https://en.wikipedia.org/wiki/Applications_of_artificial_intelligence https://www.darkreading.com/application-security/ai-in-software-development-the-good-the-bad-and-the-dangerous https://sites.google.com/view/howtouseaiinapplicationsd8e/ai-powered-application-security https://sites.google.com/view/howtouseaiinapplicationsd8e/gen-ai-in-cybersecurity What is agentic AI, and how does it differ from traditional AI in cybersecurity? refers to autonomous, goal-oriented systems that can perceive their environment, make decisions, and take actions to achieve specific objectives. Unlike traditional AI, which is often rule-based or reactive, agentic AI systems can learn, adapt, and operate with a degree of independence. In cybersecurity, agentic AI enables continuous monitoring, real-time threat detection, and proactive response capabilities. What are some examples of real-world agentic AI in cybersecurity? Examples of agentic AI in cybersecurity include: Autonomous threat detection and response platforms that continuously monitor networks and endpoints for malicious activity AI-powered vulnerability scanners that identify and prioritize security flaws in applications and infrastructure Intelligent threat intelligence systems gather data from multiple sources and analyze it to provide proactive protection against emerging threats Automated incident response tools can mitigate and contain cyber attacks without the need for human intervention AI-driven fraud detection solutions that identify and prevent fraudulent activities in real-time How can agentic AI help bridge the skills gap in cybersecurity and alleviate the burden on security teams? Agentic AI helps to address the cybersecurity skills gaps by automating repetitive and time-consuming security tasks currently handled manually. By taking on tasks such as continuous monitoring, threat detection, vulnerability scanning, and incident response, agentic AI systems can free up human experts to focus on more strategic and com