Artificial intelligence is quickly becoming a staple in cybersecurity. Many companies now offer security solutions that integrate machine learning and other forms of AI to enhance threat detection, mitigation, and prevention. Most of the top results for a Google search with the keyword phrase “cybersecurity solution” are cybersecurity providers that offer AI-enhanced products.
One specific cybersecurity technology that employs AI is next generation security information and event management (SIEM). An update to the nearly two-decade-old SIEM, this upgrade of the popular cybersecurity tech comes with new capabilities augmented by artificial intelligence. Here’s a look at the top three ways AI is bolstering the benefits of next gen SIEM.
1. Enhanced threat intelligence and detection
SIEM was created to consolidate the collection and analysis of cybersecurity data to facilitate more efficient threat management. Organizations have been accumulating vast amounts of security-related data. However, they have not been making the most out of this data because the data is in various disparate tools that have not been integrated. SIEM brings together all of this data and the mechanisms to respond to threats under a unified interface.
However, with the exponential explosion of data creation in modern IT environments, traditional SIEM is no longer enough. Also, data has become more complex. Consolidation and unified management are no longer enough to handle the massive amount and variety of data generated. Human security analysts cannot keep up with the constant stream of data. To address this significant change, an assistive tool or technology is crucial.
For this, next gen SIEM harnesses the benefits of artificial intelligence. It is helpful in precisely automating data collection and aggregation in threat intelligence. AI can quickly gather data from various disjointed sources to ensure up-to-date real-time threat identification. Additionally, AI can automatically examine data to ensure compatibility, accuracy, and completeness. This frees up human security analysts’ time to focus on more crucial and complex tasks.
More importantly, AI can simulate what human security analysts do in evaluating data to spot threats and vulnerabilities. Through machine learning algorithms, next gen SIEM augmented by AI can sift through large volumes of datasets to identify anomalies, which may not be detected as potential security breaches under conventional automation systems. Automation existed in cybersecurity even before the advent of advanced AI, but it is not the same as automation which can be done nowadays with the help of machine learning.
Ultimately, AI dramatically improves the speed and accuracy of threat detection. It makes optimal use of all available data in an organization, both internal and external. It ensures that data is a useful asset in cybersecurity, not a burdensome ocean of information that hardly makes sense in threat detection efforts.
2. Real-time and adaptive incident response
AI does not only bolster the information gathering and management aspect of next gen SIEM. It also plays an important role in incident response. Just like what human security analysts do, it enables the autonomous response to threats as they are spotted. AI is not limited to presenting actionable data, which is intended for human security analysts. To some extent, it can make incident response decisions on its own with excellent accuracy.
In other words, AI enables real-time response. Next gen SIEM solutions can perform autonomous decisions based on threat signatures and behavior analysis. They scan for threats that match the compiled threat profiles and at the same time scrutinize files, apps, and user actions to find patterns that could be indicative of malicious or adversarial actions.
The behavior analysis part tends to be equated to user and entity behavior analytics (UEBA), which is often seen as a separate cybersecurity solution. However, in the context of the next generation SIEM solutions being offered by security vendors nowadays, UEBA may be a part of the next gen SIEM solution alongside network detection and response (NDR) and sandboxing. Next gen security information and event management may serve as the consolidation platform that brings together different solutions under a simplified system that expedites not only the detection of threats but also response.
Moreover, next generation SIEM can adapt to evolving threats. No state-of-the-art cyber defense will ever be perfect. There will always be instances when attacks manage to evade detection and prevention mechanisms. Perpetrators can come up with new ways to change the patterns of their actions to mimic normal or safe activity patterns. Human analysts, however, will eventually know about these tactics and intervene to detect and stop the attack. In the process, AI-powered NG SIEM learns from the intervention and tweaks its response mechanisms to detect the same attack or new variants of it.
AI-augmented next gen SIEM can dynamically refine its response strategies. It does not have to be reconfigured every so often. It automatically commits to memory the threat evolution it observes and imitates the actions of human security analysts in responding to emerging threats. It can automatically revoke access permissions, isolate infected systems, and initiate forensic analysis in a speedy and timely manner.
3. Contextual insights and predictive analytics
Traditional SIEM was conceived to enable more efficient, dynamic, and proactive security information management and threat response. It supposedly provided a highly viable alternative to the generally reactive cybersecurity systems most organizations used in the past. However, this dynamism and proactiveness have been reduced to mere buzzwords in the face of overwhelming amounts of data and rapidly evolving cyber threats. Cybersecurity teams now find it difficult to be proactive as they encounter vast amounts of data and unknown attacks that do not fit existing threat signatures and activity patterns deemed anomalous.
Artificial intelligence helps address this formidable challenge through data enrichment and contextualization. Data enrichment is the process of augmenting data with new information obtained from various sources to address incompleteness, inconsistencies, and incompatibilities with other data. Data contextualization, on the other hand, is also about supplementing data to provide context or to build the bigger picture of the threats and security situation an organization is facing. It is often undertaken as part of data enrichment.
AI’s ability to build context for security data makes it possible to automatically prioritize security data, particularly when it comes to alerts and notifications. Instead of showing all alerts chronologically, AI-powered NG SIEM identifies the most urgent alerts and puts them on top to make sure that they are addressed first and not buried under myriad other alerts that are not as crucial.
Additionally, AI allows next gen SIEM to conduct predictive analytics to anticipate other vulnerabilities and threats. The system can examine historical data and look into new trends to predict new attacks and make sure new tactics do not evade detection. Or, at the very least, these zero-day attacks should be more easily isolated and mitigated. This makes NG SIEM a strategic tool that helps organizations more effectively manage risks and allocate limited resources.
Smarter threat management
To be clear, artificial intelligence does not replace human security analysts. It only serves as an extremely powerful tool, an aid to more rapidly and accurately detect and respond to threats. AI helps cybersecurity teams cope with the evolution of cyber attacks, especially since threat actors are also using artificial intelligence in their felonious pursuits. Next generation SIEM greatly benefits from AI, which in turn helps cybersecurity teams in building more powerful defenses. AI and cybersecurity are not just an intelligent combination; they are a necessary integration.
