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Introduction In the ever-evolving landscape of cybersecurity, where threats get more sophisticated day by day, organizations are using artificial intelligence (AI) to strengthen their defenses. AI, which has long been a part of cybersecurity is being reinvented into agentsic AI that provides flexible, responsive and context aware security. This article focuses on the revolutionary potential of AI by focusing on its application in the field of application security (AppSec) and the groundbreaking idea of automated security fixing. The rise of Agentic AI in Cybersecurity Agentic AI is a term used to describe autonomous, goal-oriented systems that understand their environment to make decisions and make decisions to accomplish certain goals. Contrary to conventional rule-based, reactive AI, these systems possess the ability to develop, change, and operate in a state that is independent. The autonomy they possess is displayed in AI agents for cybersecurity who can continuously monitor the network and find abnormalities. They can also respond immediately to security threats, with no human intervention. The potential of agentic AI in cybersecurity is enormous. Through the use of machine learning algorithms as well as huge quantities of data, these intelligent agents can spot patterns and connections that analysts would miss. The intelligent AI systems can cut through the chaos generated by numerous security breaches by prioritizing the crucial and provide insights to help with rapid responses. Furthermore, agentsic AI systems can learn from each encounter, enhancing their threat detection capabilities and adapting to constantly changing tactics of cybercriminals. Agentic AI (Agentic AI) and Application Security Agentic AI is a powerful instrument that is used for a variety of aspects related to cyber security. However, the impact the tool has on security at an application level is particularly significant. agentic ai code security analysis of apps is paramount for businesses that are reliant increasing on interconnected, complex software platforms. AppSec techniques such as periodic vulnerability scanning as well as manual code reviews are often unable to keep up with current application design cycles. Agentic AI can be the solution. Through the integration of intelligent agents in the software development lifecycle (SDLC), organizations can change their AppSec practices from reactive to proactive. The AI-powered agents will continuously check code repositories, and examine every code change for vulnerability or security weaknesses. The agents employ sophisticated methods such as static analysis of code and dynamic testing to identify numerous issues, from simple coding errors to subtle injection flaws. The thing that sets agentsic AI out in the AppSec field is its capability to understand and adapt to the particular circumstances of each app. With the help of a thorough code property graph (CPG) – a rich description of the codebase that can identify relationships between the various parts of the code – agentic AI has the ability to develop an extensive understanding of the application's structure in terms of data flows, its structure, as well as possible attack routes. The AI will be able to prioritize weaknesses based on their effect in the real world, and what they might be able to do and not relying on a general severity rating. Artificial Intelligence Powers Intelligent Fixing The idea of automating the fix for security vulnerabilities could be the most fascinating application of AI agent technology in AppSec. When a flaw has been discovered, it falls upon human developers to manually look over the code, determine the problem, then implement a fix. It could take a considerable period of time, and be prone to errors. It can also slow the implementation of important security patches. https://sites.google.com/view/howtouseaiinapplicationsd8e/gen-ai-in-cybersecurity has changed with agentsic AI. By leveraging the deep knowledge of the codebase offered by the CPG, AI agents can not only detect vulnerabilities, and create context-aware not-breaking solutions automatically. They will analyze the code around the vulnerability and understand the purpose of it and design a fix that fixes the flaw while creating no additional vulnerabilities. The implications of AI-powered automatic fixing are huge. It could significantly decrease the gap between vulnerability identification and repair, making it harder for hackers. This can relieve the development team from having to devote countless hours finding security vulnerabilities. The team are able to focus on developing new features. Automating the process of fixing vulnerabilities helps organizations make sure they're using a reliable method that is consistent that reduces the risk for human error and oversight. Problems and considerations The potential for agentic AI in cybersecurity as well as AppSec is huge however, it is vital to acknowledge the challenges as well as the considerations associated with its implementation. Accountability and trust is a crucial issue. When AI agents are more autonomous and capable of taking decisions and making actions independently, companies must establish clear guidelines and oversight mechanisms to ensure that AI is operating within the bounds of acceptable behavior. AI is operating within the boundaries of behavior that is acceptable. It is important to implement robust test and validation methods to confirm the accuracy and security of AI-generated changes. Another challenge lies in the threat of attacks against the AI itself. An attacker could try manipulating data or exploit AI models' weaknesses, as agents of AI techniques are more widespread within cyber security. This highlights the need for security-conscious AI methods of development, which include methods such as adversarial-based training and model hardening. In addition, the efficiency of the agentic AI in AppSec is dependent upon the integrity and reliability of the code property graph. In order to build and maintain an precise CPG You will have to purchase techniques like static analysis, test frameworks, as well as pipelines for integration. automated code fixes must also ensure that they are ensuring that their CPGs keep up with the constant changes which occur within codebases as well as shifting security environments. Cybersecurity: The future of agentic AI Despite all the obstacles, the future of agentic AI in cybersecurity looks incredibly hopeful. As AI techniques continue to evolve and become more advanced, we could be able to see more advanced and efficient autonomous agents that can detect, respond to, and combat cyber threats with unprecedented speed and precision. Agentic AI in AppSec is able to alter the method by which software is developed and protected, giving organizations the opportunity to build more resilient and secure applications. Furthermore, the incorporation of AI-based agent systems into the cybersecurity landscape opens up exciting possibilities of collaboration and coordination between various security tools and processes. Imagine a scenario where the agents work autonomously throughout network monitoring and response, as well as threat intelligence and vulnerability management. They could share information to coordinate actions, as well as help to provide a proactive defense against cyberattacks. As we progress we must encourage businesses to be open to the possibilities of AI agent while cognizant of the ethical and societal implications of autonomous systems. We can use the power of AI agents to build security, resilience as well as reliable digital future by creating a responsible and ethical culture in AI advancement. The final sentence of the article can be summarized as: Agentic AI is a significant advancement within the realm of cybersecurity. It represents a new paradigm for the way we identify, stop cybersecurity threats, and limit their effects. Utilizing the potential of autonomous AI, particularly in the area of applications security and automated patching vulnerabilities, companies are able to shift their security strategies by shifting from reactive to proactive, moving from manual to automated and from generic to contextually aware. Agentic AI faces many obstacles, but the benefits are sufficient to not overlook. In the process of pushing the boundaries of AI in the field of cybersecurity the need to consider this technology with the mindset of constant training, adapting and sustainable innovation. This way we will be able to unlock the full potential of AI-assisted security to protect our digital assets, protect the organizations we work for, and provide a more secure future for everyone.