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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. Agentic AI is a more flexible and adaptive version of traditional AI. 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 has the potential to revolutionize AppSec by integrating intelligent agents within 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 allows the AI to make better security decisions and prioritize vulnerabilities. ai security orchestration can also generate targeted fixes. How does AI-powered automatic vulnerability fixing work, and what are its benefits? https://www.linkedin.com/posts/qwiet_find-fix-fast-these-are-the-three-words-activity-7191104011331100672-Yq4w -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 approach significantly reduces the time between vulnerability discovery and remediation, alleviates the burden on development teams, and ensures a consistent and reliable approach to vulnerability remediation. What potential risks and challenges are associated with the use of agentic AI for cybersecurity? Some potential challenges and risks include: Ensuring trust and accountability in autonomous AI decision-making AI protection against data manipulation and adversarial attacks Building and maintaining accurate and up-to-date code property graphs Addressing ethical and societal implications of autonomous systems Integrating AI agentic into existing security tools How can organizations ensure the trustworthiness and accountability of autonomous AI agents in cybersecurity? Organizations can ensure the trustworthiness and accountability of agentic AI by establishing clear guidelines and oversight mechanisms. This includes implementing robust testing and validation processes to verify the correctness and safety of AI-generated fixes, maintaining human oversight and intervention capabilities, and fostering a culture of transparency and responsible AI development. https://www.linkedin.com/posts/chrishatter_finding-vulnerabilities-with-enough-context-activity-7191189441196011521-a8XL and continuous monitoring can help to build trust in autonomous agents' decision-making processes. What are the best practices to develop and deploy secure agentic AI? The following are some of the best practices for developing secure AI systems: Adopting secure coding practices and following security guidelines throughout the AI development lifecycle Implementing adversarial training and model hardening techniques to protect against attacks Ensure data privacy and security when AI training and deployment Conducting thorough testing and validation of AI models and generated outputs Maintaining transparency and accountability in AI decision-making processes AI systems should be regularly updated and monitored to ensure they are able to adapt to new threats and vulnerabilities. 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 are able to analyze large amounts of data in real time, identifying attack patterns, vulnerabilities and anomalies which might be evading 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. Machine learning is a critical component of agentic AI in cybersecurity. It allows autonomous agents to identify patterns and correlate data and make intelligent decisions using that information. agentic ai vulnerability fixes learning algorithms are used to power many aspects of agentic AI including threat detection and prioritization. They also automate the fixing of vulnerabilities. By continuously learning and adapting, machine learning helps agentic AI systems improve their accuracy, efficiency, and effectiveness over time. 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. They can also generate context-aware fixes automatically, reducing the time and effort required for manual remediation. Agentic AI allows security teams to respond to threats more effectively and quickly by providing actionable insights in real time. Monitor and audit AI systems regularly to identify any potential biases or errors. Make necessary adjustments for optimal performance.