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This is a short outline of the subject: Artificial intelligence (AI) which is part of the continually evolving field of cyber security it is now being utilized by businesses to improve their defenses. As security threats grow increasingly complex, security professionals have a tendency to turn towards AI. AI was a staple of cybersecurity for a long time. been used in cybersecurity is being reinvented into agentsic AI which provides proactive, adaptive and fully aware security. This article delves into the transformative potential of agentic AI, focusing specifically on its use in applications security (AppSec) and the pioneering concept of artificial intelligence-powered automated security fixing. The Rise of Agentic AI in Cybersecurity Agentic AI is a term which refers to goal-oriented autonomous robots that are able to discern their surroundings, and take decision-making and take actions in order to reach specific targets. Agentic AI is different from conventional reactive or rule-based AI in that it can be able to learn and adjust to the environment it is in, as well as operate independently. This autonomy is translated into AI agents working in cybersecurity. They have the ability to constantly monitor the networks and spot abnormalities. They are also able to respond in with speed and accuracy to attacks without human interference. The power of AI agentic in cybersecurity is vast. By leveraging machine learning algorithms as well as vast quantities of information, these smart agents are able to identify patterns and correlations which human analysts may miss. Intelligent agents are able to sort through the chaos generated by many security events prioritizing the most important and providing insights to help with rapid responses. Agentic AI systems are able to grow and develop the ability of their systems to identify security threats and adapting themselves to cybercriminals' ever-changing strategies. Agentic AI and Application Security Though agentic AI offers a wide range of application in various areas of cybersecurity, its impact on security for applications is notable. Secure applications are a top priority for organizations that rely increasingly on highly interconnected and complex software technology. Conventional AppSec approaches, such as manual code reviews, as well as periodic vulnerability checks, are often unable to keep pace with rapid development cycles and ever-expanding threat surface that modern software applications. The answer is Agentic AI. By integrating intelligent agents into the software development lifecycle (SDLC), organizations can transform their AppSec methods from reactive to proactive. AI-powered agents can keep track of the repositories for code, and evaluate each change for possible security vulnerabilities. These AI-powered agents are able to use sophisticated techniques like static analysis of code and dynamic testing to find numerous issues that range from simple code errors to more subtle flaws in injection. What separates agentic AI different from the AppSec field is its capability to recognize and adapt to the specific context of each application. Through the creation of a complete code property graph (CPG) which is a detailed description of the codebase that captures relationships between various parts of the code – agentic AI is able to gain a thorough understanding of the application's structure in terms of data flows, its structure, and possible attacks. The AI can identify weaknesses based on their effect in real life and what they might be able to do and not relying on a general severity rating. Artificial Intelligence Powers Automatic Fixing The concept of automatically fixing security vulnerabilities could be one of the greatest applications for AI agent within AppSec. In the past, when a security flaw is identified, it falls on the human developer to go through the code, figure out the flaw, and then apply an appropriate fix. This can take a lengthy time, can be prone to error and hinder the release of crucial security patches. With agentic AI, the game is changed. By leveraging the deep knowledge of the base code provided through the CPG, AI agents can not only identify vulnerabilities as well as generate context-aware not-breaking solutions automatically. They can analyze the code that is causing the issue to determine its purpose and create a solution that corrects the flaw but making sure that they do not introduce new security issues. AI-powered automation of fixing can have profound effects. It can significantly reduce the time between vulnerability discovery and resolution, thereby closing the window of opportunity for attackers. It will ease the burden on development teams and allow them to concentrate in the development of new features rather than spending countless hours fixing security issues. Additionally, by automatizing fixing processes, organisations can ensure a consistent and trusted approach to vulnerability remediation, reducing the chance of human error and errors. What are the challenges as well as the importance of considerations? Although the possibilities of using agentic AI in the field of cybersecurity and AppSec is huge It is crucial to be aware of the risks and concerns that accompany the adoption of this technology. Accountability and trust is a key one. As AI agents grow more autonomous and capable taking decisions and making actions in their own way, organisations need to 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 testing and validation processes to verify the correctness and safety of AI-generated solutions. The other issue is the threat of an the possibility of an adversarial attack on AI. Attackers may try to manipulate the data, or exploit AI model weaknesses as agents of AI techniques are more widespread for cyber security. It is crucial to implement secured AI methods like adversarial learning as well as model hardening. The quality and completeness the diagram of code properties can be a significant factor to the effectiveness of AppSec's agentic AI. To construct and maintain an precise CPG the organization will have to spend money on instruments like static analysis, test frameworks, as well as pipelines for integration. The organizations must also make sure that they ensure that their CPGs keep on being updated regularly to take into account changes in the codebase and evolving threat landscapes. Cybersecurity: The future of AI-agents The future of agentic artificial intelligence in cybersecurity appears optimistic, despite its many obstacles. As AI techniques continue to evolve it is possible to be able to see more advanced and efficient autonomous agents that are able to detect, respond to, and combat cybersecurity threats at a rapid pace and accuracy. For AppSec agents, AI-based agentic security has the potential to change the way we build and secure software, enabling organizations to deliver more robust, resilient, and secure applications. Additionally, the integration of agentic AI into the cybersecurity landscape can open up new possibilities for collaboration and coordination between different security processes and tools. Imagine a future where autonomous agents collaborate seamlessly through network monitoring, event reaction, threat intelligence and vulnerability management. Sharing insights as well as coordinating their actions to create an all-encompassing, proactive defense from cyberattacks. https://en.wikipedia.org/wiki/Large_language_model is important that organizations embrace agentic AI as we develop, and be mindful of its moral and social consequences. If we can foster a culture of accountability, responsible AI advancement, transparency and accountability, it is possible to use the power of AI for a more solid and safe digital future. The conclusion of the article will be: In the fast-changing world of cybersecurity, agentic AI is a fundamental change in the way we think about the detection, prevention, and mitigation of cyber security threats. Through the use of autonomous AI, particularly when it comes to app security, and automated fix for vulnerabilities, companies can change their security strategy by shifting from reactive to proactive, moving from manual to automated as well as from general to context aware. Agentic AI presents many issues, but the benefits are too great to ignore. As we continue pushing the limits of AI in the field of cybersecurity the need to take this technology into consideration with an attitude of continual learning, adaptation, and responsible innovation. It is then possible to unleash the power of artificial intelligence to protect digital assets and organizations.