The Evolution and Future of SIEM: Integrating Advanced Analytics and AI for Enhanced Cybersecurity

Threat researchers designed early versions of SIEM over 20 years ago to collate information from various IT tools to make sense of the many threats. At the time, IT resources collected data and generated logs. Still, they were disconnected, so it took time for a security professional to combine the information to get the complete picture. No single tool could collect data from firewalls, intrusion detection software, switches, network logs, devices, and routers.

In the early 1990s, early intrusion detection systems (IDSes) arrived on the scene, but they were not without limitations. These programs used a set of rules to evaluate network traffic and detect known threats. The problem was that the software generated a lot of false positives, wasting time and money. IDSes added another resource to the already disconnected puzzle. Threat researchers recognized the need to combine all these logs into one usable tool and identify actionable events, and SIEM was born.

SIEM products changed network security by collecting data from multiple sources and translating it into one common schema to create rules and correlate information coming from different logs. One standout feature was incorporating user and entity behavior analytics (UEBA), which added an additional data layer to extrapolate security concerns. Other early iterations include ArcSight SIEM and SOAR. 

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These revolutionary security systems allowed cybersecurity professionals to pinpoint threats in real time and prioritize and evaluate the mountains of data coming from firewalls, antivirus programs, and IDS.

The first SIEM tools came with some fairly extensive limitations, such as basic reporting and skimpy dashboards. They lacked sophistication and were not scalable. These early tools included vulnerable rule-based triggers that threat actors could easily manipulate. Another considerable drawback was the slow processing of each stage of threat detection (collecting data, defining policies, rules, and thresholds, reviewing alerts, and analyzing anomalies), making proactive response impossible. Many of these tasks required human intervention, taking even more time.

Modern SIEM solutions have come a long way and have the advantage of using AI and machine learning to extend their capabilities. However, some hurdles still exist.

Due to the above mentioned issues, AI and advanced analytics are taking center stage in finding a solution. The advent of big data and low-cost storage options helped make SIEM more efficient. For example, Amazon S3 and other providers solved the issue of long-term storage of archived data.

In 2015, AI and machine learning were introduced to the mix to improve SIEM further. Big data analytics and machine learning were integrated into SIEM tools to quickly process much larger volumes of data without manual evaluation. Detection of zero-day threats and new and known threat attack patterns improved considerably. Additionally, these technologies opened the door to deploying SIEM tools in cloud-based environments. AI and advanced analytics offer cybersecurity professionals faster processing power, more efficient threat detection, and expertise in identifying attack patterns. Because these AI tools can process more data faster than a human can, they also dramatically improve accuracy and productivity.

One area where AI is making huge strides in security applications is automated threat detection. AI-powered automation improves the threat detection process of complex and emerging threats. A well-designed AI-enhanced SIEM system can identify and alert security personnel about threats much faster than a human threat researcher can. 

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An example might be a SIEM system designed to detect sophisticated social engineering tactics in phishing emails. As attackers evolve, emails become more legitimate without the usual poor grammar and bad English. A properly prompted AI tool could detect minor anomalies, flag them as spam, and block them from entering a network. A human may be fooled, but an automated system can pick up on slight nuances that indicate a threat.

AI is also used for predictive analytics to foresee and mitigate potential security breaches. By automating user activity and behaviors, the system could thwart an intrusion by monitoring thousands of users at once, looking for specific attack patterns. 

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For example, if the system noticed a particular user logging on at an odd time of day and attempting to access specific areas of the network, the AI tool could block that user’s login until a security professional could investigate further.

Enhanced data management is another area where AI is advancing cybersecurity efforts. Data is increasing at a staggering rate; AI can more efficiently sort through vast amounts of information and identify threats. AI uses computing power to process large amounts of data and can clean it to correct malformed data and remove duplicates to help avoid false positives. 

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Artificial intelligence also has the ability to separate essential data from non-essential, making data monitoring more efficient. As the amount of data increases, AI will also need to evolve and use increased computing power and evolutionary methods to handle more information and extrapolate only what is useful.

Although AI is nothing new, the latest and greatest breakthrough is ChatGPT, which is what everyone is talking about. There is a frenzy in every vertical, including security, to find ways to use large language models to enhance efficiency. Right now, ChatGPT has the ability to analyze a document or information and produce a human-level quality summary of that information. Humans can also have conversations with ChatGPT, ask questions, and get confident answers. However, there is one caveat: ChatGPT is in its infancy and may provide incorrect answers even though it believes they are correct. 

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So, it’s safer to trust but verify when using ChatGPT for anything essential. That being said, ChatGPT can shorten the learning curve and automate complex tasks. For example, threat professionals must be SQL programmers and understand how to exploit various query languages to do their jobs efficiently.  

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Chat GPT can make it quicker and easier for newcomers to ask questions and get answers without learning the programming language. Overall, AI and language models are transforming the functionality and accessibility of SIEM systems, primarily impacting speed, efficacy, and success.

