A malware powered by Artificial Intelligence is now capable to adapt itself to the targeted infrastructure, by modifying its code and behavior during the attack. And no human intervention is needed to make these changes. AI is profoundly changing cyberattacks. Malware, bots, phishing, ransomware… all types of threats can now be amplified by AI, enabling attackers to target their victims at reduced costs, while increasing the scope and severity of attacks. With AI, the practice of social engineering is increased, intrusions into IT systems are easier and attacks are accelerated.
Yet, according to research conducted by Gartner, only 24% of businesses’ cybersecurity systems are truly ready to deal with AI-related risks. It is now clear that humans alone are not capable of responding to AI-fueled attacks. To counter them, XDRs are also starting to use AI. Through Machine Learning algorithms and, in the most innovative XDRs, Deep Learning, AI increases detection and response capabilities for all types of cyberattacks, even the most unpredictable.
Machine and Deep Learning algorithms at work in XDR platforms
Machine Learning has become invaluable within an XDR to cope with AI-generated cyberattacks. Based on pre-existing data, the Machine Learning algorithm learns tasks to perform automatically, such as detecting malicious files. Thanks to Machine Learning, XDR platforms are now able to analyze large amounts of data very quickly and detect unusual patterns or anomalies that could be potential threats.
However, traditional Machine Learning algorithms have been largely overtaken by the capacities of artificial neural networks, and the most technologically advanced XDR platform editors are already using Deep Learning to counter cyberattacks.
Deep Learning is a set of Machine Learning techniques based on deep neural networks, which are capable of performing complex tasks with unprecedented success rates. The astonishing advances made by Deep Learning researchers have enabled engineers to apply these technologies in Artificial Intelligence tools that can now learn continuously, perfect themselves and autonomously adapt to changing environments, revolutionizing the industry and more particularly computer defense platforms. Deep Learning algorithms specialized in cybersecurity analyze large quantities of data to spot anomalies or patterns of suspicious activity. Thanks to their ability to adapt autonomously, they are now able to change automatically to counter new threats, making detection and responses to attacks more effective. It is your best ally to counter AI attacks with AI.
Protecting you from previously undetectable threats
When it comes to cybersecurity, it’s all about how quickly and accurately threats are detected in your infrastructure. Preventing attacks before they have a chance to harm you is crucial to your business.
An XDR with AI is able to access and analyze more data than a traditional XDR. An XDR platform with AI can perform analyses on every layer of your infrastructure, including those that were previously inaccessible to your analysts.
Thanks to advanced statistical analyses and Machine Learning, AI analyzes your logs and compares current activities on your infrastructure to detect any unusual action on all your infrastructures: your servers, workstations, network, etc. Additionally, while a traditional XDR is limited to detecting only known malicious files, an AI-powered XDR with Next Generation Antivirus (NGAV) can detect unknown malicious files.
So, how does it work? Your XDR is equipped with physical or software sensors that are installed on the equipment in your infrastructure. These sensors monitor the activities and events of your infrastructure to detect possible cyberattacks. There are different types of sensors: network, behavior, content and security sensors. All these sensors monitor continuously and are pre-trained to recognize any anomalies in the data they collect. If an anomaly is detected, the sensors immediately send the information back to the XDR. In the most powerful XDRs, the information is sent to a datalake which feeds Deep Learning algorithms, studying these behaviors, and finally detecting any possible threat.
AI analyses to accelerate responses to attacks
During a cyberattack, a ransomware paralyzes more than 500 devices per minute. To counter such severe attacks, your responses need to be automated and supported by AI. The main challenge is to automate the response and to make the work of SOC teams easier with an XDR, and speed up their processes.
For a faster respond to attacks, specialized Deep Learning models within your XDR provide your teams with better contextual information and help them analyze all the data, activities and events in your IT infrastructure. The large number of alerts your analysts have to monitor and process has been proven to be one of the main problems slowing down operations during attacks. Accelerating the processing of these alerts means considerably reducing response time to cyberattacks.
Moreover, by performing analyses faster than a human, AI is able to predict future cyberattacks and to identify their mechanisms to determine their origin. But the capabilities of an XDR with AI now go even further in terms of analysis, as an XDR can also automatically prioritize alerts, so that your teams can immediately focus on the most critical ones.
In addition to this, Gartner recommends using a SOAR as an additional way to be more effective in your responses to attacks. A SOAR is integrated into your XDR and orchestrates all your security solutions during a cyberattack, so that you don’t have to deal with each one separately. When combined with AI, the SOAR takes over some of your analysts’ work, by activating a remediation according to a given playbook or by automating their most repetitive tasks. These tasks generally add little value (for example creating and filling in tickets) and used to slow down your teams’ remediations.