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Introduction Artificial intelligence (AI) which is part of the constantly evolving landscape of cyber security has been utilized by organizations to strengthen their defenses. As security threats grow more complex, they have a tendency to turn towards AI. AI has for years been part of cybersecurity, is currently being redefined to be agentic AI that provides active, adaptable and context aware security. The article explores the possibility for agentic AI to transform security, specifically focusing on the use cases of AppSec and AI-powered automated vulnerability fix. The rise of Agentic AI in Cybersecurity Agentic AI can be applied to autonomous, goal-oriented robots that can see their surroundings, make the right decisions, and execute actions to achieve specific desired goals. As opposed to the traditional rules-based or reacting AI, agentic systems are able to develop, change, and operate with a degree of autonomy. For cybersecurity, the autonomy can translate into AI agents that can constantly monitor networks, spot abnormalities, and react to security threats immediately, with no continuous human intervention. The application of AI agents in cybersecurity is vast. Intelligent agents are able to recognize patterns and correlatives using machine learning algorithms and huge amounts of information. Intelligent agents are able to sort through the noise of several security-related incidents, prioritizing those that are most significant and offering information for rapid response. Agentic AI systems can be trained to develop and enhance the ability of their systems to identify dangers, and changing their strategies to match cybercriminals changing strategies. agentic ai secure development platform and Application Security Agentic AI is a broad field of applications across various aspects of cybersecurity, its effect on the security of applications is notable. Secure applications are a top priority for companies that depend ever more heavily on complex, interconnected software technology. Standard AppSec strategies, including manual code reviews and periodic vulnerability tests, struggle to keep pace with the rapid development cycles and ever-expanding security risks of the latest applications. Enter agentic AI. Integrating intelligent agents in software development lifecycle (SDLC) businesses could transform their AppSec approach from proactive to. Artificial Intelligence-powered agents continuously examine code repositories and analyze every code change for vulnerability or security weaknesses. They may employ advanced methods like static code analysis, dynamic testing, as well as machine learning to find various issues such as common code mistakes to subtle injection vulnerabilities. What makes agentic AI apart in the AppSec field is its capability to comprehend and adjust to the distinct situation of every app. Through the creation of a complete Code Property Graph (CPG) that is a comprehensive representation of the codebase that is able to identify the connections between different code elements – agentic AI will gain an in-depth grasp of the app's structure, data flows, as well as possible attack routes. The AI can prioritize the security vulnerabilities based on the impact they have in the real world, and how they could be exploited in lieu of basing its decision on a standard severity score. Artificial Intelligence and Autonomous Fixing The idea of automating the fix for vulnerabilities is perhaps the most fascinating application of AI agent technology in AppSec. In the past, when a security flaw has been discovered, it falls upon human developers to manually review the code, understand the flaw, and then apply the corrective measures. The process is time-consuming in addition to error-prone and frequently causes delays in the deployment of critical security patches. The agentic AI situation is different. By leveraging the deep knowledge of the base code provided by CPG, AI agents can not only identify vulnerabilities as well as generate context-aware automatic fixes that are not breaking. They can analyze the code around the vulnerability in order to comprehend its function and create a solution that fixes the flaw while not introducing any new problems. The AI-powered automatic fixing process has significant implications. It is able to significantly reduce the gap between vulnerability identification and remediation, eliminating the opportunities for attackers. It can alleviate the burden on the development team so that they can concentrate on creating new features instead of wasting hours trying to fix security flaws. Furthermore, through automatizing fixing processes, organisations can ensure a consistent and reliable method of vulnerabilities remediation, which reduces the risk of human errors and oversights. What are the challenges and issues to be considered? It is crucial to be aware of the dangers and difficulties associated with the use of AI agentics in AppSec and cybersecurity. A major concern is the question of trust and accountability. As ai security assessment platform grow more independent and are capable of acting and making decisions by themselves, businesses should establish clear rules as well as oversight systems to make sure that the AI performs within the limits of behavior that is acceptable. This includes the implementation of robust tests and validation procedures to verify the correctness and safety of AI-generated fix. Another issue is the risk of attackers against the AI model itself. As agentic AI systems become more prevalent in the world of cybersecurity, adversaries could be looking to exploit vulnerabilities within the AI models, or alter the data on which they are trained. It is important to use secure AI methods like adversarial learning as well as model hardening. The completeness and accuracy of the property diagram for code is also an important factor in the performance of AppSec's agentic AI. Making and maintaining an accurate CPG involves a large expenditure in static analysis tools and frameworks for dynamic testing, and pipelines for data integration. Organizations must also ensure that their CPGs reflect the changes that take place in their codebases, as well as shifting security environment. Cybersecurity: The future of artificial intelligence Despite all the obstacles however, the future of cyber security AI is exciting. As automated security fixes continues to improve it is possible to be able to see more advanced and efficient autonomous agents which can recognize, react to and counter cyber-attacks with a dazzling speed and accuracy. Within the field of AppSec the agentic AI technology has the potential to revolutionize how we create and secure software. This could allow companies to create more secure reliable, secure, and resilient software. Furthermore, the incorporation of agentic AI into the wider cybersecurity ecosystem offers exciting opportunities to collaborate and coordinate the various tools and procedures used in security. Imagine a scenario where autonomous agents are able to work in tandem across network monitoring, incident reaction, threat intelligence and vulnerability management. They share insights as well as coordinating their actions to create a comprehensive, proactive protection against cyber threats. Moving forward, it is crucial for organizations to embrace the potential of autonomous AI, while being mindful of the moral implications and social consequences of autonomous system. By fostering a culture of accountable AI development, transparency and accountability, we can use the power of AI to create a more safe and robust digital future. Conclusion In the fast-changing world of cybersecurity, the advent of agentic AI will be a major shift in how we approach the identification, prevention and mitigation of cyber security threats. Through the use of autonomous AI, particularly in the realm of application security and automatic patching vulnerabilities, companies are able to transform their security posture by shifting from reactive to proactive, from manual to automated, and move from a generic approach to being contextually cognizant. There are many challenges ahead, but the potential benefits of agentic AI are too significant to not consider. As we continue to push the boundaries of AI in cybersecurity, it is crucial to remain in a state to keep learning and adapting and wise innovations. In this way it will allow us to tap into the full power of AI agentic to secure our digital assets, protect the organizations we work for, and provide better security for all.