How Agentic AI is redefining software entitlement, provisioning and access management

Technology companies frequently operate within multi-vendor ecosystems and maintain multiple in-house legacy systems and infrastructure, often leading to siloed IAM systems. These systems typically rely on static authentication methods, which require frequent manual intervention and updates to predefined rules and policies. Inadequate access control reviews can expose organizations to heightened risks of identity theft and unauthorized access due to compromised credentials. This fragmentation can make it challenging for organizations to standardize security policies and effectively protect user identities.

By leveraging AI-driven adaptive authentication, organizations can dynamically adjust the user authentication requirements based on location, device usage patterns, login time, behavior, and other contextual factors. AI can assess the risk to login attempts in real time, detect potential anomalies, and determine the need for additional authentication steps using contextual information. By analyzing vast data sets, AI can prompt users for re-authentication, initiate additional authentication steps, or flag suspicious behavior.

Managing access control policies is a growing challenge as organizations face increasing complexity from hybrid IT environments and decentralized systems. These challenges are compounded by the dynamic nature of modern application environments, which demand context-aware, risk-sensitive, and policy-driven access controls that can adapt to evolving business needs and threats. Traditional identity governance models lack consistent enforcement of access policies across diverse systems, which can lead to misconfigurations and elevated risk of unauthorized access. Additionally, they struggle to provide real-time visibility into who has access to what, in which systems, and under what conditions—limiting the organization’s ability to detect and respond to access-related risks effectively.

By analyzing large volumes of identity and activity data, AI can assist IAM teams in dynamically modeling access roles and recommending tailored policies that align with business functions and risk thresholds. Through conversational assistants, AI can also simplify the creation, evaluation, and tuning of access control policies, making IAM operations more accessible. Furthermore, with mechanisms like retrieval-augmented generation (RAG), AI systems can ensure that access recommendations are based on up-to-date, authorized enterprise data, improving policy accuracy while reducing administrative overhead.

Software-as-a-service (SaaS)-based businesses have fluctuating needs and user roles, which can result in complex and time-consuming processes to manage user access controls. Ensuring users have appropriate access based on their roles and applying policies consistently across the organization typically involves a lot of manual effort. Any potential oversight due to manual effort can result in security gaps or misuse of sensitive information.

Many leading providers use AI to continuously monitor access logs and user activities based on user roles and dynamically adjust access controls in real time. AI analyzes the existing access control policies and recommends optimizations to speed up the process of granting/revoking user access. For example, in some organizations, access provisioning is based on legacy reporting structures such as when a new hire inherits all the access rights of a long-serving manager who has rotated through multiple roles. AI identifies such mismatches, recommends corrections, revokes the incorrect access, and grants the correct permissions. It can also provide insights and recommendations to refine roles and permissions to better match actual usage patterns. This can ensure scalability without compromising on user experience or security breaches.

Organizations face mounting challenges in verifying digital identities and detecting fraud across increasingly complex and high-volume user interactions. The rapid rise in account takeover attempts, synthetic identity creation, and deepfake-enabled fraud has exposed weaknesses in traditional credentialing, authentication, and account recovery workflows. Compounding the issue is the use of fragmented identity and fraud management tools, which often operate in silos, limiting visibility into user behavior patterns, risk signals, and cross-channel anomalies—ultimately slowing response times and increasing exposure to fraud.

AI can help transform identity verification and fraud detection by powering intelligent, context-rich decision-making at scale. It can enable real-time evaluation of biometric signals and behavioral patterns by synthesizing structured and unstructured data across diverse identity sources. For fraud detection, AI can enhance anomaly detection models, generating synthetic fraud scenarios for training and fraud investigators triaging alerts, surfacing insights, and explaining risk scores. In account recovery, conversational AI interfaces guide users through secure workflows, helping improve both UX and accuracy.

XaaS-based businesses have complex licensing models that vary by user, device type, and features. As business scales, managing and analyzing an increasing number of licenses becomes cumbersome and resource-intensive, making it hard for organizations to address specific business needs and licensing requirements. Additionally, a lack of a centralized license tracking mechanism can often result in inefficiencies such as underutilized licenses and overlapping subscriptions.

AI can analyze license usage data to help organizations better forecast future needs by gaining insights into underutilized or overutilized features and identifying factors that influence license renewal rates. By analyzing customer profiles, usage patterns and preferences, historical license renewal data, and changes in subscription plans, AI can help organizations make decisions based on data-driven insights. It can also provide recommendations such as upgrading to a higher tier in cases of overutilized licenses, reallocating underutilized licenses, or adjusting subscription plans based on usage patterns.

These capabilities complement, rather than replace, established software asset management (SAM) tools. One of AI’s strengths lies in predictive forecasting and conversational interfaces that can help executives and employees understand license usage without deep technical expertise.

XaaS-based businesses often manage various licensing models that can make scaling services up or down time-consuming and complex. It can lead to customer dissatisfaction due to delayed access provisioning or revenue leakage in cases where customers are able to access services without having paid for them due to incorrect provisioning. If the entitlement systems are rigid and lack the flexibility to provide real-time data insights on customer usage patterns, it can be difficult for organizations to optimize the services.

Leveraging AI can help organizations dynamically adjust entitlements including controlling and adjusting user access rights and provisions by analyzing contractual data, access patterns, and under/over licensing utilization. AI can be used to predict future access needs and continuously adjust policies based on changes in user roles, organizational policies, and security assignments to ensure optimal level of provisioning.

XaaS-based businesses often have manual, resource-intensive workflows for user onboarding, offboarding, and role changes. As organizations scale, managing user accounts across multiple services and platforms can become challenging, leading to incorrect permissions, user rights, or privilege issues. Additionally, the manual processes often result in delayed user onboarding where users don’t have the right access, and overprivileged accounts in cases of user position changes. Assigning incorrect privileges to users can expose organizations to security risk and possible data breach.

Organizations can leverage AI to automate user account workflow and life cycle management for access provisioning and deprovisioning, ensuring compliance with contractual data especially when handling a large volume of users across multiple platforms. AI can help validate the information provided by operations for access provisioning requests for accuracy and completeness. This includes user account provisioning requests, such as assigning roles and configuring permissions, or deprovisioning requests during employee termination.