Businesses today are constantly under threat of hacks. Every year, the frequency and sophistication of these attacks increase, putting pressure on organizations to defend their sensitive data and maintain operational integrity. 

Security Information and Event Management (SIEM) systems are one of the key tools for organizations to combat this onslaught. SIEMs provide a centralized platform for collecting, normalizing, and analyzing data, enabling early threat detection and effective security policy management.

This guide is designed to offer an overview of SIEM systems in 2024. It covers how SIEMs work, how AI impacts SIEMs, and why they are indispensable for modern businesses. Additionally, it provides practical advice on choosing and configuring an SIEM solution to maximize efficiency and minimize risk.

What is SIEM?

Security Information and Event Management (SIEM) refers to a class of cybersecurity platforms that allow organizations to collect, normalize, and analyze security event data from various sources. SIEM system comes with features such as:

• Centralized Data Collection and Analysis: SIEMs aggregate security-related data from across an organization’s IT environment, making it easier to detect and respond to potential threats.

• Log Management and Review: By managing and auditing logs, SIEMs help ensure that all security-related events are documented and can be reviewed as needed.

• Real-time Security Alerts: SIEMs can create and manage security alerts in real-time, ensuring that potential threats are addressed promptly.

• Threat and Anomaly Detection: SIEMs can detect insider threats and anomalies, providing organizations with needed information about suspicious activities within their networks.

• Compliance Reporting: SIEMs generate reports that help organizations comply with various regulations, such as PCI DSS and HIPAA.

Modern SIEM systems include advanced features such as User and Entity Behavior Analytics (UEBA) and integration with third-party threat intelligence databases. These innovations allow teams to not only detect threats but also to contextualize them, determining the level of risk posed by each identified threat.

How AI Impacts SIEM

Artificial Intelligence (AI) is changing and innovating many aspects of SIEM systems,  improving their ability to detect and respond to threats more effectively. AI impacts SIEM with AI-driven technologies like Deep Learning, Natural Language Processing (NLP), and User and Entity Behavior Analytics (UEBA) each unique role.

Deep Learning Algorithms

Deep Learning is a subset of machine learning that mimics the human brain’s decision-making process through artificial neural networks. In the context of SIEM, deep learning algorithms can access large amounts of data to identify complex patterns that might look like a security threat. 

These algorithms excel at processing unstructured data—like documents, binary files, and images—expanding the range of data sources that can be analyzed for potential threats. They also uncover subtle correlations that traditional rule-based systems might overlook.

Natural Language Processing (NLP)

NLP involves the use of computational techniques to understand and interpret human language. Within SIEM systems, NLP can analyze text-based data, such as system logs, network traffic, and user communications, to detect potential threats. For instance, NLP can parse system logs to determine normal operational patterns and flag deviations that might look like a security issue. 

Similarly, it can scrutinize network traffic for signs of data exfiltration or unauthorized access, and even analyze user communications to detect insider threats or social engineering attacks.

User and Entity Behavior Analytics (UEBA)

UEBA uses machine learning algorithms to establish what constitutes normal behavior for users and entities within an organization. UEBA can detect deviations that might look like a security threat by understanding these baselines. For example, if a user accesses sensitive data at odd hours or from an unusual location, UEBA can flag this behavior as suspicious. 

Similarly, if a device starts communicating with a suspicious IP address, it may flag a malware infection.

Why Do Enterprises Need a SIEM?

The term “SIEM” was first introduced by Gartner analysts in 2005, and it remains as relevant today as it was nearly two decades ago. The challenges SIEM was designed to address have only become more pressing as cyber risks have grown in both scale and complexity.

The primary reason organizations need a SIEM is to efficiently analyze large volumes of data from multiple sources. A SIEM consolidates this data, allowing cybersecurity and IT teams to detect and react to threats more quickly and accurately. Without a SIEM, teams would have to manually sift through log files and event streams, a time-consuming and error-prone process that makes it difficult to correlate data sources and disclose potential risks.

For example, an application event log might show a sudden spike in requests, which could either be a sign of a DDoS attack or simply the result of increased traffic. A SIEM can help determine whether the requests are legitimate or part of an attack by correlating this log with network data that shows where the traffic is coming from.

Best Practices for Maximizing SIEM Effectiveness

• Maximize Data Ingestion

The more data your SIEM ingests, the better it can detect and analyze threats. However, many organizations limit their data ingestion, either due to cost concerns or a lack of understanding of the importance of comprehensive data collection. 

To avoid this pitfall, choose a SIEM like Stellar Cyber that can support different ranges of data sources and formats, and configure it to collect as much data as possible. Often, a single log file could be the key to detecting a threat or gaining the context needed to accurately assess a risk.

• Smart Alert Management

Ingesting more data means your SIEM will generate more alerts. Although this is beneficial for threat detection, it can also lead to alert fatigue if not managed properly. It’s vital to choose an SIEM that can automatically triage alerts, helping your analysts prioritize their responses. 

Seek for features that reduce false positives and streamline alert management, allowing your team to focus on the most critical threats.

• Gain Threat Context

The more context you have about a potential threat, the better equipped you are to respond effectively. To enhance threat context, use SIEM features like pattern matching and threat intelligence integration. 

These tools help security analysts determine the likelihood that a threat will be exploited and guide them in prioritizing their response. SIEMs do more than just detect anomalies by transforming raw data into actionable intelligence.

Conclusion

SIEM systems are a primary component of any organization’s cybersecurity strategy. Over the years the role of SIEMs in detecting and responding to threats has only become more intense. Organizations can make informed decisions about how to implement and optimize their SIEM solutions by understanding how SIEMs work, and how AI enhances their capabilities. Also going for the service of a trusted SIEM service provider, especially Stellar Cyber can provide not just security, but peace of mind for the organization and its data.