The Evolution and Future of SIEM: Integrating Advanced Analytics and AI for Enhanced Cybersecurity
Security information and event management (SIEM) is a proactive cybersecurity solution designed to detect and respond to vulnerabilities and online attacks in a business environment. Initially, developers designed SIEM as a log management tool for compliance and auditing. It is now widely used across organizations for advanced threat detection, investigation, and response. SIEM is a crucial notch in the cybersecurity tool belt.
What began as a log management tool has evolved into a sophisticated network of cybersecurity processes and tools that help companies detect and respond to threats before they disrupt business operations. As the threats evolve, SIEM must also improve and keep up. The introduction of AI and machine learning has radically changed the cybersecurity landscape and will continue to aid threat researchers in meeting future demands.
Historical Development of SIEM
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.
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.
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The idea behind SIEM came from combining security information management (SIM) and security event management (SEM), offering a more dynamic approach to cybersecurity. Examining the data coming from all these IT/security tools and the events themselves offered insight into hackers’ threats and behaviors. Despite SIEM’s evolution, challenges that require even further change remain, and part of the solution lies with AI and advanced analytics.
The first big issue is data overload. Most organizations deal with volumes of data and users that can easily overwhelm systems and hinder the effectiveness of SIEM systems. A typical company experiences billions of events per day. Although some systems have kept pace with the changes, only some can handle the sheer volume of data from all angles.
The growing scale of cybersecurity challenges has spread resources (both human and technical) thin. Data has gone from terabytes to petabytes overnight. Even big teams of security professionals need help to keep up with that scale, creating a need for more advanced solutions that can pick up the slack and automate many of the manual security tasks.
Another issue is the evolution of cyber threats. The rapid changes and increasingly sophisticated cyber threats challenge traditional SIEM solutions, which designers initially crafted with a specific model in mind. These systems need to be more flexible, able to pivot quickly, and able to adjust to threat landscape changes as they occur. There is much need for improvement here.
SIEM professionals must work on integrating SIEM with various security tools and systems that need more uniformity. When there were only a few solutions, it was easy to cover them all. Now the market overflows with devices, software solutions, and security resources, all of which must integrate seamlessly with SIEM systems Along with better integration, these solutions must adapt to more sophisticated attacks with built-in detection and evasion techniques.
In terms of retrospective and forensic analysis, time is of the essence. Current SIEM solutions take too long to analyze the vast amount of collected data. Therefore, clients must cut down data storage to only the past 90 days or, if the budget allows, the client can preserve the previous six months instead of a year’s worth.
Storing all that data to review comes at a cost. It’s frustrating for clients and cybersecurity professionals that it takes so much time to sift through the data and identify a threat while it may still be active on the network, causing problems. In many cases, forensic analysis can take weeks to complete.
Clients with bigger budgets can store more data. But eventually, they will roll it off and archive it. However, bringing that data back when there is a reason to investigate, for example, two years down the road, can complicate things. Not only will it be expensive, but it could also compete with the live SIEM system already running, forcing the need for a second deployment system to quickly and easily unarchive old data. Their current SIEM is geared towards monitoring present-day threats, resulting in redundant and expensive systems.
Industry leaders like Anomali are working hard to change the current state of the art by integrating AI, machine learning, and advanced analytics to solve some of these issues and improve the efficiency of SIEM solutions. One example is security orchestration automation and response (SOAR), which uses APIs to integrate security tools with SIEM solutions, further enhancing its capabilities. SOAR aims to accelerate threat detection and response to contain a crisis before it becomes unmanageable and costly.
The Role of AI and Advanced Analytics in SIEM
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.
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.
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.
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.
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.
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.
Case Studies and Real-World Applications
They say a picture is worth a thousand words. Case studies often reveal secrets into the world of SIEM that cannot be seen any other way. The details of real-world problems and how they are solved by SIEM offer a more in-depth perspective.
Case Study I: Centralized Threat Intelligence
Recently, Anomali provided a SIEM-based solution to a large financial services client in the U.K. The company has 25,000 employees, 100 of whom are on SecOps. The client wanted to centralize threat intelligence while also supporting a long list of premium third-party integrations. Their current SIEM solution could not handle the quantity of information entering the system, which limited their security team’s effectiveness.
Anomali sped up the detection and response process from weeks to minutes by elevating the company’s security infrastructure using SOAR integrations of QRadar, Splunk, Intezer, and ThreatWorx (via ThreatStream). These tools helped their SecOps team contextualize threats and automate security workflows, effectively thwarting attacks before they get in the door. The end solution allows the client to identify which threat actors and malware might be targeting them by correlating all traffic through these centralized security protocols.
Case Study II: Solved Blackhawk’s Disconnected SIEM System
Blackhawk Network Holdings is a private company in the prepaid debit card, gift card, and payments industries. Their threat intelligence platform was a patchwork of tools pieced together, but none of them integrated into their SIEM solution or could provide enough context around compromise indicators to understand their potential impact.
As a financial company, Blackhawk was naturally a target for attackers. Although they used many security platforms for threat intelligence and event management, each one had its own dashboard, and none of them integrated with SIEM or communicated with each other. They received thousands of alerts daily, with security personnel spending an excessive amount of time evaluating and analyzing the threats. The team was struggling to keep up with responses. They turned to Anomali for a solution.
Anomali implemented its ThreatStream® solution to sync actionable threat intelligence with its SIEM alerts, integrating disparate threat feeds into a single, easy-to-use, and understandable dashboard. The solution also offered Blackhawk Network Holdings the context around IOCs to understand what was important and what was not. Through a system of consolidation, integration, correlation, and detonation, Anomali brought everything together in one place so Blackhawk’s team could focus on critical threats and issues and respond faster.
Future Trends in 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.
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.
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.
Anomali's Role in SIEM and Emerging Innovations
Anomali is a leader in the security industry, laser-focused on modernizing security operations using AI, advanced analytics, and automation. Our goal is to uncover unmatched levels of security intelligence, threat detection, cyber exposure management, and response to keep our clients safe. We accomplish this by helping global SOCs transform their system to be more efficient and cost-effective.
As Anomali heads into the future, we plan to use emerging technologies like AI, ML, and advanced analytics to improve SIEM in all areas further, blazing new trails with our innovations.
Understanding how SIEM fits into a company’s overall ecosystem is a crucial piece of the puzzle. Other factors that affect the future of SIEM include knowing precisely who the customers and suppliers are and how we exchange information with them safely. Examining all use cases is an effective way to determine what to include and what to ignore.
Customers need first to understand their goal for the system and then how they will track its success. These are the ingredients for creating futuristic SIEM solutions that work for the customer. Another issue is determining whether we have the correct logs to provide the necessary information and whether we manage the tools generating these logs. If not, do we need to build a relationship with another department or integrate a system between two or more tools to get all the necessary information to make an informed decision about the data?
Without AI and cloud scale, these are not manageable problems for a security analyst who can maybe focus on 15 cases in an hour, spending only four minutes on each one. If any are serious, they will take even more time to review. Tools needed to take away all that work and give you instant answers are going to be the next generation of SIEM.
Combining detection with threat response is the key to timely mitigation of attacks. This will include systems talking to other systems and communicating automatically to remove or block malicious users. AI will help eliminate work and empower analysts while making security analytics more effective and affordable.
SIEM has come a long way since its inception, and threat researchers are constantly looking for new ways to improve threat detection and response. Thankfully, companies like Anomali are blazing new trails towards a safer and more secure online experience. You can contact us today to learn more about how Anomali can help you improve your SIEM capabilities.
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