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Introduction The ever-changing landscape of cybersecurity, where the threats are becoming more sophisticated every day, businesses are using Artificial Intelligence (AI) for bolstering their defenses. While AI has been an integral part of cybersecurity tools for a while and has been around for a while, the advent of agentsic AI can signal a revolution in intelligent, flexible, and contextually sensitive security solutions. This article delves into the transformational potential of AI by focusing on its application in the field of application security (AppSec) and the ground-breaking concept of AI-powered automatic security fixing. Cybersecurity is the rise of artificial intelligence (AI) that is agent-based Agentic AI relates to self-contained, goal-oriented systems which are able to perceive their surroundings to make decisions and make decisions to accomplish particular goals. Agentic AI is distinct from traditional reactive or rule-based AI because it is able to change and adapt to its environment, as well as operate independently. The autonomous nature of AI is reflected in AI agents for cybersecurity who are capable of continuously monitoring networks and detect irregularities. Additionally, they can react in real-time to threats without human interference. Agentic AI has immense potential in the cybersecurity field. Through the use of machine learning algorithms as well as vast quantities of data, these intelligent agents can spot patterns and relationships that analysts would miss. They can discern patterns and correlations in the haze of numerous security events, prioritizing the most crucial incidents, as well as providing relevant insights to enable rapid response. Agentic AI systems can be trained to improve and learn their abilities to detect security threats and responding to cyber criminals constantly changing tactics. Agentic AI (Agentic AI) and Application Security Agentic AI is an effective technology that is able to be employed in a wide range of areas related to cybersecurity. But the effect it can have on the security of applications is noteworthy. With more and more organizations relying on interconnected, complex software systems, securing these applications has become an absolute priority. AppSec tools like routine vulnerability scans as well as manual code reviews are often unable to keep up with modern application developments. Enter agentic AI. Integrating intelligent agents into the software development lifecycle (SDLC) companies are able to transform their AppSec processes from reactive to proactive. AI-powered agents are able to continuously monitor code repositories and evaluate each change for potential security flaws. They may employ advanced methods such as static analysis of code, test-driven testing and machine learning to identify a wide range of issues including common mistakes in coding to subtle injection vulnerabilities. The agentic AI is unique to AppSec because it can adapt and understand the context of any app. Agentic AI is capable of developing an intimate understanding of app structures, data flow as well as attack routes by creating the complete CPG (code property graph), a rich representation that captures the relationships between various code components. This contextual awareness allows the AI to prioritize vulnerabilities based on their real-world vulnerability and impact, rather than relying on generic severity ratings. Artificial Intelligence and Automatic Fixing Perhaps the most interesting application of agents in AI within AppSec is the concept of automatic vulnerability fixing. Human developers were traditionally in charge of manually looking over codes to determine the vulnerability, understand the issue, and implement the corrective measures. This can take a long time, error-prone, and often leads to delays in deploying crucial security patches. The game is changing thanks to the advent of agentic AI. Through the use of the in-depth understanding of the codebase provided with the CPG, AI agents can not just detect weaknesses as well as generate context-aware not-breaking solutions automatically. AI agents that are intelligent can look over the code that is causing the issue, understand the intended functionality and then design a fix which addresses the security issue while not introducing bugs, or breaking existing features. The implications of AI-powered automatized fixing are profound. The period between discovering a vulnerability and fixing the problem can be significantly reduced, closing the possibility of the attackers. It can alleviate the burden on development teams as they are able to focus on building new features rather then wasting time fixing security issues. Automating the process for fixing vulnerabilities allows organizations to ensure that they are using a reliable and consistent method which decreases the chances of human errors and oversight. Problems and considerations Although the possibilities of using agentic AI in cybersecurity and AppSec is vast but it is important to understand the risks and considerations that come with its implementation. In https://go.qwiet.ai/multi-ai-agent-webinar of accountability and trust is a key one. As AI agents get more independent and are capable of making decisions and taking action by themselves, businesses should establish clear rules as well as oversight systems to make sure that AI is operating within the bounds of acceptable behavior. AI is operating within the boundaries of behavior that is acceptable. This includes the implementation of robust test and validation methods to check the validity and reliability of AI-generated changes. Another issue is the possibility of adversarial attacks against AI systems themselves. In the future, as agentic AI systems are becoming more popular in cybersecurity, attackers may seek to exploit weaknesses within the AI models or manipulate the data upon which they're based. This underscores the necessity of security-conscious AI development practices, including strategies like adversarial training as well as model hardening. Additionally, the effectiveness of the agentic AI used in AppSec is heavily dependent on the quality and completeness of the code property graph. Maintaining and constructing an accurate CPG is a major spending on static analysis tools such as dynamic testing frameworks and pipelines for data integration. Businesses also must ensure they are ensuring that their CPGs reflect the changes that take place in their codebases, as well as the changing security environments. Cybersecurity: The future of artificial intelligence The future of autonomous artificial intelligence in cybersecurity is exceptionally positive, in spite of the numerous challenges. As AI techniques continue to evolve, we can expect to see even more sophisticated and efficient autonomous agents that can detect, respond to, and reduce cyber threats with unprecedented speed and precision. Within the field of AppSec, agentic AI has the potential to revolutionize how we design and secure software. This could allow businesses to build more durable reliable, secure, and resilient software. Furthermore, the incorporation in the cybersecurity landscape opens up exciting possibilities to collaborate and coordinate different security processes and tools. Imagine a world where autonomous agents are able to work in tandem in the areas of network monitoring, incident intervention, threat intelligence and vulnerability management. Sharing insights and taking coordinated actions in order to offer an all-encompassing, proactive defense against cyber threats. It is crucial that businesses accept the use of AI agents as we progress, while being aware of the ethical and social impacts. By fostering a culture of accountable AI development, transparency and accountability, we are able to make the most of the potential of agentic AI for a more robust and secure digital future. The end of the article is: Agentic AI is a breakthrough in cybersecurity. It's a revolutionary approach to detect, prevent cybersecurity threats, and limit their effects. Agentic AI's capabilities particularly in the field of automated vulnerability fix and application security, can assist organizations in transforming their security posture, moving from a reactive strategy to a proactive strategy, making processes more efficient that are generic and becoming contextually aware. There are many challenges ahead, but the benefits that could be gained from agentic AI are too significant to overlook. While we push AI's boundaries in cybersecurity, it is important to keep a mind-set of continuous learning, adaptation, and responsible innovations. This way it will allow us to tap into the potential of artificial intelligence to guard our digital assets, secure our organizations, and build the most secure possible future for all.