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Introduction Artificial Intelligence (AI) which is part of the continuously evolving world of cybersecurity it is now being utilized by businesses to improve their defenses. As the threats get increasingly complex, security professionals have a tendency to turn to AI. AI, which has long been a part of cybersecurity is currently being redefined to be agentic AI which provides flexible, responsive and context-aware security. This article explores the transformative potential of agentic AI with a focus on the applications it can have in application security (AppSec) as well as the revolutionary concept of artificial intelligence-powered automated vulnerability fixing. The rise of Agentic AI in Cybersecurity Agentic AI relates to intelligent, goal-oriented and autonomous systems that recognize their environment as well as make choices and implement actions in order to reach the goals they have set for themselves. Agentic AI is distinct from traditional reactive or rule-based AI because it is able to adjust and learn to changes in its environment and can operate without. For cybersecurity, the autonomy transforms into AI agents who continuously monitor networks, detect abnormalities, and react to dangers in real time, without the need for constant human intervention. The application of AI agents in cybersecurity is vast. Intelligent agents are able to identify patterns and correlates through machine-learning algorithms and huge amounts of information. Intelligent agents are able to sort out the noise created by numerous security breaches prioritizing the crucial and provide insights to help with rapid responses. Agentic AI systems are able to learn from every interactions, developing their detection of threats and adapting to the ever-changing tactics of cybercriminals. Agentic AI as well as Application Security Though agentic AI offers a wide range of application across a variety of aspects of cybersecurity, its effect on application security is particularly important. The security of apps is paramount for organizations that rely more and more on interconnected, complicated software platforms. AppSec techniques such as periodic vulnerability analysis and manual code review can often not keep up with modern application cycle of development. Agentic AI is the answer. Incorporating intelligent agents into the lifecycle of software development (SDLC) businesses are able to transform their AppSec procedures from reactive proactive. AI-powered systems can keep track of the repositories for code, and analyze each commit for vulnerabilities in security that could be exploited. They can employ advanced techniques such as static analysis of code and dynamic testing to detect numerous issues including simple code mistakes or subtle injection flaws. AI is a unique feature of AppSec because it can be used to understand the context AI is unique in AppSec because it can adapt to the specific context of each and every application. With the help of a thorough code property graph (CPG) which is a detailed description of the codebase that shows the relationships among various code elements – agentic AI is able to gain a thorough knowledge of the structure of the application in terms of data flows, its structure, as well as possible attack routes. The AI will be able to prioritize vulnerabilities according to their impact in real life and ways to exploit them in lieu of basing its decision on a standard severity score. https://sites.google.com/view/howtouseaiinapplicationsd8e/ai-in-application-security -powered Automated Fixing the Power of AI One of the greatest applications of agents in AI in AppSec is automated vulnerability fix. When a flaw is discovered, it's on humans to go through the code, figure out the issue, and implement a fix. It could take a considerable time, be error-prone and hold up the installation of vital security patches. With agentic AI, the game changes. AI agents are able to detect and repair vulnerabilities on their own through the use of CPG's vast expertise in the field of codebase. These intelligent agents can analyze the source code of the flaw as well as understand the functionality intended and design a solution that addresses the security flaw without creating new bugs or affecting existing functions. The implications of AI-powered automatized fixing have a profound impact. The period between the moment of identifying a vulnerability before addressing the issue will be drastically reduced, closing a window of opportunity to attackers. It reduces the workload on developers, allowing them to focus on creating new features instead than spending countless hours solving security vulnerabilities. Furthermore, through automatizing the fixing process, organizations are able to guarantee a consistent and reliable approach to vulnerabilities remediation, which reduces the chance of human error and inaccuracy. What are the issues and the considerations? While the potential of agentic AI in the field of cybersecurity and AppSec is vast but it is important to acknowledge the challenges and issues that arise with its implementation. In the area of accountability and trust is a crucial one. When AI agents grow more self-sufficient and capable of making decisions and taking actions independently, companies need to establish clear guidelines and oversight mechanisms to ensure that AI is operating within the bounds of acceptable behavior. https://sites.google.com/view/howtouseaiinapplicationsd8e/gen-ai-in-appsec performs within the limits of behavior that is acceptable. This includes the implementation of robust test and validation methods to confirm the accuracy and security of AI-generated changes. Another concern is the potential for adversarial attacks against AI systems themselves. Since agent-based AI technology becomes more common within cybersecurity, cybercriminals could attempt to take advantage of weaknesses in the AI models, or alter the data from which they're based. This underscores the importance of secure AI practice in development, including methods like adversarial learning and the hardening of models. The quality and completeness the property diagram for code is a key element to the effectiveness of AppSec's AI. To construct and maintain an precise CPG the organization will have to spend money on techniques like static analysis, testing frameworks and pipelines for integration. Organisations also need to ensure their CPGs are updated to reflect changes occurring in the codebases and changing security environments. The future of Agentic AI in Cybersecurity The future of AI-based agentic intelligence in cybersecurity is extremely promising, despite the many problems. As AI technologies continue to advance it is possible to see even more sophisticated and resilient autonomous agents capable of detecting, responding to and counter cyber threats with unprecedented speed and accuracy. Agentic AI within AppSec is able to alter the method by which software is designed and developed and gives organizations the chance to create more robust and secure applications. In addition, the integration of agentic AI into the larger cybersecurity system opens up exciting possibilities to collaborate and coordinate diverse security processes and tools. Imagine a future where autonomous agents work seamlessly across network monitoring, incident reaction, threat intelligence and vulnerability management, sharing information and taking coordinated actions in order to offer a holistic, proactive defense from cyberattacks. It is essential that companies embrace agentic AI as we progress, while being aware of its social and ethical implications. It is possible to harness the power of AI agentics in order to construct security, resilience, and reliable digital future by creating a responsible and ethical culture that is committed to AI advancement. The final sentence of the article will be: Agentic AI is a breakthrough in the field of cybersecurity. It's a revolutionary method to discover, detect cybersecurity threats, and limit their effects. Utilizing the potential of autonomous agents, specifically in the realm of application security and automatic fix for vulnerabilities, companies can shift their security strategies in a proactive manner, shifting from manual to automatic, as well as from general to context conscious. Agentic AI is not without its challenges yet the rewards are sufficient to not overlook. While we push AI's boundaries in the field of cybersecurity, it's vital to be aware of continuous learning, adaptation of responsible and innovative ideas. Then, we can unlock the capabilities of agentic artificial intelligence to secure companies and digital assets.