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. <a href="https://www.linkedin.com/posts/qwiet_qwiet-ai-webinar-series-ai-autofix-the-activity-7202016247830491136-ax4v">measuring ai security</a> is a more flexible and adaptive version of traditional AI. In cybersecurity, agentic AI enables continuous monitoring, real-time threat detection, and proactive response capabilities.<br /><br />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 can also prioritize vulnerabilities based on their real-world impact and exploitability, providing contextually aware insights for remediation. What is a code-property graph (CPG) and why is it so important for agentic artificial intelligence in AppSec. A code property graph (CPG) is a rich representation of a codebase that captures relationships between various code elements, such as functions, variables, 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 allows the AI to make better security decisions and prioritize vulnerabilities. It can also generate targeted fixes. What are the benefits of AI-powered automatic vulnerabilities fixing? AI-powered automatic vulnerability fixing leverages the deep understanding of a codebase provided by the CPG to not only identify vulnerabilities but also generate context-aware, non-breaking fixes automatically. The AI analyses the code around the vulnerability to understand the intended functionality and then creates a fix without breaking existing features or introducing any new bugs. This method reduces the amount of time it takes to discover a vulnerability and fix it. It also relieves development teams and provides a reliable and consistent approach to fixing vulnerabilities. Some of the potential risks and challenges include:<br /><br />Ensure trust and accountability for autonomous AI decisions<br /><br />Protecting AI systems against adversarial attacks and data manipulation<br /><br />Building and maintaining accurate and up-to-date code property graphs<br /><br />Ethics and social implications of autonomous systems<br /><br />Integrating AI agentic into existing security tools<br /><br />By establishing clear guidelines, organizations can establish mechanisms to ensure accountability and trustworthiness of AI agents. It is important to implement robust testing and validating processes in order to ensure the safety and correctness of AI-generated fixes. Also, it's essential that humans are able intervene and maintain oversight. Regular audits, continuous monitoring, and explainable AI techniques can also help build trust in the decision-making processes of autonomous agents. What are <a href="https://www.linkedin.com/posts/qwiet_qwiet-ai-webinar-series-ai-autofix-the-activity-7202016247830491136-ax4v">https://www.linkedin.com/posts/qwiet_qwiet-ai-webinar-series-ai-autofix-the-activity-7202016247830491136-ax4v</a> to develop and deploy secure agentic AI? Best practices for secure agentic AI development include:<br /><br />Adopting secure coding practices and following security guidelines throughout the AI development lifecycle<br /><br />Protect against attacks by implementing adversarial training techniques and model hardening.<br /><br />Ensure data privacy and security when AI training and deployment<br /><br />Conducting thorough testing and validation of AI models and generated outputs<br /><br />Maintaining transparency in AI decision making processes<br /><br />AI systems should be regularly updated and monitored to ensure they are able to adapt to new threats and vulnerabilities.<br /><br />How can agentic AI help organizations keep pace with the rapidly evolving threat landscape? By continuously monitoring data, networks, and applications for new threats, agentic AI can assist organizations in keeping up with the rapidly changing threat landscape. These autonomous agents can analyze vast amounts of security data in real-time, identifying new attack patterns, vulnerabilities, and anomalies that might evade traditional security controls. By learning from each interaction and adapting their threat detection models, agentic AI systems can provide proactive defense against evolving cyber threats, enabling organizations to respond quickly and effectively. Agentic AI is not complete without machine learning. It allows autonomous agents to identify patterns and correlate data and make intelligent decisions using that information. Machine learning algorithms power various aspects of agentic AI, including threat detection, vulnerability prioritization, and automatic fixing. By continuously learning and adapting, machine learning helps agentic AI systems improve their accuracy, efficiency, and effectiveness over time. How can agentic AI increase the efficiency and effectiveness in vulnerability management processes. Agentic AI can streamline vulnerability management processes by automating many of the time-consuming and labor-intensive tasks involved. Autonomous agents are able to continuously scan codebases and identify vulnerabilities. They can then prioritize these vulnerabilities based on the real-world impact of each vulnerability and their exploitability. The agents can generate context-aware solutions automatically, which reduces the amount of time and effort needed for manual remediation. By providing real-time insights and actionable recommendations, agentic AI enables security teams to focus on high-priority issues and respond more quickly and effectively to potential threats. Monitor and audit AI systems regularly to identify any potential biases or errors. Make necessary adjustments for optimal performance.<br /><br />