Challenges with integrating ML and IAM

While integrating identity and access management has numerous benefits for organizations, some challenges remain with the initial deployment. There is a significant upfront expense associated with implementing IAM, and it can be challenging to integrate IAM with legacy systems. The ML models that power the behavior analytics approach require large amounts of high-quality data to learn from. This can be difficult to obtain, especially in the early stages of implementation. IAM systems occasionally report false positives and flag suspicious activity when there is none, resulting in increased employee tedium.

Tackling these challenges begins with a sound implementation strategy. Developing a comprehensive data collection, cleaning, and pre-processing strategy can help enterprises ensure they have access to enough quality data to train their models. The difficulties of integrating IAM systems with legacy ones can be eased with the strategic use of application programming interfaces (APIs). End-to-end testing in lower environments before deployment can minimize operational disruption. When false positive flags occur, they can be integrated into future training, resulting in self-improving models that learn from their mistakes. Despite the upfront investment, IAM systems can be tremendously financially beneficial for organizations due to the high cost of cybersecurity breaches. For instance, a Forrester report found that one such system boasted a return on investment (ROI) of 240 percent.

Enterprises can proactively detect and stop potential threats by incorporating ML into IAM. To prepare for a future involving new cybersecurity threats, it is critical for businesses to invest in behavioral analytics technologies. Equally vital is the careful training of analytics models, including designing and implementing data collection, cleaning, and management strategies. The future of IAM lies in self-healing systems capable of predicting and adapting to new threats in real-time, significantly minimizing human error and enhancing overall security posture. With proper implementation, integrating AI and ML can provide strong guardrails against threats in a rapidly changing cybersecurity landscape.

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Protecting What Matters: How Smart Technology Is Making Organizational Lives Safer

By Gaurav Rathi on

June 5, 2024

How Smart Technology Is Making Organizational Lives Safer Authentication. Verifying a user’s identity using passwords, biometric logins, or other methods is conducted via authentication. Multi-factor authentication, in which more than one form of identity verification is required for users to access their accounts, can enhance the security of authentication, as can adopting advanced verification methods like biometrics.

Authorization. This is a user’s ability to access resources and perform actions. A zero-trust authorization model denies users, even those within the company, access to resources by default. Within a zero-trust framework, there are two main ways to manage access control. In role-based access control (RBAC) systems, authorization is based solely on a user’s role in the organization. In attribute-based access control (ABAC) systems, factors such as the user’s role, the resource’s importance or level of sensitivity, and the date, time, or location all contribute to determining access permissions. The most secure and appropriate choice depends on the size and structure of the organization.

Administration. This refers to the managing of user accounts and permissions. Strong administration grants only the permissions necessary for users to carry out their job responsibilities, a concept known as the principle of least privilege.

Auditing and reporting. These involve logging and monitoring user behavior to identify suspicious activities, creating detailed reports regarding identity and access permissions, and distributing them to stakeholders.

A robust IAM system can detect and tackle data breaches before they occur. Consider a scenario where an employee’s credentials are compromised. An IAM system with dynamic authorization could limit access based on anomalous login patterns, effectively neutralizing the potential breach.

How continuous user behavior analytics fits into an IAM framework

Continuous user behavior analytics (CUBA) is an advanced approach that integrates seamlessly into an IAM framework, significantly enhancing its security and efficiency. It can be compared to a neighborhood watch that pays attention to everyone’s habits. If someone starts doing something odd, like peeking into windows, the neighborhood watch will notice and investigate. CUBA utilizes advanced ML algorithms that can be integrated into IAM frameworks. By alerting security teams when unusual behavior is detected, CUBA can anticipate and prevent potential security incidents. Financial software company Intuit, for example, has found success by implementing a continuous behavior analytics system. Not only has it successfully prevented many instances of fraud, but it also has a dramatically lower false positive rate than the company’s previous, less sophisticated fraud detection system.