Agentic AI Frequently Asked Questions

Author : Salomonsen Adler | Published On : 18 Apr 2025

What is agentic AI, and how does it differ from traditional AI in cybersecurity? Agentic AI refers to autonomous, goal-oriented systems that can perceive their environment, make decisions, and take actions to achieve specific objectives. Unlike traditional AI, 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.
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 monitor code repositories continuously, analyze commits to find vulnerabilities, and use advanced techniques such as static code analysis and dynamic testing. 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 important for agentic AI in AppSec? A code property graph is a rich representation that shows the relationships between code elements such as variables, functions 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 enables the AI to make more accurate and relevant security decisions, prioritize vulnerabilities effectively, and 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 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 are some potential challenges and risks associated with the adoption of agentic AI in cybersecurity? Some of the potential risks and challenges 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
Ethics and social implications of autonomous systems
Integrating AI agentic into existing security tools
How can organizations ensure that autonomous AI agents are trustworthy and accountable in cybersecurity? By establishing clear guidelines, organizations can establish mechanisms to ensure accountability and trustworthiness of AI agents. 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://output.jsbin.com/lizidotali/ , continuous monitoring, and explainable AI techniques can also help build trust in the decision-making processes of autonomous agents. 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
Ensuring data privacy and security during 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 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. What role does machine-learning play in agentic AI? 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 are used to power many aspects of agentic AI including threat detection and prioritization. They also automate the fixing of vulnerabilities. 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 can continuously scan codebases, identify vulnerabilities, and prioritize them based on their real-world impact and 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.