Harnessing AI in Access Management and Identity Security

Itzik Alvas. Entro Security

Artificial Intelligence has lately been shaking things up in the world of Identity Access Management (IAM). And when we talk about harnessing AI for this niche in cybersecurity, it’s largely about leveraging AI’s analytical prowess to scrutinize access patterns, preemptively catching any fishy behavior before it becomes a full-blown security or regulatory nightmare.

But using AI-driven IAM won’t just look out for human users — all autonomous systems, APIs, and connected devices will be studied carefully before authorization. The result is a dynamic security ecosystem that continuously evolves, providing adaptive defense mechanisms against sophisticated cyber threats targeting the whole melting pot of identity types that form our IT infrastructure.

The impact of AI and ML on IAM

Now that we’ve got the basics down, let’s shift gears and see just how AI and ML are turning the IAM world on its head.

Intelligent monitoring

AI excels at continuous monitoring of machine-to-machine interactions, providing an unprecedented level of insight into the behavior of APIs, service accounts, and other automated systems. It complements traditional monitoring systems through its analytics to dissect complex patterns in these interactions and identify potential security risks that might slip past otherwise.

Enhanced anomaly detection

AI and machine learning algorithms are pretty sharp when it comes to identifying patterns that deviate from the norm, making them invaluable for detecting anomalies in the way non-human identities interact.

For instance, AI can detect unusual access patterns or data transfers between systems, particularly in dynamic environments like containerized applications and microservices.

By establishing a baseline of normal behavior for each non-human entity, the system can quickly identify and alert on deviations, allowing for rapid response to potential threats. The system can swiftly identify unusual access patterns or data transfers between systems so that all stakeholders can get a buzz when we face any outlier scenarios. Through constant analysis, it can flag potential issues before they escalate into significant security breaches — a proactive approach all of us can appreciate.

Intelligent access governance

AI-driven role mining can give access and non human identity management an all-new outlook by analyzing interaction patterns and recommending optimal role structures. This approach ensures that all entities maintain the principle of least privilege without the burden of manual oversight.

Similarly, continuous compliance monitoring becomes a reality with AI systems that can track and audit non-human identity activities in real time. These systems can automatically flag policy violations and generate regulatory reports, and adapt to the requirements as they grow.

In the same vein, leveraging risk-based authentication for machine-to-machine interactions adds an extra layer of security. Context-aware policies, when in force, can weigh the risk level of each interaction based on factors such as the sensitivity of the requested resource, the historical behavior of the non-human identity, as well as the current threat intelligence. This dynamic approach shall allow for a more fine-tuned control over access decisions without impeding legitimate operations, just as it shall automatically tighten the measures for the already affected non-human identities.

Improving user experience

There are a ton of ways AI can make the lives of security admins and devs much easier:

With AI keeping track of user behavior patterns just as it does with inter-NHI interactions, organizations can reap the benefits of adaptive authentication. So, now, security measures can be automatically adjusted based on perceived risk, reducing friction for legitimate users while maintaining robust security.
We can also streamline onboarding by automating role assignments through generative AI and IAM access provisioning based on job functions and organizational structure.
Usage patterns can also help AI in creating a highly functional just in time privileged access paradigm to minimize standing privileges.

Customization and personalization

For human users, AI-driven IAM systems can assess patterns in usage and job roles to dynamically adjust access rights, ensuring users have precisely the permissions they need. This granular control extends to adaptive authentication, where AI assesses risk factors in real time to determine the appropriate level of authentication required for each access attempt.

User identity management: AI can enhance the customization of user identity profiles by analyzing user behavior, role changes, and organizational structure, suggesting tailored attribute sets for different user types. For example, AI might recommend adding specific attributes for contractors or temporary workers based on their unique access needs and tenure patterns.
Directory services: AI can enhance the customization of directory structures by analyzing organizational hierarchies, user relationships, and access patterns. It can suggest custom attributes or object classes that better represent the unique structure of an organization. For example, AI might recommend creating custom group types for cross-functional project teams or temporary task forces, improving the directory’s ability to model complex organizational structures.
Audit and compliance reporting: AI can help customize audit trails and compliance reports by analyzing regulatory requirements, internal policies, and stakeholder needs. It can suggest custom log formats that capture the most relevant data for specific compliance frameworks. AI can also help create tailored dashboards and reports that highlight the most critical compliance metrics for different roles within the organization.
Identity governance: AI identity governance can help create custom workflows for access reviews, suggesting different review frequencies or approval chains based on the criticality of access or the user’s role. AI can also help tailor risk-scoring models to better reflect the organization’s specific risk tolerance and security priorities.

