analysis systems that can detect deviations from normal user and system processes.

Using AI in SIEM to detect account compromise

As we mentioned in our previous post, to detect attacks involving account compromise, we equipped our Kaspersky Unified Monitoring and Analysis Platform SIEM system with a set of UEBA rules designed to detect anomalies in authentication processes, network activity, and the execution of processes on Windows-based workstations and servers. In the latest update, we continued to develop the system in the same direction, adding the use of AI approaches.

The system creates a model of normal user behavior during authentication, and tracks deviations from usual scenarios: atypical login times, unusual event chains, and anomalous access attempts. This approach allows SIEM to detect both authentication attempts with stolen credentials, and the use of already compromised accounts, including complex scenarios that may have gone unnoticed in the past.

Instead of searching for individual indicators, the system analyzes deviations from normal patterns. This allows for earlier detection of complex attacks while reducing the number of false positives, and significantly reduces the operational load on SOC teams.

Previously, when using UEBA rules to detect anomalies, it was necessary to create several rules that performed preliminary work and generated additional lists in which intermediate data was stored. Now, in the new version of SIEM with a new correlator, it’s possible to detect account hijacking using a single specialized rule.

Other updates in the Kaspersky Unified Monitoring and Analysis Platform

The more complex the infrastructure and the greater the volume of events, the more critical the requirements for platform performance, access management flexibility, and ease of daily operation become. A modern SIEM system must not only accurately detect threats, but also remain “resilient” without the need to constantly upgrade equipment and rebuild processes. Therefore, in version 4.2, we’ve taken another step toward making the platform more practical and adaptable. The updates affect the architecture, detection mechanisms, and user experience.

Addition of flexible roles and granular access control

One of the key innovations in the new version of SIEM is a flexible role model. Now customers can create their own roles for different system users, duplicate existing ones, and customize a set of access rights for the tasks of specific specialists. This allows for a more precise differentiation of responsibilities among SOC analysts, administrators, and managers, reduces the risk of excessive privileges, and better reflects the company’s internal processes in the SIEM settings.

New correlator and, as a result, increased platform stability

In release 4.2, we introduced a beta version of a new correlation engine (2.0). It processes events faster, and requires fewer hardware resources. For customers, this means:

• stable operation under high loads;
• the ability to process large amounts of data without the need for urgent infrastructure expansion;
• more predictable performance.

TTP coverage according to the MITRE ATT&CK matrix

We’re also systematically continuing to expand our coverage of the MITRE ATT&CK matrix of techniques, tactics, and procedures: today, Kaspersky SIEM covers more than 60% of the entire matrix. Detection rules are regularly updated and accompanied by response recommendations. This helps customers understand which attack scenarios are already under control, and plan their defense development based on a generally accepted industry model.

Other improvements

Version 4.2 also introduces the ability to back up and restore events, as well as export data to secure archives with integrity control, which is especially important for investigations, audits, and regulatory compliance. Background search queries have been implemented for the convenience of analysts. Now, complex and resource-intensive searches can be run in the background without affecting priority tasks. This speeds up the analysis of large data sets.

 

We continue to regularly update Kaspersky SIEM, expanding detection capabilities, improving architecture, and adding AI functionality so that the platform best meets the real-world conditions of information security teams, and helps not only to respond to incidents, but also to build a sustainable protection model for the future. Follow the updates to our SIEM system, the Kaspersky Unified Monitoring and Analysis Platform, on the official product page.

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