How SIEM Automation Can Improve Threat Management
Let’s see why it is important to know about SIEM Automation. An ocean of data and security alerts are dispatched to organizations on a regular basis. According to the Achieving High-Fidelity Security research by EMA, 92% of organizations were receiving up to 500 events per day, and 88% percent of respondents said they were receiving up to 500 severe/critical alerts per day. To make the matters worse, 88% of the respondents had just one to three people investigating and triaging security events per day, and 67% of organizations were only able to investigate 10 or fewer of their severe/critical events per day.
The struggle in managing alerts and potential threats follows alert fatigue; alert fatigue is when an overwhelming number of alerts desensitizes security analysts, leading to missed or ignored alerts or delayed responses.
Neglecting the ever-evolving technology and tools is another key issue in alert management that could lead to a lack of visibility in cybersecurity. To protect their data, organizations need to have visibility into who is accessing data, what data they are accessing, when, and how they are accessing it.
Lack of proper time management regarding alert response is another factor contributing to deficiencies in alert management, consequently, threat detection & response. Triaging and responding to alerts using manual processes often takes too much time leading to a longer dwell time.
One solution to have meaningful alerts and accelerated threat detection is integrating automation into SIEM. Automation in SIEM can increase efficiency and aims to achieve the maximum level of it.
Katell Thielemann—Research Vice President Gartner—noted the importance of automation:
“We are no longer asking the singular question of how we’re managing risk and providing security to our organization. We’re now being asked how we’re helping the enterprise realize more value while assessing and managing risk, security and even safety. The best way to bring value to your organizations today is to leverage automation.”
Threat Detection: What problems does SIEM automation solve?
An automated SIEM applies automation to each step of the threat detection process; the process of detecting a threat begins by receiving a trigger action. The next step is to create context and enrich data by automatically extracting the indicator that is associated with the alert. After the trigger and the contextualization/enrichment, there has to be a decision for how to respond, which can be manual or automated. In a conventional SIEM, remediation actions will not go further without a human response.
However, an automated SIEM allows you to respond and remediate without human intervention; in this case, SIEM has to be taught to follow a corresponding remediation path once a condition is met. This remediation action varies based on the level of maturity an organization has; for example, the response could be issuing a ticket, or automatically disabling a suspicious user. During these steps, certain measures must be taken which can be done automatically and without any human involvement in the process.
Mitigating Threat Detection Challenges
AI and ML can help security analysts implement use cases within a SIEM system; thus, assess the alerts, prioritize them, and automatically manage a large chunk of the workload and as a result, eliminate alert fatigue from the landscape of threat detection challenges. SIEM use cases make sense of all the large volumes of data by examining log data for patterns and identify the ones that could indicate a cyberattack, then correlate event information between devices to detect potentially anomalous activity, and finally, issue alerts accordingly.
It is noteworthy to say that alongside SIEM, Security Orchestration, Automation and Response (SOAR) technologies can also help analysts with the automation of threat detection and remediation. SOAR platforms automate and accelerate time-intensive, manual processes by conducting comprehensive data collecting and aggregating vast amounts of security data and alerts from a wide range of sources.
Using real-time machine learning, SIEM can meet the visibility and time management challenges when detecting threats; SIEM can provide continuous data analytics and ongoing assessments of the security posture and compliance. Additionally, it can surface relevant threats in minutes, compared to the hours or days that took to detect when previously analyzed by human security analysts. SIEM automation is able to reduce Mean-Time-To-Identify (MTTI) — the amount of time it takes to discover a potential security incident – by implementing proactive threat detection — rather than reactive.
Machine Learning in Automation
SIEM powered by machine learning can combine various data sets across complex environments; then, take it a step further by applying correlation, enrichment, and attribution to the data to turn it into actionable information. Furthermore, it is able to generate early and reliable detections with attack and behavioral analytics. Automated SIEM allows contextualized data for in-depth investigations that help security teams respond faster and better.
Machine learning in SIEM can also provide threat analytics and create alerts of risk in real-time. Another highlight of machine learning is its ability to use algorithms to predict future data from previous patterns. Furthermore, machine learning is capable of classifying data that has not been recognized before. Clustering capabilities — especially valuable in forensic analysis — can identify unknown values and group them together based on detected similarities.
In incident response, machine learning can provide suggestions based on previous incident response measures to facilitate future actions.
More and more organizations are keeping up with the constantly changing threat landscape by applying automation to remove redundant manual processes, advance response, and accelerate operations.
SIEM Automation in Threat Hunting and Threat Response
SIEM provides the data required to remediate threats to an incident response system; however, SIEM is not a response tool. Integrating automation into SIEM allows security teams to focus more on value-added activities, such as threat hunting and threat prevention, given the fact that the less duration cyber attackers spend operating within your infrastructure, the less damage they can cause.