FAQs about Agentic AI

What is  https://swisschin63.bloggersdelight.dk/2025/05/05/agentic-artificial-intelligence-frequently-asked-questions-20/ , and how does it differ from traditional AI 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. In cybersecurity, agentic AI enables continuous monitoring, real-time threat detection, and proactive response capabilities.
How can agentic AI enhance 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 can also prioritize vulnerabilities based on their real-world impact and exploitability, providing contextually aware insights for remediation.  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. By building a comprehensive CPG, agentic AI can develop a deep understanding of an application's structure, potential attack paths, and security posture. This contextual awareness allows the AI to make better security decisions and prioritize vulnerabilities. It can also generate targeted fixes. How does AI-powered automatic vulnerability fixing work, and what are its benefits? AI-powered automatic vulnerabilities fixing uses the CPG's deep understanding of the codebase to identify vulnerabilities and generate context-aware fixes that do not break existing features. The AI analyzes the code surrounding the vulnerability, understands the intended functionality, and crafts a fix that addresses the security flaw without introducing new bugs or breaking existing features. 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:

Ensure trust and accountability for autonomous AI decisions
Protecting AI systems against adversarial attacks and data manipulation
Building and maintaining accurate and up-to-date code property graphs
Addressing ethical and societal implications of autonomous systems
Integrating agentic AI into existing security tools and processes
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 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? Best practices for secure agentic AI development include:

Adopting safe coding practices throughout the AI life cycle and following security guidelines
Implementing adversarial training and model hardening techniques to protect against attacks
Ensure data privacy and security when AI training and deployment
Validating AI models and their outputs through thorough testing
Maintaining transparency in AI decision making processes
Regularly monitoring and updating AI systems to adapt to evolving threats and vulnerabilities
How can agentic AI help organizations keep pace with the rapidly evolving threat landscape? Agentic AI can help organizations stay ahead of the ever-changing threat landscape by continuously monitoring networks, applications, and data for emerging threats. 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. Agentic AI systems provide proactive defenses against evolving cyber-threats by adapting their detection models and learning from every interaction. What role does machine-learning play in agentic AI? 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. Machine learning algorithms power various aspects of agentic AI, including threat detection, vulnerability prioritization, and automatic fixing. Machine learning improves agentic AI's accuracy, efficiency and effectiveness by continuously learning and adjusting. How can agentic AI increase the efficiency and effectiveness in vulnerability management processes. Agentic AI automates many of the laborious and time-consuming tasks that are involved in vulnerability management. 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.