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Agentic AI for Cybersecurity and Risk Management (Autonomous AI for Fraud Detection, Compliance, And Threat Mitigations)
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
The high complexity of cyber threats prompts Agentic AI systems to restructure cybersecurity measures because they autonomously handle self-directed decisions. Agentic AI receives its capability for real-time cyber threat detection and analysis and response through deep learning and machine learning combined with reinforcement learning. This enables automatic response instead of human-based detection and intervention methods. Such security systems use automated threat assessment with abnormality detection to enable self-operated incident response functions and accelerate defense against fraud and compliance violations. Current financial institutions utilize Agentic AI technology to develop operational fraud detection systems as their primary beneficial application. Machine agents during transactions execute behavioral analysis and predictive analysis to find fraudulent activities through organizational data patterns. Self-operated security auditing through Agentic AI enhances regulatory compliance while simultaneously facilitating easier manual audit tasks to support data protection standards. The implementation of Agentic AI brings value to businesses through multiple benefits but introduces three main operational difficulties due to operational biases and unclear decision paths as well as security risk exposure. AI autonomous security systems need ethical methods to achieve critical decisions before they can provide users with secure and unbiased cybersecurity services. The document explores Agentic AI applications in cybersecurity and fraud detection with an analysis of both advantages and challenges in addition to expected future application prospects.
Keywords
Agentic AI in Cybersecurity, Autonomous AI for Threat Detection, AI-Powered Risk Management, Fraud Detection with AI Agents, AI in Compliance and Regulatory Security
How to cite this paper
@article{1707702,
author = {Sai Santhosh Polagani},
title = {Agentic AI for Cybersecurity and Risk Management (Autonomous AI for Fraud Detection, Compliance, And Threat Mitigations)},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {10},
pages = {3-20},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1707702.pdf},
abstract = {The high complexity of cyber threats prompts Agentic AI systems to restructure cybersecurity measures because they autonomously handle self-directed decisions. Agentic AI receives its capability for real-time cyber threat detection and analysis and response through deep learning and machine learning combined with reinforcement learning. This enables automatic response instead of human-based detection and intervention methods. Such security systems use automated threat assessment with abnormality detection to enable self-operated incident response functions and accelerate defense against fraud and compliance violations. Current financial institutions utilize Agentic AI technology to develop operational fraud detection systems as their primary beneficial application. Machine agents during transactions execute behavioral analysis and predictive analysis to find fraudulent activities through organizational data patterns. Self-operated security auditing through Agentic AI enhances regulatory compliance while simultaneously facilitating easier manual audit tasks to support data protection standards. The implementation of Agentic AI brings value to businesses through multiple benefits but introduces three main operational difficulties due to operational biases and unclear decision paths as well as security risk exposure. AI autonomous security systems need ethical methods to achieve critical decisions before they can provide users with secure and unbiased cybersecurity services. The document explores Agentic AI applications in cybersecurity and fraud detection with an analysis of both advantages and challenges in addition to expected future application prospects.},
keywords = {Agentic AI in Cybersecurity, Autonomous AI for Threat Detection, AI-Powered Risk Management, Fraud Detection with AI Agents, AI in Compliance and Regulatory Security},
month = {April},
}