How AI and ML are Reshaping Cybersecurity: Advancing Towards the Best SIEM Alternatives
It is given that organizations should have an effective way of managing all information about their security and be capable of addressing security events as they arise. That’s why since its introduction in 2005, security information and event management (SIEM) has been regarded as a vital component of cybersecurity. It has played an important role in systematically dealing with cyber threats.
However, times have changed, and some opine that SIEM is already dead. In the age of rapidly evolving and increasingly aggressive cyber threats, it is understandable why there are doubts regarding the effectiveness of undertaking the usual SIEM operations. Attack surfaces have significantly expanded as organizations digitalized and adopted new technologies. It has become almost impossible to effectively handle the current flow of security data and events with conventional SIEM.
The need for SIEM alternatives
Arguably, the idea of performing security information and event management as part of cybersecurity is not necessarily dead. Organizations still need a way to properly manage the collection and analysis of security data to facilitate efficient responses to security events. It may not be the traditional SIEM, though. There is a need for a SIEM alternative in response to the changing cyber threat landscape.
It is important to discuss this need for an upgrade or alternative to SIEM in view of the undeniable presence of artificial intelligence in modern IT. AI has been a double-edged sword. It has been extensively used to benefit humanity, but bad actors have also been taking advantage of it.
Weaknesses have surfaced in traditional SIEM’s arsenal, and many more continue to emerge as threats evolve and level up to unprecedented levels of belligerence and inexorability. With the widespread availability of AI, threat actors have found a new weapon that allows them to create more attack vectors and methods, discover and exploit new vulnerabilities, and rapidly come up with new attack strategies or techniques.
Traditional SIEM limitations and how threat actors exploit them
Conventional SIEM has inherent weaknesses that are too significant to ignore in the context of modern threats. Here’s a rundown of the biggest issues.
Continued reliance on a rules-based approach — Legacy SIEM has been heavily dominated by predefined threat signatures and policies on handling threats and attacks. As such, it is unable to address zero-day attacks and sophisticated attack techniques that have yet to be profiled or defined in cybersecurity frameworks. AI has been used to create sophisticated cyber attacks and attack strategies that evade security controls.
Inadequate context in evaluating security data and events — Conventional SIEM is not capable of seeing the bigger threat picture. Since it heavily relies on rules and threat profiles, it cannot accurately detect threats that do not match existing threat profiles. Also, it is not capable of taking advantage of multi-sourced data to have better scrutiny of actions and incidents. In other cases, conventional SIEM may also be too sensitive that it produces too many false positives, which may bury more urgent security alerts or incidents in the security response queue.
Limited scalability — Conventional SIEM is scalable, but this scalability may not be enough to keep up with the enormity of data and the complexity of the environment that come with the advent of AI. It is easy to be overwhelmed by the unprecedented volume of logs and event information arising from the operation of modern networks. Bad actors can take advantage of this weakness by formulating and launching distributed attacks that go beyond the capabilities of traditional SIEM.
Lengthy analysis — Upon its inception, SIEM was supposed to make the handling of security data and events more efficient, hence speedier. However, this speed has since been overtaken by the aggressiveness, sophistication, and velocity of new attacks, especially since conventional SIEM continues to rely on a great deal of manual analysis by human security analysts. There’s no doubt that AI easily crushes human speed in examining data.
With conventional SIEM’s obsolete methods and limitations in scale and speed, it is easy for AI-toting threat actors to overcome SIEM’s cybersecurity “benefits” — if there are still any left. Cybercriminals make use of AI tools to quickly produce malware and probe vulnerabilities in networks and systems. It is not unusual that threat actors have already come up with new attacks before an organization discovers vulnerabilities in their systems.
Harnessing AI for better cybersecurity
AI is a potent tool for cybersecurity, and it should be used extensively in building cyber defenses. Next-generation SIEM, which is an upgrade for conventional SIEM, takes advantage of AI to address information overload. SIEM alternatives such as Open XDR (Extended Detection and Response) and NDR (Network Detection and Response) also use artificial intelligence to address the weaknesses of conventional security tools. Below are examples of how SIEM alternatives leverage AI to improve security.
Advanced Threat Detection — AI plays a role in enabling advanced threat detection, especially when it comes to analyzing vast amounts of security-related data, network traffic, user behaviors, as well as system logs. AI algorithms make it possible to simultaneously undertake multifaceted threat detection covering various sources, including disparate hardware and software. The role of AI in cybersecurity is expected to grow further as threats become more complex and overwhelming.
Automated Incident Response — Supplementing advanced threat detection is the automation of responses to security incidents. Machine learning is vital in enabling automated responses, as demonstrated by the automation involved in Security Orchestration, Automation, and Response (SOAR), which is not necessarily a SIEM alternative but another security tool that can fill gaps in conventional security information and event management. SOAR can integrate machine learning to create automated incident response workflows and correlate security data to improve detection accuracy, cut response times, and allow human security analysts to focus on tasks that necessitate more complex scrutiny and decision-making.
Predictive Analytics — Another function that harnesses AI to provide better outcomes (compared to conventional SIEM) is predictive analytics. It gathers huge amounts of data to identify trends and patterns and come up with forecasts about potential attack vectors. It helps organizations anticipate attacks and prepare the necessary mitigation and backup measures. AI-powered predictive analytics serves as a proactive tool against new threats, including zero-days that tend to evade rules and identity-based detection mechanisms.
Behavior Analysis — Lastly, AI is useful in analyzing behaviors and patterns of action to spot potential anomalies or malicious intentions. This is exemplified by the use of machine learning in User and Entity Behavior Analytics (UEBA), a SIEM alternative, which benchmarks normal/safe behavior and identifies harmful or anomalous behavior, especially those associated with insider threats.
Finding the best SIEM alternative
Some will probably argue that “SIEM alternative” is rather a misnomer since the basics of SIEM are not exactly being abandoned as organizations look for alternatives to conventional SIEM. Organizations still need an effective management system for their security data and event responses. These alternatives may have different names and advertised functions, but they still incorporate the fundamentals of SIEM.
The path towards finding the best SIEM alternative is paved with SIEM components together with new features and functions that take advantage of artificial intelligence to address the new challenges that come with new threats and more resourceful and cunning cybercriminals. Cybersecurity is generally agglutinative, which means it evolves by adding more technologies and functions instead of completely replacing old ones unless they are already decidedly obsolete and inapplicable.
About the Author — Tim Ferguson is a tech writer and the editor of Marketing Digest. He enjoys writing about SaaS, AI, machine learning, analytics, and Big Data. He spends his free time researching the most recent technological trends. You can connect with him on LinkedIn.
Originally posted on OpenDataScience.com
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