AI/ML tools and technologies heavily influence the modern digital landscape by introducing numerous use cases involving AI-based malware detection, preventing social engineering attacks, and threat identification and remediation. AI/ML tools can significantly enhance the pace of response results when encountering cybersecurity incidents, as they automate real-time threat detection and mitigation strategies. AI/ML solutions, when leveraged with predictive analytics and threat intelligence techniques, can enable organizations to forecast potential threats and/or cybersecurity incidents. Learn how to get started to design and build AI/ML solutions that are capable of revolutionizing the cybersecurity threat detection landscape. Many organizations have acknowledged AI/ML's prominence in the cybersecurity threat landscape and have started to leverage AI/ML in their business and security requirements. Below are the statistics taken after thorough research highlighting the impact of AI/ML tools and techniques on cybersecurity threat detection and increasing overall cybersecurity performance. According to the Global State of Security Operations report, AI/ML powers 93% of SOCs (Security Operations Centres) for threat detection. Earlier, anti-virus or malware detection systems heavily relied on known patterns or behaviors depicted by malware, explicitly known as "attack signatures." However, this methodology makes these solutions less effective against zero-day malware attacks. Let's explore some key use cases of AI-driven security solutions in enhancing cybersecurity threat detection. A SOC's day-to-day tasks include threat monitoring, researching varied log datasets, detecting and identifying threats, and providing remediation steps to lower the impact of security incidents. They help recognize known attack patterns while also adapting to identify previously unknown signatures, helping security systems stay ahead of emerging threats. Hence, cybersecurity professionals and software developers have also started using AI/ML to build more innovative, adaptive, and robust systems and applications to protect an organization's IT and network infrastructure. No one can deny the significant impact that AI and ML solutions are going to have on cybersecurity threat detection. These models play a crucial role in predicting and preventing phishing attacks while analyzing large datasets (both structured and unstructured) to analyze patterns and create attack signatures, consequently easing the early identification and remediation of such attacks. AI/ML helps swiftly detect and respond to cybersecurity threats in a dynamic and complex cyber landscape. Attempts to suspicious activities like unauthorized access, data exfiltration, insider-driven breaches, etc., can be detected in real-time, significantly minimizing the impact of cybersecurity incidents. According to a report presented by Statista, the market for AI in cybersecurity is expected to show considerable growth in the coming years, from around 24 billion U.S. dollars in 2023 to roughly 134 billion U.S. dollars by 2030. However, at the same time, we have also witnessed an exponential increase in the cybersecurity threat landscape.
This Cyber News was published on feeds.dzone.com. Publication date: Wed, 02 Oct 2024 17:43:09 +0000