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Introduction In the constantly evolving world of cybersecurity, in which threats get more sophisticated day by day, businesses are turning to artificial intelligence (AI) for bolstering their security. Although AI has been a part of cybersecurity tools since a long time, the emergence of agentic AI can signal a revolution in proactive, adaptive, and contextually sensitive security solutions. This article focuses on the revolutionary potential of AI by focusing on its applications in application security (AppSec) as well as the revolutionary concept of artificial intelligence-powered automated security fixing. Cybersecurity: The rise of artificial intelligence (AI) that is agent-based Agentic AI refers to intelligent, goal-oriented and autonomous systems that are able to perceive their surroundings take decisions, decide, and implement actions in order to reach specific objectives. Agentic AI is different in comparison to traditional reactive or rule-based AI as it can adjust and learn to changes in its environment and also operate on its own. In the context of cybersecurity, the autonomy translates into AI agents that continually monitor networks, identify anomalies, and respond to dangers in real time, without the need for constant human intervention. The power of AI agentic in cybersecurity is enormous. By leveraging machine learning algorithms and vast amounts of data, these intelligent agents can identify patterns and correlations that human analysts might miss. They can sift through the chaos generated by a multitude of security incidents prioritizing the most significant and offering information for quick responses. Agentic AI systems are able to improve and learn the ability of their systems to identify dangers, and being able to adapt themselves to cybercriminals and their ever-changing tactics. Agentic AI as well as Application Security Agentic AI is a broad field of applications across various aspects of cybersecurity, its effect in the area of application security is noteworthy. As organizations increasingly rely on highly interconnected and complex systems of software, the security of their applications is the top concern. AppSec methods like periodic vulnerability scans as well as manual code reviews tend to be ineffective at keeping up with rapid developments. Agentic AI is the new frontier. Incorporating intelligent agents into the software development cycle (SDLC) companies could transform their AppSec practices from reactive to proactive. AI-powered agents can continuously monitor code repositories and examine each commit to find potential security flaws. They employ sophisticated methods like static code analysis, test-driven testing and machine-learning to detect a wide range of issues including common mistakes in coding to little-known injection flaws. What sets agentic AI distinct from other AIs in the AppSec area is its capacity to recognize and adapt to the specific situation of every app. Agentic AI is able to develop an understanding of the application's structures, data flow as well as attack routes by creating an exhaustive CPG (code property graph) an elaborate representation that reveals the relationship between the code components. This contextual awareness allows the AI to determine the most vulnerable weaknesses based on their actual vulnerability and impact, instead of relying on general severity scores. AI-powered Automated Fixing: The Power of AI The idea of automating the fix for weaknesses is possibly the most fascinating application of AI agent technology in AppSec. Human developers have traditionally been required to manually review the code to identify vulnerabilities, comprehend it, and then implement the fix. This could take quite a long period of time, and be prone to errors. It can also delay the deployment of critical security patches. With agentic AI, the game has changed. By leveraging the deep knowledge of the codebase offered by the CPG, AI agents can not only detect vulnerabilities, and create context-aware and non-breaking fixes. They can analyse the code that is causing the issue and understand the purpose of it before implementing a solution that fixes the flaw while making sure that they do not introduce new vulnerabilities. The implications of AI-powered automatized fixing have a profound impact. It is able to significantly reduce the gap between vulnerability identification and resolution, thereby eliminating the opportunities for attackers. This will relieve the developers team of the need to devote countless hours solving security issues. In their place, the team are able to be able to concentrate on the development of fresh features. Automating the process of fixing security vulnerabilities helps organizations make sure they are using a reliable and consistent approach and reduces the possibility for oversight and human error. What are the main challenges and the considerations? It is important to recognize the threats and risks that accompany the adoption of AI agents in AppSec as well as cybersecurity. In the area of accountability and trust is a key issue. As AI agents become more autonomous and capable making decisions and taking action on their own, organizations must establish clear guidelines and control mechanisms that ensure that AI is operating within the bounds of acceptable behavior. AI performs within the limits of behavior that is acceptable. https://sites.google.com/view/howtouseaiinapplicationsd8e/gen-ai-in-cybersecurity includes the implementation of robust testing and validation processes to verify the correctness and safety of AI-generated fixes. Another issue is the possibility of adversarial attack against AI. Attackers may try to manipulate data or make use of AI model weaknesses since agentic AI systems are more common in cyber security. It is important to use secured AI practices such as adversarial-learning and model hardening. Quality and comprehensiveness of the CPG's code property diagram is also an important factor in the success of AppSec's agentic AI. Building and maintaining an reliable CPG will require a substantial spending on static analysis tools and frameworks for dynamic testing, as well as data integration pipelines. Businesses also must ensure they are ensuring that their CPGs keep up with the constant changes that occur in codebases and shifting threat environment. Cybersecurity Future of AI agentic Despite the challenges, the future of agentic AI for cybersecurity is incredibly hopeful. As AI advances in the near future, we will get even more sophisticated and resilient autonomous agents capable of detecting, responding to, and combat cybersecurity threats at a rapid pace and precision. Agentic AI within AppSec can alter the method by which software is created and secured and gives organizations the chance to create more robust and secure software. Moreover, the integration of AI-based agent systems into the cybersecurity landscape offers exciting opportunities of collaboration and coordination between the various tools and procedures used in security. Imagine a scenario where the agents work autonomously in the areas of network monitoring, incident response, as well as threat security and intelligence. They'd share knowledge, coordinate actions, and offer proactive cybersecurity. It is vital that organisations take on agentic AI as we move forward, yet remain aware of the ethical and social impacts. Through fostering a culture that promotes ethical AI creation, transparency and accountability, we can make the most of the potential of agentic AI in order to construct a robust and secure digital future. The final sentence of the article will be: With the rapid evolution of cybersecurity, agentsic AI will be a major transformation in the approach we take to the detection, prevention, and elimination of cyber risks. The capabilities of an autonomous agent, especially in the area of automatic vulnerability fix and application security, could enable organizations to transform their security posture, moving from being reactive to an proactive one, automating processes moving from a generic approach to contextually-aware. Agentic AI is not without its challenges but the benefits are far sufficient to not overlook. In the process of pushing the limits of AI in the field of cybersecurity, it is essential to approach this technology with an attitude of continual adapting, learning and sustainable innovation. This will allow us to unlock the power of artificial intelligence to secure companies and digital assets.