The evolution of network security has led to the development of more advanced technologies that can effectively monitor and counter network attacks. In the past, firewalls were considered the best method for keeping networked devices secure. However, as network traffic grew and became more complex, firewalls became less effective. They lacked the ability to classify traffic based on content and context, leading to a demand for better technologies.
In the early 1990s, commercial intrusion detection systems (IDSes) were introduced. These systems evaluated network traffic against a set of rules and known attacks, generating an alert if an attack was identified. This was a significant improvement from manual log and system message analysis, which had limited success in detecting active intrusions. However, IDSes often produced a large number of false positives and lacked the ability to centralize and correlate event data from multiple systems.
To address these issues, security vendors combined the concepts of security information management (SIM) and security event management (SEM) to create security information and event management (SIEM) systems. SIEM systems provided greater visibility into the overall operating environment by centralizing, normalizing, and analyzing event data across an IT environment. This allowed security teams to efficiently and effectively tackle the increasing volumes of traffic in complex IT infrastructures.
Despite their benefits, first-generation SIEM systems had their own shortcomings. Their dashboards and reports were basic, and their alerts lacked sophistication. They also required manual intervention at each stage of the process, making them less scalable. Additionally, attackers were finding ways to work around the rule-based triggers, operating undetected.
The next stage in the evolution of SIEM came with the availability of low-cost, scalable storage solutions such as Apache Hadoop and Amazon S3. These technologies enabled SIEM systems to use big data analytics to improve the correlation and interpretation of live and historical data. Machine learning and AI integration further enhanced SIEM tools, allowing them to detect zero-day threats, attack patterns, and known threats. SIEM systems also began ingesting log data from cloud deployments, SaaS applications, and other nonstandard data sources, improving their accuracy and usefulness.
Anomaly detection, powered by AI, became a cornerstone in SIEM’s evolution. User and entity behavior analytics (UEBA) added a dynamic layer of detection capabilities by generating a baseline of normal behavior and detecting activities outside of accepted ranges. For example, if a malicious hacker gained access to sensitive systems using stolen credentials, a SIEM system with UEBA could detect and stop the access.
As the number and complexity of cyber attacks have increased, SIEM tools have continued to evolve. Vendors have introduced new concepts, such as security orchestration automation and response (SOAR), to improve the detection of complex threats and lateral movements. SIEM systems are now an established part of most security operation centers, deployed in various ways, including appliances, software, and managed security services. They provide real-time telemetry for operations teams to analyze and resolve network issues, and logs for incident response and compliance purposes.
While SIEM systems have come a long way, there is still room for improvement. Advances in machine learning and AI will further enhance the understanding of network activity. The goal is to develop predictive alerts that can stop cyber attacks before they occur, without disrupting everyday operations.
In conclusion, SIEM has played a key role in the evolution of network security. It has provided the necessary visibility into complex infrastructures and enabled efficient monitoring and detection of threats. As cyber attacks continue to evolve, SIEM systems will continue to improve, turning data into actionable information and automating security incident investigation and response processes.