Reducing false positives

A key challenge in traditional setups is the high rate of false positives in threat detection, which wastes time and resources but IAM AI and ML-driven approaches address this by dramatically improving detection accuracy. These advanced systems are trained on colossal datasets and can do an impeccable job of differentiating genuine threats from harmless anomalies. This not only streamlines operations but also enhances overall security effectiveness by ensuring quicker and more precise threat responses.

AI implementations in IAM

We have discovered just how far-reaching the benefits of AI can be. Now let’s see how it can be implemented in various components of IAM:

Privileged Access Management (PAM)

As discussed earlier, AI has the unique capacity to gather vast amounts of data from a diverse array of sources to detect anomalies in real-time. For instance, AI trained on acceptable and unacceptable behavior from session recordings can go so far as to terminate suspicious sessions and revoke access of the entity. This enables proactive threat mitigation for both human and non-human identities, such as automatically revoking suspicious access or triggering multi-factor authentication for high-risk activities. For NHIs in particular, it can be quite difficult to maintain a zero trust model and accurately assess the least amount of privilege they need to function, but with AI, policy creation can be automated at an organizational level.

Beyond threat detection, AI enhances PAM by optimizing access workflows. It can help create custom approval processes, and time-based access rules, or even suggest the creation of fine-grained privileged roles that align more closely with actual job functions, reducing the risk of over-privileged accounts. For non-human identities, AI can recommend least-privilege policies and identify dormant accounts for deprovisioning. Moreover, in complex multi-cloud environments, AI-driven PAM solutions have the ability to dynamically adjust access rights, and ensure consistent security policies across diverse platforms.

Identity Governance and Administration

Let’s face it, managing the lifecycle of non-human identities can be a real headache. But AI is stepping up to the plate, offering a more efficient solution, and streamlining the process of provisioning, monitoring, and decommissioning non-human identities.

With identity lifecycle management there’s always the risk of human error, but with AI-driven IGA systems, the whole process gets automated. All usage patterns are continuously analyzed, and the access rights get adjusted dynamically, reducing the need for manual intervention. And this automation backed by stringent policies can practically guarantee that all non-human identities maintain the least privilege throughout their lifecycle.

Secrets management is a major component of dealing with NHIs, and AI’s predictive capability comes in handy here. By analyzing historical data and current trends, we can forecast when a service account might require updating or when an API key is approaching its expiration date, or if high-risk secrets need more frequent rorations, we can ensure their timely maintenance.

Simulation of NHI attack patterns

Machine learning models can also be trained to recognize sophisticated attack patterns targeting non-human identities. And since these models evolve as they consume more data, adapting to new threat vectors isn’t a problem, and in fact, we can expect growing accuracy over time.

By simulating non human identity attack scenarios, the system can highlight weak points in the identity infrastructure before they can be exploited by malicious actors.

Secrets detection

Notably, advanced machine learning models now analyze contextual information, commit history, and code patterns through entropy levels, assessing the randomness and complexity of strings to identify potential secrets.

Entro takes this AI-powered approach further, extending secret scanning beyond code repositories to collaboration tools like Slack and Jira, DevOps platforms, and CI/CD pipelines. Our solution does an extraordinary job of comprehensively inventorying secrets, classifying each based on impact and exposure risk. Our AI continuously learns, adapting to new secret types and emerging threats. Real-time alerts and automated mitigation workflows accelerate incident response, while integration with major code repositories and secret vaults enhances overall security posture.

To learn more about Entro and how it brings AI into the fold of non human identity management, click here.