REGISTRO DOI: 10.69849/revistaft/ni10202212302015
Wilson Leite Rebouças Filho
Abstract
In the modern digital world, safeguarding sensitive data and managing access to critical systems are essential for organizations. Identity and Access Management (IAM) is a key framework for controlling access to systems and data, ensuring only authorized users can gain access. Traditionally relying on static methods like passwords, IAM systems are now facing challenges due to the complexity of cyber threats and the increasing number of users and devices. To address these issues, AI is transforming IAM by improving user authentication, detecting security anomalies, and refining permission management. AI contributes to user authentication through biometric technologies like facial recognition, fingerprint scanning, and voice recognition, reducing vulnerabilities from traditional methods. Machine learning also enhances authentication by continuously analyzing user behavior, adapting systems to recognize legitimate users more accurately. Additionally, AI plays a vital role in anomaly detection by analyzing user activity data across various platforms and identifying unusual patterns that indicate potential threats. AI’s impact extends to dynamic and context-aware permission management, offering real-time adjustments based on factors such as user role and location. Furthermore, AI supports continuous risk assessment and regulatory compliance by monitoring user activities and ensuring proper access controls. The integration of AI in IAM also strengthens cloud security, as seen in the research of Muppa (2022), Mandru (2022), Oduri (2019), Mohammed (2021), Ramakrishnan (2021), and Subburaman (2022), who explore how AI helps mitigate emerging threats, optimize authentication, and improve access control in cloud environments. Ultimately, AI in IAM offers a more adaptive, resilient, and precise solution to evolving security challenges. Organizations adopting AI-powered IAM systems will be better equipped to face future cyber threats, ensuring both data protection and operational continuity.
Keywords: Artificial Intelligence, Identity and Access Management, Cybersecurity, Cloud Security, Machine Learning.
In today’s increasingly digital world, safeguarding sensitive data and managing access to critical systems is essential for organizations. Identity and Access Management (IAM) plays a vital role in ensuring that only authorized users can access specific data and systems. Traditionally, IAM relied on static credentials such as passwords and role-based access controls, but the growing complexity of cyber threats and the increasing number of users and devices have made traditional methods less effective. As a result, the need for more dynamic and adaptive IAM systems has emerged, and this is where artificial intelligence (AI) is making a significant impact. AI is enhancing user authentication, detecting security anomalies, and improving permission management, allowing for more secure and efficient systems.
One of AI’s key contributions is in user authentication. Traditional methods like passwords are vulnerable to attacks such as brute force and phishing, but AI-powered IAM systems have integrated advanced biometric technologies, such as facial recognition, fingerprint scanning, and voice recognition. These methods not only improve security but also provide a smoother user experience. Additionally, machine learning algorithms are continuously learning from user behavior, allowing systems to adapt and identify legitimate users more accurately, thus minimizing unauthorized access risks.
AI also plays a critical role in anomaly detection. By analyzing vast amounts of data from user activities across various platforms, AI can identify unusual patterns that may indicate potential threats. For example, if an employee tries to access sensitive information from an atypical location or during off-hours, the AI system flags this as suspicious. Furthermore, as AI systems evolve, they can detect new and emerging threats, ensuring proactive security measures.
AI enhances the management of user permissions by making IAM systems more dynamic and context-aware. Traditional role-based access control (RBAC) systems can be rigid, often leading to either excessive or insufficient permissions. In contrast, AI-powered IAM systems continuously assess user roles and context, adjusting permissions in real-time based on factors such as location, role, and authentication context. This adaptability not only improves security by reducing risks related to privilege escalation but also enhances the user experience by providing more efficient access control.
Moreover, AI contributes significantly to risk assessment and regulatory compliance. By monitoring user access patterns continuously, AI systems can identify high-risk activities in real-time and trigger appropriate alerts or security measures. They also help organizations maintain compliance by recording user access data, which is crucial for audits and reporting.
Figure 1: Identity and Access Management (IAM) and artificial intelligence (AI).
Research by Muppa (2022) explores how integrating AI with Amazon Web Services (AWS) Identity and Access Management (IAM) can enhance cloud security. With the rapid migration to cloud platforms, ensuring robust security has become increasingly important. Muppa’s study reveals how AI’s predictive capabilities, when combined with AWS IAM tools, can strengthen perimeter security, streamline authentication and authorization, and provide proactive responses to emerging threats, offering valuable insights into how to improve cloud security frameworks.
Similarly, Mandru (2022) highlights AI’s transformative role in IAM systems, addressing challenges like identity management, real-time access control, and protection against modern threats. By incorporating machine learning, data analytics, and automation, AI-powered IAM systems can dynamically monitor user behavior, detect anomalies, and adjust security policies. This adaptability enhances both security and operational efficiency, providing better scalability and resilience against evolving cyber threats.
Oduri’s (2019) research focuses on the growing importance of AI-driven security protocols in enhancing cloud security. With the complexity of cloud environments increasing, traditional security measures often fail to address modern threats. Oduri’s study demonstrates how AI can analyze large data volumes to detect anomalies in real time, enabling more proactive security measures. It also shows how AI enhances data encryption management and ensures compliance with regulatory standards, thereby reducing the risk of unauthorized access and data breaches.
Mohammed (2021) further emphasizes the need for effective identity management in cloud environments. As cloud infrastructures become integral to organizations, identity management plays a crucial role in securing data and ensuring privacy. The study explores how AI-driven solutions can detect abnormal user behavior and automate IAM processes, making these systems more efficient. While automation has improved security and usability, the study stresses the importance of human oversight to maintain security standards and compliance.
Ramakrishnan (2021) presents an in-depth analysis of AI’s integration in IAM systems, specifically focusing on user authentication, authorization, and access control in cloud environments. Using a mixed-methods approach, the study identifies key factors influencing the effectiveness of AI in IAM, such as hardware configuration, computational environments, and demographic factors. The research highlights the need for standardized software, user-centric design, and continuous AI development to enhance IAM performance in cloud environments.
Lastly, Subburaman (2022) discusses how AI can enhance IAM systems to reduce risks associated with cyber threats. The research emphasizes the significance of managing access to digital resources and the role of AI in improving access control, especially in areas like privilege monitoring and administration. By integrating AI, IAM systems can automate tasks, adapt to technological advances, and mitigate security risks. The study also proposes a machine learning-based vector decision classifier to optimize policy decision points (PDP) for improved accuracy and flexibility, enhancing overall access control without compromising security.
Together, these studies reinforce the idea that AI not only optimizes existing IAM systems but also provides innovative solutions to meet the growing security and compliance demands of organizations. The implementation of AI in IAM offers a more adaptable, dynamic, and precise approach to tackling ever-evolving cyber threats. Moreover, the integration of AI makes it possible to personalize and automate access controls more efficiently, ensuring the protection of sensitive data and enhancing the user experience.
In conclusion, adopting AI in identity and access management is not just a trend, but a strategic necessity for organizations seeking to secure their data and maintain the integrity of their systems. AI provides an additional layer of protection, allowing IAM systems to evolve alongside emerging threats, making them more resilient and capable of delivering faster and more accurate responses to risk situations. Organizations that implement these advanced solutions will be better prepared to face the dynamic and challenging cyber landscape of the future, safeguarding not only their data but also their reputation and operational continuity.
References
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