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Introduction Artificial Intelligence (AI) as part of the continually evolving field of cybersecurity it is now being utilized by businesses to improve their defenses. Since threats are becoming more complex, they are increasingly turning to AI. AI, which has long been used in cybersecurity is being reinvented into agentsic AI, which offers an adaptive, proactive and fully aware security. This article explores the revolutionary potential of AI and focuses specifically on its use in applications security (AppSec) and the pioneering concept of automatic security fixing. Cybersecurity is the rise of artificial intelligence (AI) that is agent-based Agentic AI can be that refers to autonomous, goal-oriented robots that are able to detect their environment, take decisions and perform actions to achieve specific desired goals. Agentic AI is distinct in comparison to traditional reactive or rule-based AI as it can change and adapt to changes in its environment as well as operate independently. When it comes to cybersecurity, this autonomy is translated into AI agents that are able to continuously monitor networks, detect anomalies, and respond to threats in real-time, without continuous human intervention. Agentic AI's potential in cybersecurity is immense. The intelligent agents can be trained to recognize patterns and correlatives with machine-learning algorithms and huge amounts of information. These intelligent agents can sort out the noise created by a multitude of security incidents and prioritize the ones that are most important and providing insights for quick responses. Agentic AI systems are able to develop and enhance their ability to recognize risks, while also responding to cyber criminals constantly changing tactics. Agentic AI and Application Security Agentic AI is a powerful device that can be utilized to enhance many aspects of cybersecurity. The impact it can have on the security of applications is notable. In a world where organizations increasingly depend on sophisticated, interconnected software, protecting those applications is now an absolute priority. The traditional AppSec strategies, including manual code reviews and periodic vulnerability scans, often struggle to keep up with the rapidly-growing development cycle and threat surface that modern software applications. Enter agentic AI. Through the integration of intelligent agents into software development lifecycle (SDLC) companies are able to transform their AppSec practices from proactive to. These AI-powered systems can constantly monitor code repositories, analyzing every code change for vulnerability as well as security vulnerabilities. They are able to leverage sophisticated techniques like static code analysis test-driven testing and machine learning, to spot a wide range of issues, from common coding mistakes to subtle injection vulnerabilities. The thing that sets agentic AI distinct from other AIs in the AppSec area is its capacity to comprehend and adjust to the particular circumstances of each app. Agentic AI can develop an intimate understanding of app structure, data flow, and the attack path by developing the complete CPG (code property graph), a rich representation of the connections between code elements. The AI will be able to prioritize vulnerability based upon their severity on the real world and also the ways they can be exploited and not relying upon a universal severity rating. Artificial Intelligence and Autonomous Fixing The idea of automating the fix for flaws is probably one of the greatest applications for AI agent AppSec. Human developers were traditionally required to manually review code in order to find the vulnerabilities, learn about it and then apply the solution. This can take a lengthy duration, cause errors and slow the implementation of important security patches. Through agentic AI, the game is changed. Through the use of the in-depth knowledge of the base code provided by CPG, AI agents can not only identify vulnerabilities as well as generate context-aware automatic fixes that are not breaking. They can analyze the code around the vulnerability and understand the purpose of it before implementing a solution which fixes the issue while not introducing any additional problems. AI-powered automation of fixing can have profound effects. It can significantly reduce the period between vulnerability detection and repair, eliminating the opportunities for attackers. It can also relieve the development team from having to devote countless hours fixing security problems. Instead, they could work on creating innovative features. Furthermore, through automatizing the fixing process, organizations are able to guarantee a consistent and reliable method of fixing vulnerabilities, thus reducing the chance of human error and oversights. What are the main challenges and issues to be considered? While the potential of agentic AI for cybersecurity and AppSec is vast but it is important to be aware of the risks and issues that arise with its adoption. The issue of accountability as well as trust is an important one. When AI agents grow more autonomous and capable taking decisions and making actions by themselves, businesses should establish clear rules and oversight mechanisms to ensure that the AI is operating within the boundaries of behavior that is acceptable. It is essential to establish solid testing and validation procedures in order to ensure the security and accuracy of AI generated fixes. A second challenge is the risk of an attacking AI in an adversarial manner. In the future, as agentic AI systems become more prevalent in cybersecurity, attackers may attempt to take advantage of weaknesses in the AI models or manipulate the data on which they're trained. This is why it's important to have safe AI practice in development, including methods such as adversarial-based training and model hardening. In addition, the efficiency of the agentic AI for agentic AI in AppSec is heavily dependent on the completeness and accuracy of the property graphs for code. Building and maintaining an reliable CPG is a major spending on static analysis tools and frameworks for dynamic testing, as well as data integration pipelines. Companies also have to make sure that their CPGs correspond to the modifications that occur in codebases and changing threat landscapes. The future of Agentic AI in Cybersecurity The future of AI-based agentic intelligence in cybersecurity is extremely positive, in spite of the numerous challenges. The future will be even better and advanced autonomous AI to identify cyber-attacks, react to them, and diminish the damage they cause with incredible agility and speed as AI technology develops. In the realm of AppSec the agentic AI technology has the potential to revolutionize the way we build and secure software. This will enable organizations to deliver more robust reliable, secure, and resilient applications. Additionally, the integration of agentic AI into the wider cybersecurity ecosystem opens up exciting possibilities to collaborate and coordinate diverse security processes and tools. Imagine a scenario where autonomous agents collaborate seamlessly throughout network monitoring, incident response, threat intelligence, and vulnerability management. They share insights and co-ordinating actions for a comprehensive, proactive protection against cyber threats. It is crucial that businesses take on agentic AI as we move forward, yet remain aware of its social and ethical implications. If we can foster a culture of ethical AI advancement, transparency and accountability, we are able to use the power of AI for a more solid and safe digital future. The end of the article is as follows: In today's rapidly changing world of cybersecurity, the advent of agentic AI represents a paradigm shift in the method we use to approach the detection, prevention, and mitigation of cyber security threats. Agentic AI's capabilities specifically in the areas of automated vulnerability fix as well as application security, will help organizations transform their security posture, moving from a reactive strategy to a proactive strategy, making processes more efficient as well as transforming them from generic context-aware. Agentic AI faces many obstacles, however the advantages are enough to be worth ignoring. In ai security for startups of pushing AI's limits when it comes to cybersecurity, it's essential to maintain a mindset to keep learning and adapting of responsible and innovative ideas. It is then possible to unleash the potential of agentic artificial intelligence for protecting digital assets and organizations.