https://www.linkedin.com/posts/eric-six_agentic-ai-in-appsec-its-more-then-media-activity-7269764746663354369-ENtd https://en.wikipedia.org/wiki/Machine_learning What is agentic AI and how does this differ from the traditional AI used in cybersecurity? 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 traditional AI, which is often rule-based or reactive, agentic AI systems can learn, adapt, and operate with a degree of independence. Agentic AI is a powerful tool for cybersecurity. It allows continuous monitoring, real time threat detection and proactive response. How can agentic AI improve application security (AppSec?) practices? Agentic AI can revolutionize AppSec practices by integrating intelligent agents into the software development lifecycle (SDLC). These agents can continuously monitor code repositories, analyze commits for vulnerabilities, and leverage advanced techniques like static code analysis, dynamic testing, and machine learning to identify a wide range of security issues. Agentic AI prioritizes vulnerabilities according to their impact in the real world and exploitability. This provides contextually aware insights into remediation. What is a code-property graph (CPG) and why is it so important for agentic artificial intelligence in AppSec. A code property graph is a rich representation that shows the relationships between code elements such as variables, functions and data flows. Agentic AI can gain a deeper understanding of the application's structure and security posture by building a comprehensive CPG. This contextual awareness enables the AI to make more accurate and relevant security decisions, prioritize vulnerabilities effectively, and generate targeted fixes. AI-powered automatic vulnerabilities fixing uses the CPG's deep understanding of the codebase to identify vulnerabilities and generate context-aware fixes that