AI has been in the news for the past few months, but not everyone is enthusiastic about it. Many prominent technologists have expressed concern about the risks associated with it, and there is a legitimate concern that artificial intelligence will do more harm than good. For example, there are reports that AI is helping cybercriminals create malware that is hard to detect.
It’s heartening to know that cybersecurity is one of the first to take advantage of artificial intelligence. Cybersecurity companies have developed ways to integrate AI into their detection, mitigation, and prevention capabilities. Next-generation security information and event management (SIEM) is especially hot as organizations try to keep up with the increasing aggressiveness and complexity of cyber threats.
Next-Generation SIEM: Addressing SIEM Limitations
SIEMs have been around for nearly 20 years. Introduced in 2005, this security solution combines the benefits of previously separate log and event management systems. It was the foundation for most security processes in the Security Operations Center (SOC). This has enabled organizations to leverage vast amounts of security-related data captured at various points in time to enhance their threat monitoring and attack handling capabilities.
But as the threat landscape has changed, SIEMs no longer offer the benefits they once did. As networks grow exponentially and IT infrastructures move to cloud and hybrid setups, they are experiencing log overload and scalability issues. The increasing complexity of network deployments and configurations makes it difficult to keep up with advanced and persistent threats. Additionally, traditional SIEMs are ineffective against zero-day attacks due to their reliance on rules and threat signatures, lack of contextual awareness, and lack of real-time incident response.
Next-generation SIEMs incorporate new technologies and strategies such as behavioral analytics, contextual and threat intelligence integration, real-time monitoring and incident response, automation, increased scalability and flexibility, and integration with other advanced cybersecurity technologies. to address these weaknesses. The most notable is Machine Artificial Intelligence.
how artificial intelligence can help
Artificial intelligence is one of the big upgrades for next-gen SIEMs. It has the ability to target critical weaknesses, especially given the rapid generation and evolution of attacks. Today’s cybercriminals can take advantage of new technologies, especially AI, to rapidly generate malware and scan for vulnerabilities. Leveraging AI to optimize cyber defenses makes a lot of sense.
Addressing Over-Reliance on Rules and Threat Signatures
Reliance on predefined rules and threat identification (signatures) has been one of the biggest weaknesses of traditional SIEMs. The system has no knowledge of zero-day attacks and fails to detect and prevent zero-day attacks. In some cases, threat information arrives late and the damage has already been done.
Artificial intelligence is used in many security technologies such as User and Entity Behavior Analytics (UEBA), Next Generation Antivirus (NGAV), and Enhanced Detection and Response (XDR). These advanced solutions go beyond the rules of threat identification and detection. They look at other factors to identify abnormalities or deviations from activity patterns that are considered regular or safe.
For example, UEBA uses machine learning techniques such as Bayesian networks, supervised and unsupervised learning, reinforcement learning, and deep learning to more effectively detect threats, including threats posed by insiders. In NGAV, the system employs a machine learning system that can perform string analysis, n-gram analysis, entropy, API command analysis, binary content visualization, and control flow graphs to more effectively detect threats, especially unknown threats. To do.
XDR uses artificial intelligence to analyze threat intelligence data alongside telemetry from IT systems, perform in-depth analysis of dynamic data, perform real-time queries, and analyze raw, unstructured data. Run a trained model based on and use a classifier to make predictions. Make an attack and determine the best response.
Provide contextual awareness
Contextual awareness is not entirely absent in a traditional SIEM, as it can correlate security events and logs from a variety of sources. However, this can become a chore if your SIEM doesn’t present everything right away and give you the tools you need for quick comparison and analysis. Next-generation SIEM enables contextual exploration using AI to rapidly perform advanced analytics, user profiling, contextual enrichment, threat intelligence integration, big data analytics, and endpoint and detection response integration is significantly easier.
One of the biggest benefits of contextual awareness is a significant reduction in false positives. In many cases, SIEMs incorrectly flag activity as threats due to inappropriate and often too conservative detection settings. Artificial intelligence solves this shortcoming by correlating security data from disparate sources and flagging only genuine threats. Similarly, it addresses false negatives and false beliefs that threats are harmless.
A high frequency of false positives may seem harmless, but it can prevent your SOC from addressing more urgent threats. A flood of false positive security alerts makes it difficult to respond to critical security incidents in a timely manner. It can also cause alert fatigue, resulting in missed threats and overworked cybersecurity teams.
Enhanced real-time response
One of the goals when security information and event management was developed was to speed up response to threats. Unfortunately, the sheer volume of data generated today makes it very difficult to respond quickly to threats, let alone respond in real time. Inspecting all security-relevant data is a daunting task, especially for networks with a large number of connected devices. Each device can generate data that adds to the pool of security information that cybersecurity teams need to evaluate.
AI enables a kind of intelligent alert triage, examining the security and impact of alerts, allowing security teams to focus their efforts on the most urgent and important incidents. An AI-powered next-generation SIEM can help you contextualize data and prioritize it accordingly, significantly reducing the amount of security data that requires human evaluation. This ensures that the most important alerts are addressed first and prevent escalation into more serious problems.
In addition, artificial intelligence enables automation and orchestration. When combined with other security solutions, such as threat intelligence platforms and centralized incident management solutions, her AI-driven SIEM automatically responds to specific alerts, delivering only more complex threat alerts to human cybersecurity analysts. can.
More intelligent threat detection and prevention
Next-generation SIEMs offer significantly better threat detection and prevention capabilities with advanced threat detection capabilities, behavioral analytics, security information correlation, and real-time response capabilities. It’s not a perfect cybersecurity solution, but with the help of artificial intelligence, we can do more in response to the new challenges posed by the modern cyberthreat landscape.
Like the cliché, AI has strengths and weaknesses. But it is an inevitable reality for everyone. The best way to live with it is to ensure it is used for the right purposes, and one of his ways of doing so is leveraging AI in cybersecurity through next-gen SIEM. This is one of many ways to maximize the benefits of AI and respond intelligently and efficiently to the growing cyberthreats.
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Peter Davidson works as a Senior Business Associate helping brands and start-ups make effective business decisions and plan the right business strategy. He’s a big gadget freak and loves sharing his thoughts on the latest technologies and applications.