AI is also impacting specific threats, making cybersecurity more efficient. For example, phishing attacks target individuals and companies through deceitful emails. Security tools with built-in AI can attach to these email servers, detect phishing emails quicker and easier, and block or quarantine them so they never get delivered to the user.

In terms of ransomware attacks, AI helps in a few different ways. For example, companies are using machine learning algorithms built into AI to automatically detect patterns and anomalies much quicker than human security professionals can. Along with monitoring and detection, cybersecurity experts also employ AI to automatically respond to threats without any human intervention.

Along with the whole host of SIEM solutions out there, some security providers also offer specialized solutions such as EDR (Endpoint Detection and Response), which is host security that monitors an endpoint for any potential threats. If AI detects any security issues, it automatically triggers a response based on a set of predefined rules. As AI learns, it adds more rules to the mix. Another example is email products specifically focused on detecting phishing emails. SOAR playbooks are a type of specialization where the provider codifies a company’s practices. The problem with this is that someone has to manually interview each company and code that into the product for a specific client. These playbooks are seen as an auxiliary function to SIEM.

Technology has been on the bullet train for many years and continues to evolve rapidly. SIEM revolutionized cybersecurity by consolidating data coming from various sources, organizing and analyzing it to identify threats, and responding to them. As the volume of data increased and threats evolved to be more sophisticated, SIEM efforts needed to catch up. Modern SIEM models use AI and machine learning (ML) to solve these issues and confidently face future challenges.

AI-based SIEM solutions automate the collection, normalization, and analysis of data. Relying on ML and big data, the AI system can quickly and efficiently sift through volumes of data to proactively detect threats and respond to them automatically before they become a security incident. AI turns SIEM into a sentry that protects the company, minimizing the opportunity for security breaches or attacks before they occur.

An example would be a company using AI with SIEM to monitor, detect, and respond to threats—a hacker collective attempts to breach the company servers. The ML engine has learned the patterns of this hacker group and knows what to look for to identify threats. As soon as they roll out their standard procedure for breaching the company network, the AI detects their presence and takes action to prevent the breach. Antivirus and anti-malware programs are other ways AI detects threats and minimizes damage by quarantining files and software it deems dangerous.

Another area of focus is integration with emerging technologies. Historically, SIEM and SOAR were separate entities. SIEM handles the monitoring and detection, and SOAR handles the response. Vendors have been acquiring SEIM or SOAR companies to offer both sides of the coin, but the technologies are still divided into separate solutions, making them less effective. 

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The future is building products from the ground up with combined SIEM and SOAR that use AI and predictive analytics out of the gate. Now, we have fully integrated solutions that do it all: monitoring, detecting, and responding, and everything works together seamlessly.

SIEM will integrate with machine learning, blockchain, and IoT for comprehensive security.  New programs, devices, and solutions are popping up constantly. Therefore, SIEM solutions will need to be flexible enough to include technologies that do not exist today but will tomorrow. Adding these resources on the fly needs to be simple and effective.

The biggest change in future SIEM solutions is a focus shift from a reactive to a proactive posture. The goal of modern SIEM systems is to automatically identify and mitigate cyber threats before they happen, while minimizing the need for transactional oversight from a threat analyst. While there have been overstated concerns about AI replacing humans, the highest probability scenario is an AI/Human combination, where AI manages the low-level processes and trained analysts focus on higher-level strategic challenges. 

AI is also impacting SIEM solutions by changing the user interface and increasing accessibility. Generative tools like ChatGTP combined with NLP (natural language processing) are making it easy for analysts to ask a security question in plain language and get an immediate answer regardless of the complexity of the question. 

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The leading edge AI tools can sort through billions of data points (now measured in petabytes) to arrive at an answer within seconds, rather than days. Thus, AI has the potential to reduce the learning curve with proprietary query languages, making SIEM systems more accessible to a broader range of users. Security professionals won’t need to be programmers or understand the languages they monitor. AI/NLP can do the heavy lifting for them.

Automated threat response is one of the most crucial areas of future enhancements expected in SIEM tools. AI within detection systems can streamline the incident response approach and threat management, eliminating the need for separate systems, extensive programming, and, in many cases, even human intervention. While AI can always respond faster than a security expert, humans (as mentioned before) need to be in the loop.

Machine learning, based on big data, is expanding in reach. AI-powered SIEM systems are advancing rapidly, increasing their abilities to profile threats, detect anomalies, and create rules about what normal behavior looks like and what is considered a threat.

Data privacy and regulatory compliance are another area where future improvements will come into play. It’s no secret that governments, especially in Europe, prioritize privacy and security, and the corresponding laws are constantly changing. SIEM solutions are adapting to meet the increasing emphasis on data privacy and regulatory compliance rules worldwide. How are they doing so without compromising effectiveness?

The challenge comes when the law states that you cannot look at something even though you need to for security purposes. For example, in some countries, employees’ emails are private, even though they are company emails and company laptops. So, as a security professional, although you cannot read people’s emails as users receive them to look for phishing attacks, you can move that role to the server, examine them as soon as they arrive before they are delivered, and quarantine any suspicious emails.