In the 16 years since Security Information and Event Management (SIEM) was coined by Mark Nicolett and Amrit Williams of Gartner, the idea and the different vendor offerings available continue to mature and expand its capabilities. Despite this growth, many enterprises and organizations have failed to understand which security needs can be solved with a SIEM solution and how crucial they truly are.
In today’s fast-paced world, true security can be fleeting. ackers won’t wait for victims to understand what has happened or why. The attack vectors targeting organizations are only getting wider both in terms of sophistication and volume, so organizations must quickly come to terms with what they need and implement quickly.
It’s easy to point to any SIEM solution, but to easily and most reliably detect threats of any uncommon security events, I recommend that organizations implement one that integrates with artificial intelligence (AI) to identify the most mysterious and suspicious activity.
SIEM is based on a combination of security information management (SIM) and security event management (SEM) to provide organizations with next-generation detection, real-time visibility analytics, and response within their on-premises and cloud-based networks.
SIEM software uses analytics engines to match events against organization policies. Then it indexes the data and events for a sub-second search to detect and analyze advanced threats using globally gathered intelligence. This gives security teams both insights and track records of the activities within their IT environment, and provides data analysis, event correlation, aggregation, reporting, and log management.
When SIEM identifies a threat through network security monitoring, it generates an alert and defines a threat level based on predetermined rules. For example, if someone is trying to log into an account 10 times in 10 minutes that is normal but trying to login 100 times within 10 minutes would be flagged as an attempted attack. SIEM’s custom dashboards and event management system improves investigative efficiency and reduces time wasted on false-positives.
SIEM software empowers IT and security teams to detect known and unknown threats and mitigate security incidents faster. However, as organizations implement new types of technologies and more endpoints, attack surfaces are expanding and have already created more undetected blind spots to monitor.
To be on top of all the many different attacks and threats, organizations need a better understanding of what’s happening inside and outside of their organization from their on-premises and cloud environments. SIEM solutions handle this well, but they must be user-friendly; the more complex it is the less productive it will be.
Organizations need to adopt SIEM solutions that are easier to manage, maintain, and deploy. Log fatigue or shallow data features are a caveat of choosing unfitting solutions. Within a good SIEM tool, there will always be an increasing amount of data sources that will need to be managed. Due to the ongoing shortage of cybersecurity skills, organizations should adopt a solution that comes with vendor support that will provide ongoing updates and best practices so your IT team won’t be forced to be SIEM experts.
Today, the typical SIEM solution will help with many security use cases from insider threats to detecting endpoint attacks and more. AI, machine learning, and advanced analysis help automate the detection of anomalous behaviors and response time even more, stopping any potential attacks on the organization in real-time, proactively and reactively.
SIEM systems are usually data-driven platforms and some use AI and machine learning capabilities for better correlations and alerts. There is also the threat detection element that helps to detect threats in emails, cloud resources, applications, external threat intelligence sources, and endpoints. This can include user and entity behavior analytics (UEBA) which monitors for abnormal behaviors which could indicate a threat. It can also detect behavior anomalies, lateral movement, and compromised accounts.
AI can help organizations adopt and implement more specific use cases within their SIEM system. Organizations can close the gap when it comes to security while automating their IT teams’ day-to-day work. By staying ahead of their attackers, by being updated with expanding attack vectors, and by using AI and automation
An example of an attack SIEM software could mitigate might involve. an organization that knows they have unauthorized access from an external user. Their internal network activity system recognizes a user who obtained access from a different country and a different time zone or IP address and alerts the IT team of the network activity. If the alert was delayed or shows as a dated activity, it will be a challenge to mitigate the risks and respond.
With a SIEM solution in place, the security alerts would have occurred in real-time and would show more accuracy which would have made it easier for IT managers to decide on how to react and mitigate. This strengthens the idea of investing in technology and SIEM systems, which is great, but if the security teams behind the SIEM dashboard are not trained and qualified they won’t be able to prevent attacks or they might not recognize important alerts.
Organizations need to dictate policies within the SIEM system and make sure to update them all the time. Additionally, it is vital to educate employees and train them so there will be fewer false positive alerts in the system. When looking for the right SIEM solution it’s crucial to ensure that it can integrate with the current SIEM System.
Implementing the ideal SIEM software can help your organization increase its network visibility into the numerous amounts of data and attacks happening all around. Moving on from manual risk identification, and adopting automation with AI allows firms to shift security efforts and strategies towards the proactive, without intense effort.