5 ways Generative AI Can Redefine Identity Access Management

KEY TAKEAWAYS

Generative AI's application in IAM solutions brings about stronger security measures for IAM providers. Leveraging the capabilities of Gen AI can bolster security and operational efficiency in organizational IAM frameworks. We ask experts to explain how this is happening now — and what may come in the future.

Since ChatGPT appeared, the generative artificial intelligence (Gen AI) wheel has been continually spinning.

While the technology has mostly gained popularity in AI-powered chatbots like Google Bard, Claude, and ChatGPT, it’s branched out with more use cases, propelling a high adoption rate in both personal and corporate spaces.

Gartner recently reported that more than 80% of enterprises will have used Gen AI’s application programming interfaces (APIs) or deployed GenAI-enabled applications by 2026, up from less than 5% in 2023.

One technology domain poised for the transformative role of Gen AI is Identity and Access Management (IAM). A recent survey of over 500 identity and security professionals shows that a majority (90%) are confident in the positive impact of AI in improving identity and access management.

But before we dive into the hows, let’s glance at some of the challenges of IAM that make it ripe for GenAI transformation.

Challenges of IAM

Managing identities for enterprises comes with a lot of complexities. With a growing number and variety of user identities, cloud resources, decentralized workforces, and shadow IT, businesses are under pressure to ensure their employees can securely and effectively access organizational networks, email accounts, file servers, etc.

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Before now, organizations leveraged MFA as a security recipe against identity-based attacks, but the IAM threat landscape is changing. A recent identity threat report indicates that attackers have learned ways to maneuver the security checkpoints embedded in MFA through means like reverse phishing proxies, also known as real-time phishing proxies.

READ MORE: 9 Common Phishing Attacks — and How to Avoid Them

Cybercriminals use this method to harvest session cookies and erode MFA security checks. The report also noted that there are detection evasion tools, MFA bypass techniques, and other social engineering vectors that can crack MFA. This means only one thing – MFA is no longer enough to boost the security and useability of IAM systems.

And this is where Gen AI comes into the fray.

5 Ways Gen AI can redefine the use of IAM in enterprises

5. Contribute to Intelligent Access Policy Management

Many identity and access management systems have a complex set of rules and groups. Atul Tulshibagwale, Chief Technology Officer at SGNL.ai, noted that, in some ways, it is akin to having to program in low-level languages such as assembly language.

In this case, GenAI plays a crucial role in enhancing intelligent access policy management due to its ability to leverage advanced artificial intelligence (AI) and machine learning (ML) techniques. Gen AI facilitates the analysis of massive datasets in real-time to identify patterns, anomalies, and user behaviors within an organization’s digital ecosystem.

With sophisticated algorithms, Gen AI can recognize trends in user access patterns, helping IAM systems dynamically adjust access policies based on real-time data.

According to Tulshibagwale, “Gen AI makes it easier for administrators to make sense of policies and myriad groups/roles such that the AI can answer questions about the current status or even generate new policies and memberships based on high-level prompts.”

With the capacity for continuous learning, Gen AI can understand the context of access requests and evaluate them against predefined policies, Gideon Kalu, Founder and CEO of Femur Inc., told Techopedia.

This enables organizations to implement more adaptive access controls, granting or revoking access based on factors such as user behavior, location, and device characteristics.

4. Curb Challenges Posed by Insider Threat

To some organizations, their most significant asset could also become their most considerable risk. Insider threats and, to some extent, zombie credentials are two major exposure points for many organizations.

As a result, IAM providers can leverage AI to create auto-deployed decoys, enhance behavioral detection, and improve Asset Graph technology within their extended detection and response (XDR) platforms to combat these threats.

In addition, many IAM providers like IBM Cloud Identity, Microsoft Azure, Active Directory, and Google Cloud Identity would most likely capitalize on this approach to deliver enhanced products that focus on contextual intelligence to improve identity-based solutions for enterprises, leading to IAM market expansion.

3. Improve Application Access Rights Management

Managing a team’s account credentials and access rights is such a sensitive and challenging task, especially in larger organizations. Gen AI streamlines this intricate process by automating most of the functions. When an employee undergoes onboarding or offboarding, a user’s details and role information can be input into a natural language prompt, prompting the model to autonomously generate tailored account credentials, group memberships, and access rights for various applications.

Tulshibagwale shares a similar view as she stated that “Gen AI can help administrators generate low-level rules or group memberships based on high-level descriptions of policies.”

These dynamically generated profiles serve as a foundation, providing administrators with a convenient starting point. Admins can then efficiently review and fine-tune the generated settings based on specific requirements or user role changes.

2. Improve Personalized Access Recommendations

Generative AI can help tailor access permissions for individual users within an organization by analyzing each user’s historical access patterns and job responsibilities.

“With this level of analysis, the AI system gains a nuanced understanding of each person’s specific needs and behaviors in the organization using factors such as the frequency of access to certain resources, the type of tasks performed, and the hierarchical context within the organization,” Gideon Kalu, Founder and CEO at Femur Inc., told Techopedia in a statement.

This granular analysis enables the system to generate personalized recommendations that align precisely with each user’s role and responsibilities.

These recommendations are not static but dynamically adapt over time as the user’s job evolves or new patterns emerge.

With this, system admins can easily incorporate these personalized access recommendations into an IAM portal without having to calibrate through lists of access permissions.

1. Reduce False Positives

False positives or alerts incorrectly indicating a security threat during authentication are significant problems for many IAM providers.

The earlier generation of IAM models operated on a rule-based framework that adhered to a simplistic “if-this-then-that” approach. While this made the system easily manageable, its effectiveness was limited in handling intricate scenarios.

Some models combined rule-based systems with traditional machine-learning techniques, allowing for high-dimensional data analysis. Unfortunately, this approach proved to be resource-intensive, demanding considerable amounts of data and time.

However, integrating Gen AI into a traditional machine learning algorithm in IAM solutions enhances fraud detection capabilities. Tulshibagwale noted that “even in scenarios where advanced malware variant constantly mutates their code and evasion tactics in response to detection mechanisms, IAM solutions built on this model will be able to adapt and identify complex and evolving fraudulent patterns.”

The outcome is a substantial reduction in false positives, marking a significant advancement in the efficiency of fraud detection in AIM systems.

The Bottom Line

Applying generative AI in IAM solutions brings about more robust security measures for IAM providers. Leveraging the capabilities of Gen AI can bolster security and operational efficiency in organizational IAM frameworks.

Nevertheless, a prudent approach is essential. The potential for biased decision-making from machine learning algorithms being trained on biased datasets and privacy issues is still a source of concern to many. Also, without human supervision, access policies suggested by generative AI might lead to policies that allow access to unintended users.

So, while organizations strive to unlock the complete potential of Gen AI, it’s also crucial to uphold the integrity and reliability of their identity access management processes.

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Franklin Okeke

Franklin has been covering tech and cybersecurity for over 5 years. His work has appeared on TechRepublic, The Register, TechInformed, Computing, ServerWatch, and Moonlock, among others.