Leveraging AI & Machine Learning in Identity Security

AI and Machine Learning Accelerate Identity Security by Streamlining Processes and Providing Actionable Insights to Administrators and Users.

The recent advent of ChatGPT has created an explosion of interest in artificial intelligence and machine learning. People have theorized about the potential use of AI and ML as both a threat to and a protector of cybersecurity systems. As a threat, they can be used to generate better phishing emails, find faults in code, and engage users in life-like conversations that trick them into sharing personal information. But they also can strengthen identity security processes, enabling faster detection of bad actors and rapid termination of access. In this blog post, we will discuss the benefits of using AI and ML in identity and access management.

Defining Artificial Intelligence

So how do AI and ML work and what’s the difference between the two? AI is the simulation of human intelligence by machines. It enables machines to perform tasks that require human intelligence such as thinking, reasoning, learning from experience and, most importantly, making their own decisions. AI can be broken down into applied AI and general AI. Applied AI refers to systems that may automate a specific function or activity while general AI could, in theory, handle any task (Hello Skynet!). 

Currently, AI is more a broad term that can be construed as a general umbrella that includes a variety of technologies such as:

• Machine learning
• Speech recognition
• Recommendation engines
• Image and video processing

Where Does Machine Learning Fit In?

What do we mean by machine learning if it is only a subset of what many consider a broader notion of AI? Within identity security, machine learning is really where the rubber meets the road. 

Think of machine learning as a pathway or application of artificial intelligence that uses complex algorithms and other models to learn and develop insights based on patterns gleaned from data and activities to make increasingly improved decisions. These decisions can be used to provide automated tasks and actionable insights into analyzed data. Within identity security, ML allows the system to learn from user behavior and understand the context of user activity.

Ways to Leverage Machine Learning in Identity Security

ML delivers data-driven predictions and suggestions to help streamline workflows and minimize administrator frustration. There are several areas where ML can be successfully leveraged for identity security. By doing so, identity security programs can empower workforces, reduce management complexity, save costs, and more. With its contextual understanding, a system can automatically suggest the next step or revise a workflow. It helps improve and streamline processes, reduce human related errors, and strengthen overall security.

For example, by assessing and reviewing who has access to what and how that access is or isn’t being used, a system can provide access recommendations throughout an identity’s lifecycle, from the initial request through ongoing micro-certification campaigns. Additionally, many of the more mundane activities related to identity security can be automated, speeding up employee onboarding. Additional insights can be provided to entitlement owners as to how someone’s access stacks up against their peers and other roles to help accelerate approvals and reduce digital fatigue for both administrators and end users.

Machine learning can also identify anomalous activity and identity outliers that increase enterprise risk. Analyzing this data allows for the automation of access revocations or for providing a trigger for further reviews. When working with role development and maintenance, ML can analyze existing roles, see if there are similar roles that could be merged, and provide recommendations on new roles that may be beneficial. 

How Saviynt Uses Artificial Intelligence and Machine Learning

At the heart of Saviynt’s Enterprise Identity Cloud (EIC) sits our Identity Warehouse. This warehouse  consolidates identities from multiple authoritative sources of data (IDMS, HR systems, EHR/EMR, security database, etc.). It ingests and normalizes this information to facilitate quick and accurate decision making related to any identity within your environment. 

The warehouse includes a strong analytics engine at its core that performs ML activities. With all identities in a single repository and the ability to review data from other security technologies, Saviynt’s identity warehouse can provide risk-based analysis of the data and usage patterns, anomaly identification, and remediation. By understanding the sensitivity of your data, you can also increase the speed and accuracy of enterprise security decisions.

The warehouse can also integrate and share risk intelligence with other identity and cybersecurity tools (SIEM, XDR, SASE, etc.) to contextualize risk across the organization. The warehouse enables Saviynt’s strong automation capabilities to reduce manual workloads, increase efficiency, and lower operational costs. And in today’s digital age, the warehouse’s elastic architecture scales automatically with limitless data collection and indexes data to quickly access relevant information, fine tune results, and act instantly.

At the end of the day, AI and machine learning capabilities accelerate identity security program adoption by streamlining processes and providing actionable insights to both administrators and end users. High risk access and activities can be quickly identified and remediated, helping provide continuous regulatory compliance.

By better understanding how identities are being granted access and how that access is being used, organizations can reduce administrator management burdens and let them focus on more pressing security needs such as threat hunting while still shrinking the threat landscape by eliminating over-privileging, and reducing human error.