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Introduction Artificial Intelligence (AI) which is part of the continuously evolving world of cyber security it is now being utilized by organizations to strengthen their security. As the threats get increasingly complex, security professionals are increasingly turning to AI. AI, which has long been a part of cybersecurity is currently being redefined to be agentic AI, which offers flexible, responsive and fully aware security. This article explores the transformative potential of agentic AI and focuses on the applications it can have in application security (AppSec) and the ground-breaking concept of AI-powered automatic vulnerability fixing. Cybersecurity The rise of Agentic AI Agentic AI refers specifically to self-contained, goal-oriented systems which recognize their environment, make decisions, and take actions to achieve the goals they have set for themselves. Agentic AI differs from conventional reactive or rule-based AI as it can adjust and learn to the environment it is in, and can operate without. When it comes to cybersecurity, this autonomy can translate into AI agents that can constantly monitor networks, spot abnormalities, and react to security threats immediately, with no continuous human intervention. Agentic AI's potential in cybersecurity is vast. With the help of machine-learning algorithms and vast amounts of data, these intelligent agents can spot patterns and connections which human analysts may miss. They can sift through the chaos generated by numerous security breaches, prioritizing those that are most important and providing insights for rapid response. Agentic AI systems can be trained to learn and improve their abilities to detect dangers, and changing their strategies to match cybercriminals changing strategies. Agentic AI as well as Application Security Agentic AI is an effective instrument that is used to enhance many aspects of cybersecurity. However, the impact it can have on the security of applications is noteworthy. Since organizations are increasingly dependent on highly interconnected and complex systems of software, the security of their applications is an absolute priority. measuring ai security like periodic vulnerability testing and manual code review are often unable to keep up with current application developments. Agentic AI is the new frontier. By integrating intelligent agents into the software development lifecycle (SDLC), organizations can change their AppSec procedures from reactive proactive. The AI-powered agents will continuously monitor code repositories, analyzing every commit for vulnerabilities and security flaws. These agents can use advanced methods like static code analysis as well as dynamic testing to find various issues that range from simple code errors to invisible injection flaws. Intelligent AI is unique in AppSec since it is able to adapt to the specific context of any application. Through the creation of a complete Code Property Graph (CPG) – – a thorough description of the codebase that shows the relationships among various elements of the codebase – an agentic AI is able to gain a thorough knowledge of the structure of the application in terms of data flows, its structure, and potential attack paths. This understanding of context allows the AI to identify weaknesses based on their actual potential impact and vulnerability, rather than relying on generic severity rating. AI-Powered Automatic Fixing AI-Powered Automatic Fixing Power of AI The notion of automatically repairing vulnerabilities is perhaps the most intriguing application for AI agent within AppSec. The way that it is usually done is once a vulnerability is identified, it falls on humans to go through the code, figure out the flaw, and then apply a fix. This process can be time-consuming with a high probability of error, which often results in delays when deploying crucial security patches. It's a new game with agentic AI. By leveraging the deep understanding of the codebase provided through the CPG, AI agents can not just identify weaknesses, and create context-aware and non-breaking fixes. The intelligent agents will analyze all the relevant code to understand the function that is intended as well as design a fix that corrects the security vulnerability while not introducing bugs, or breaking existing features. AI-powered automation of fixing can have profound implications. The period between finding a flaw before addressing the issue will be reduced significantly, closing an opportunity for hackers. It reduces the workload for development teams so that they can concentrate on building new features rather of wasting hours trying to fix security flaws. Additionally, by automatizing the fixing process, organizations can guarantee a uniform and reliable process for vulnerability remediation, reducing the possibility of human mistakes or oversights. Questions and Challenges Although the possibilities of using agentic AI for cybersecurity and AppSec is immense It is crucial to acknowledge the challenges and issues that arise with its adoption. One key concern is the question of confidence and accountability. Organisations need to establish clear guidelines to ensure that AI acts within acceptable boundaries since AI agents become autonomous and become capable of taking decision on their own. It is crucial to put in place solid testing and validation procedures to ensure security and accuracy of AI created changes. Another challenge lies in the risk of attackers against the AI itself. When agent-based AI techniques become more widespread in the world of cybersecurity, adversaries could attempt to take advantage of weaknesses in the AI models or manipulate the data upon which they're trained. This highlights the need for secure AI development practices, including methods such as adversarial-based training and the hardening of models. Quality and comprehensiveness of the property diagram for code can be a significant factor in the success of AppSec's agentic AI. To create and keep an precise CPG You will have to acquire techniques like static analysis, testing frameworks, and integration pipelines. https://sites.google.com/view/howtouseaiinapplicationsd8e/ai-in-application-security have to make sure that their CPGs are updated to reflect changes that occur in codebases and changing security environment. Cybersecurity Future of AI agentic The future of autonomous artificial intelligence in cybersecurity is extremely positive, in spite of the numerous obstacles. As AI advances and become more advanced, we could be able to see more advanced and efficient autonomous agents capable of detecting, responding to, and reduce cyber-attacks with a dazzling speed and accuracy. For AppSec, agentic AI has an opportunity to completely change the process of creating and secure software. This could allow organizations to deliver more robust as well as secure applications. Furthermore, the incorporation of artificial intelligence into the wider cybersecurity ecosystem provides exciting possibilities for collaboration and coordination between diverse security processes and tools. Imagine a future w here agents are self-sufficient and operate on network monitoring and response as well as threat analysis and management of vulnerabilities. They would share insights, coordinate actions, and help to provide a proactive defense against cyberattacks. It is crucial that businesses accept the use of AI agents as we progress, while being aware of the ethical and social impacts. The power of AI agentics to create an unsecure, durable as well as reliable digital future by creating a responsible and ethical culture in AI development. The article's conclusion is as follows: Agentic AI is an exciting advancement in the world of cybersecurity. https://www.linkedin.com/posts/qwiet_gartner-appsec-qwietai-activity-7203450652671258625-Nrz0 's an entirely new method to recognize, avoid, and mitigate cyber threats. Through the use of autonomous AI, particularly in the area of app security, and automated security fixes, businesses can shift their security strategies by shifting from reactive to proactive, from manual to automated, and move from a generic approach to being contextually sensitive. While challenges remain, the potential benefits of agentic AI are far too important to ignore. In the process of pushing the limits of AI for cybersecurity the need to consider this technology with an attitude of continual development, adaption, and innovative thinking. This way we can unleash the full potential of agentic AI to safeguard our digital assets, secure our organizations, and build better security for everyone.