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The following article is an overview of the subject: Artificial Intelligence (AI) which is part of the ever-changing landscape of cybersecurity is used by businesses to improve their security. As security threats grow more sophisticated, companies are increasingly turning towards AI. AI is a long-standing technology that has been a part of cybersecurity is now being re-imagined as agentic AI which provides flexible, responsive and context aware security. This article explores the transformational potential of AI with a focus specifically on its use in applications security (AppSec) and the ground-breaking concept of artificial intelligence-powered automated vulnerability-fixing. Cybersecurity is the rise of agentsic AI Agentic AI is the term used to describe autonomous goal-oriented robots which are able discern their surroundings, and take the right decisions, and execute actions that help them achieve their targets. Agentic AI is different from the traditional rule-based or reactive AI in that it can change and adapt to its environment, and can operate without. For security, autonomy is translated into AI agents who continually monitor networks, identify abnormalities, and react to threats in real-time, without the need for constant human intervention. Agentic AI has immense potential in the area of cybersecurity. By leveraging machine learning algorithms as well as huge quantities of information, these smart agents are able to identify patterns and similarities that analysts would miss. They can discern patterns and correlations in the haze of numerous security-related events, and prioritize the most crucial incidents, and providing actionable insights for rapid responses. Furthermore, agentsic AI systems can gain knowledge from every incident, improving their threat detection capabilities and adapting to constantly changing tactics of cybercriminals. Agentic AI and Application Security Although agentic AI can be found in a variety of application in various areas of cybersecurity, the impact on the security of applications is important. As organizations increasingly rely on complex, interconnected software, protecting their applications is the top concern. ai application security , including manual code reviews and periodic vulnerability checks, are often unable to keep up with fast-paced development process and growing threat surface that modern software applications. The answer is Agentic AI. By integrating intelligent agent into software development lifecycle (SDLC), organisations can transform their AppSec process from being reactive to proactive. AI-powered systems can continuously monitor code repositories and evaluate each change for potential security flaws. They employ sophisticated methods such as static analysis of code, test-driven testing and machine-learning to detect various issues that range from simple coding errors as well as subtle vulnerability to injection. Agentic AI is unique in AppSec as it has the ability to change to the specific context of each application. With the help of a thorough data property graph (CPG) which is a detailed representation of the codebase that is able to identify the connections between different components of code – agentsic AI is able to gain a thorough grasp of the app's structure as well as data flow patterns and attack pathways. The AI can identify security vulnerabilities based on the impact they have in real life and what they might be able to do, instead of relying solely on a standard severity score. The Power of AI-Powered Intelligent Fixing The idea of automating the fix for vulnerabilities is perhaps the most intriguing application for AI agent technology in AppSec. The way that it is usually done is once a vulnerability is identified, it falls on humans to review the code, understand the vulnerability, and apply the corrective measures. This could take quite a long time, be error-prone and slow the implementation of important security patches. Through agentic AI, the game changes. AI agents are able to detect and repair vulnerabilities on their own thanks to CPG's in-depth knowledge of codebase. They can analyse the source code of the flaw in order to comprehend its function and design a fix which corrects the flaw, while being careful not to introduce any additional vulnerabilities. The implications of AI-powered automatized fix are significant. It will significantly cut down the amount of time that is spent between finding vulnerabilities and its remediation, thus eliminating the opportunities for hackers. This relieves the development team from having to dedicate countless hours fixing security problems. Instead, they can concentrate on creating new capabilities. Automating the process of fixing vulnerabilities will allow organizations to be sure that they are using a reliable and consistent approach which decreases the chances for human error and oversight. What are the challenges and issues to be considered? Though the scope of agentsic AI for cybersecurity and AppSec is immense It is crucial to understand the risks and issues that arise with its implementation. Accountability and trust is a crucial issue. The organizations must set clear rules for ensuring that AI behaves within acceptable boundaries since AI agents gain autonomy and can take decision on their own. It is vital to have reliable testing and validation methods to ensure safety and correctness of AI developed changes. A second challenge is the possibility of attacks that are adversarial to AI. When agent-based AI systems become more prevalent in the world of cybersecurity, adversaries could try to exploit flaws in the AI models or manipulate the data they're based. This underscores the necessity of security-conscious AI methods of development, which include methods like adversarial learning and the hardening of models. Additionally, the effectiveness of agentic AI in AppSec is heavily dependent on the accuracy and quality of the code property graph. The process of creating and maintaining an exact CPG will require a substantial investment in static analysis tools and frameworks for dynamic testing, and pipelines for data integration. Companies also have to make sure that their CPGs reflect the changes that occur in codebases and the changing threats environments. The future of Agentic AI in Cybersecurity The future of agentic artificial intelligence in cybersecurity is exceptionally promising, despite the many problems. We can expect even superior and more advanced autonomous agents to detect cyber security threats, react to them and reduce their impact with unmatched agility and speed as AI technology advances. legacy system ai security inside AppSec has the ability to alter the method by which software is designed and developed which will allow organizations to develop more durable and secure software. In addition, the integration of AI-based agent systems into the cybersecurity landscape opens up exciting possibilities to collaborate and coordinate various security tools and processes. Imagine a world where agents are autonomous and work on network monitoring and responses as well as threats security and intelligence. They would share insights to coordinate actions, as well as help to provide a proactive defense against cyberattacks. It is essential that companies accept the use of AI agents as we move forward, yet remain aware of its ethical and social consequences. By fostering machine learning appsec of responsible AI development, transparency and accountability, we are able to harness the power of agentic AI to create a more safe and robust digital future. Conclusion Agentic AI is a revolutionary advancement in the world of cybersecurity. It's a revolutionary paradigm for the way we identify, stop, and mitigate cyber threats. Through the use of autonomous AI, particularly in the area of applications security and automated vulnerability fixing, organizations can improve their security by shifting from reactive to proactive shifting from manual to automatic, and from generic to contextually cognizant. Agentic AI has many challenges, yet the rewards are too great to ignore. As we continue pushing the limits of AI in cybersecurity, it is essential to approach this technology with a mindset of continuous adapting, learning and responsible innovation. This will allow us to unlock the full potential of AI agentic intelligence to protect the digital assets of organizations and their owners.