The Value of AI-Powered Identity

Machine Learning / Data Security

Introduction

Artificial intelligence (AI) deepfakes and misinformation may cause worry in the world of technology and investment, but this powerful, foundational technology has the potential to benefit organizations of all kinds when harnessed appropriately.

In the world of cybersecurity, one of the most important areas of application of AI is augmenting and enhancing identity management systems. AI-powered identity lifecycle management is at the vanguard of digital identity and is used to enhance security, streamline governance and improve the UX of an identity system.

Benefits of an AI-powered identity

AI is a technology that crosses barriers between traditionally opposing business area drivers, bringing previously conflicting areas together:

• AI enables better operational efficiency by reducing risk and improving security
• AI enables businesses to achieve goals by securing cyber-resilience
• AI facilitates agile and secure access by ensuring regulatory compliance

AI and unified identity

AI-powered identity delivers the intelligence needed to repel attacks and correct access anomalies impacting our identity infrastructure. However, a key enabler of AI within an identity lifecycle management system is the unification of identity. AI can find applications across a unified identity surface, working symbiotically to meet the requisites of the business drivers.

AI-powered identity in practice

When applied appropriately, AI technologies have the power to mitigate access errors and tackle the current onslaught of identity-centered cyberattacks. AI-powered identity can leverage machine learning models to identify signals of an attack, such as behavioral anomalies, that point to a data exfiltration event.

One Identity has capitalized on the power of AI models to enhance and enable various aspects of identity security:

Risk detection for identity governance and administration (IGA)

AI-powered identity governance and administration (IGA) offers a method to identify unusual behavior and spot the signals of data exposure and data exfiltration events. One Identity Safeguard uses an AI model known as “Random Forests,” a machine learning algorithm combining the output from multiple decision trees to deliver insights. Safeguard analyzes data from such events as mouse movement, keystroke dynamics, login time and command analytics to identify behavioral anomalies and automate attack. Human operators then interact with a dashboard to interpret and make decisions based on the AI-generated output to allow an organization to effectively lower the cybersecurity skills barrier.

Access management

Data from access management authentication events can be leveraged to identify a signal of cyberattack and credential compromise. The access event data (e.g., identity, location, device, etc.) is gathered when someone logs in. An authorization decision is made, and security requirements may then use step-up authentication rather than deny access.

However, AI advances this simple model. One Identity OneLogin uses Vigilance AI™ Threat Engine15 to analyze large volumes of data to identify threats. By utilizing User and Entity Behavior Analytics (UEBA), a profile of typical user behavior is created as a baseline. This is then used to identify anomalies and prevent risk.

OneLogin can feed the data from access requests, as well as its derived analytical insights, in the form of rich syslogs into SIEM and SOC systems.

Entitlement management

Role-based access is a fundamental principle of identity security. But managing those roles manually can pose a challenge. Machine learning has been used in identity “role mining” or “role discovery” for some time, but a novel application from One Identity delivers the role mining insights directly to the relevant person for streamlined entitlement management.

For example, you can use AI to optimize team role policies on an ongoing basis, making entitlement management an ongoing, automated task that provides accurate insights into access requirements across the organization.

Conclusion

Identity management systems must respond to the increasing volume of sophisticated identity-based threats. The response comes in the form of system augmentation through AI, with authoritative, high-quality identity data feeding the AI models used to enhance identity lifecycle management. This capability enhancement is essential in developing and delivering entitle management and IGA for a robust security posture and cyber resilience. With the unification of identity-related services making identity management simpler and more effective, adding AI to a unified identity platform endows an organization with the resilience to resist even the most complex identity-related threats.

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