Introduction
Artificial intelligence (AI) fakes and misinformation can cause concern in the tech and investment worlds, but this powerful foundational technology can benefit a variety of organizations if used correctly.
In the world of cyber security, one of the most important areas of application of artificial intelligence is to complement and improve identity management systems. AI-powered identity lifecycle management is at the forefront of digital identity and is used to improve security, optimize management and improve the UX of the identity system.
Advantages of an ID based on artificial intelligence
AI is a technology that overcomes barriers between traditionally opposing factors of business spheres, uniting previously conflicting areas:
- AI enables increased operational efficiency by reducing risk and improving security
- AI enables businesses to achieve their goals by ensuring cyber resilience
- AI enables flexible and secure access while ensuring compliance with regulatory requirements
AI and unified identity
AI-powered authentication provides the intelligence needed to repel attacks and fix access anomalies that affect our authentication infrastructure. However, a key driver of AI in an identity lifecycle management system is identity unification. Artificial intelligence can find applications on a single identity surface, working in symbiosis to meet the demands of business drivers.
Artificial intelligence-based identification in practice
When applied correctly, artificial intelligence technology can mitigate access errors and counter the current onslaught of cyberattacks targeting identity data. AI-powered identification can use machine learning models to identify attack signals, such as behavioral anomalies, that indicate a data theft event.
One Identity has harnessed the power of artificial intelligence models to improve and enable various aspects of identity security:
Identification of risks for identity management and administration (IGA)
Based on artificial intelligence identity management and administration (IGA) offers a method to identify unusual behavior and detect signals of exposure and data theft. One Identity Safeguard uses an artificial intelligence model known as Random Forests, a machine learning algorithm that combines the results of multiple decision trees to generate insights. Safeguard analyzes data on events such as mouse movements, keystroke dynamics, login times, and command analytics to identify behavioral anomalies and automate an attack. Human operators then interact with the dashboard to interpret and make decisions based on AI-generated results to enable the organization to effectively lower the cybersecurity skills barrier.
Access control
Data from access control authentication events can be used to identify the signal of a cyber attack and breach of credentials. Access event data (such as identity, location, device, etc.) is collected when someone logs in. An authorization decision is made, and security requirements can use enhanced authentication instead of denying access.
However, artificial intelligence advances this simple model. One Identity OneLogin uses the Vigilance AI™ Threat Engine15 to analyze large volumes of data to detect threats. When using User and Entity Behavior Analytics (UEBA), a profile of typical user behavior is created as a baseline. This is then used to detect anomalies and prevent risk.
OneLogin can transmit data from access requests, as well as received analytics in the form of rich syslogs to SIEM and SOC systems.
Rights management
Role-based access is a fundamental principle of identity security. But managing these roles manually can be a challenge. Machine learning has been used in “role mining” or “role discovery” identity processes for some time, but a new app from One Identity delivers role mining information directly to the right person for optimization rights management.
For example, you can use artificial intelligence to continuously optimize team role policies, turning rights management into a continuous, automated task that provides a clear view of access requirements across the organization.
Conclusion
Identity management systems must respond to the increasing number of sophisticated identity-based threats. The answer comes in the form of augmenting the system with artificial intelligence, using authoritative high-quality identity data that feeds the artificial intelligence models used to improve identity lifecycle management. This capability enhancement is critical to the development and enforcement of rights management and IGA for robust security and cyber resilience. With the unification of identity-related services making identity management simpler and more efficient, adding artificial intelligence to a unified identity platform gives an organization the resilience to face even the most complex identity-related threats.
