https://www.lastwatchdog.com/rsac-fireside-chat-qwiet-ai-leverages-graph-database-technology-to-reduce-appsec-noise/ https://finance.yahoo.com/news/qwiet-ai-takes-giant-step-120000488.html https://www.linkedin.com/posts/qwiet_gartner-appsec-qwietai-activity-7203450652671258625-Nrz0 https://www.youtube.com/watch?v=vMRpNaavElg https://www.linkedin.com/posts/qwiet_appsec-webinar-agenticai-activity-7269760682881945603-qp3J https://www.gartner.com/reviews/market/application-security-testing/vendor/qwiet-ai/product/prezero/review/view/5285186 Agentic AI is a term used to describe autonomous, goal-oriented, systems that are able to perceive their environment, take decisions, and act to achieve specific goals. Unlike , 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: Platforms that automatically detect and respond to malicious threats and continuously monitor endpoints and networks. -powered vulnerability scans that prioritize and identify security flaws within applications and infrastructure Intelligent threat intelligence systems that gather and analyze data from multiple sources to provide proactive defense against emerging threats Autonomous incident response tools that can contain and mitigate cyber attacks without human intervention AI-driven fraud detection solutions that identify and prevent fraudulent activities in real-time How can agentic AI bridge the cybersecurity skills gap and ease the burden on security team? Agentic AI can help address the cybersecurity skills gap by automating many of the repetitive and time-consuming tasks that security professionals currently handle manually. By taking on tasks such as continuous monitoring, threat detection, vulnera