In today’s business environment, where everything is highly interconnected and globalized, the chances of cyber threats are also exceptionally high. By pressing a single button, we can get access to a vast range of information or get connected. Isn’t it interesting? No one has any barrier while collaborating. But do not forget, With the increase in globalization, cyber threats also increase. To prevent these cyber-attacks, organizations need to take proactive actions like implement Artificial Intelligence or Machine learning strategies. AI plays the most significant role in every field of life. Fortunately, it is no longer part of science-fiction; it is now all around us.

Al has significant importance in Cybersecurity and Identity and Access Management (IAM). One of the biggest challenges for an organization is to decide who should have access to what data set. Ironically, it leaves their systems vulnerable. Therefore, the importance of a mature Identity & Access Management (IAM) strategy cannot be over-emphasized. Keep in mind; smart IAM tactics indicate an instant correlation to minimize security risk, improvise productivity, highly privileged activity management, and extensively reduces loss over the organization’s less mature counterparts. This article plans to help you understand identity management in the Era of Artificial Intelligence (AI) and how to deal with non-human identities.

1. What Identity management exactly is?

It is the organizational process for identifying, verifying, and authorizing a person or group of people to have access to particular applications, systems, or networks.These are associated with user rights and limitations with established identities. Identity and Access Management solution vendors see AI and Machine learning approached as significant opportunities that make clear business sense. Sometimes it is difficult to determine the fraudulent activities in large organizations, but AI can identify unusual activities, outliers, or deviant cases that require additional investigations. AI helps managers to detect problems early in the cycle. Fraud detection is one-wayAI very useful in financial systems.

2. Identity & Access Management (IAM) Tools

In organizations, IAM systems allow administrators to change an employee’s role with IAM tools and technologies. They can track user activities, create reports on employees’ activities, and enforce policies on an ongoing basis. These systems specially designed to provide a means of user access across the enterprise and ensure agreements with corporate policies and government regulations. Identity and management technologies come with multiple tools, which include password-management tools, security -policy enforcement applications, provisioning software, reporting, and monitoring apps, and identity repositories. In this segment of the article, I would like to share a few identity management tools for better understanding. These IAM technologies come with low maturity, but have high current business value;

Customer Identity and Access Management (CIAM): This tool allows “comprehensive management and authentication of users; self-service and profile management, integration with customer relationship management (CRM), ERP, and other customer management systems and databases,” based on the report.

Identity as a Service (IDaas) – This tool includes “software-as-a-service (SaaS) solutions that provide Single sign-on (SSO) from a portal to web applications and native mobile applications, and user account provisioning and access request management.”

API security: It is a widely used tool that provides high security to the database. Therefore, it enables IAM to secure access. It has been used in B2B commerce, integration with the cloud, and microservices-based IAM architectures. API security solutions used for Single sign-on (SSO) between mobile applications or user-managed access. It allows the security team to manage various device authorization and personally identifiable data.

Identity analytics (IA) -This tool allows security teams to detect and prevent risky identity behaviors,  using rules, machine learning, and other statistical algorithms for security purposes.

Identity Management and Governance (IMG): It provides automated ways to govern the identity life cycle. It is essential when it comes to acquiescence with identity and privacy regulations.

Risk Base authentication (RBA): It takes in the context of a user session and authentication and establishes a risk score. Then organizations prompt high-risk users for 2FA and allow low-risk users to authenticate with single factor identifications.

3. IAM face challenging situations

As we know, an IAM system manages user identities. It ensures that users have access to the particular applications and data that they need. For example, IAM prevents junior sales representative access to information related to customers that are not assigned to them. Only Vice President of sales has access to examine the entire customer list. In real-time, IAM is becoming an increasing issue in every organization. One issue with IAM is that enterprises give access privileges to the employees based on their role in an organization, but employees rarely fit into single roles. They need exclusive one-time access or each worker that performs the same role might need different types of access on the database. By which very complicated situations occur that often require collaboration between many departments. Whenever management involves many employees across all layers of the organization, then people might suffer from “security fatigue.” The reason is that employees have to deal with a high amount of technical data and complicated decision-making processes in day-to-day Job activities. Terrible situations occur in the business when the administration is poorly managed in the IAM infrastructure.

4. How AI deal with challenging situations of IAM?

Well, the above mention situation is common in various organizations. It does not mean there is no solution to this issue. AI and Machine learning technologies significantly help to improve the IAM of any organization and conquer much frustration. Both technologies facilitate an organization’s technical access management. Analytics, combined with an AI system, offers focus and addresses insights so each worker, whether he/she is a technical or non-technical worker, works with ease. These technologies provide different ways to get new insights and automate processes and speed up the IAM system effectively. They detect variances and potential threats and prompt the security consultants to take immediate actions to prevent threats. This whole system provides each technical and non-technical worker appropriate knowledge. So, workers can make correct choices. AI and machine learning have been used in the area of anti-money laundering and fraud detection. These also fight against business executive threats. It leads the organizations to perform up to the mark with a continuous secure system for IAM. Therefore, IAM experts suggest that the IAM system must be strong enough to face the complexities of today’s challenging virtual world.

5. How to deal with fraud or non-human identities

An enterprise’s computing environment mainly used on-premises, and identity management systems authenticated and tracked users as they worked on-premises. IAM system enables an organization’s network to authenticate the identity of an employee against a set of pre-prescribed identifications. The system can range from simple username and password to digital certification and physical tokens, it depends on the system that is accessed. Most organizations used biometric ID and passwords that can range from fingerprints, iris scans, and facial recognition, or even authentication based on heartbeats. In this advanced world of technology, identity fraud is a growing menace.

Whenever data breaches occur, it is not the management that transfers identification to an unknown party. Artificial intelligence is the best solution that glue to bind them together to mitigate the effects. Move toward biometric passwords, AI could identify a user securely by using sight and sound. Now, machines have an AI system that makes them able to understand and confirm a user whom they claimed to be. These identification machines know when to grant access and act accordingly. They permit access based on machine learning. The hacker and fraudsters are actively looking for a weak IAM system and cybersecurity system of the organizations. To prevent hackersor threats, the enterprise needs to implement robust ID scanning solutions. They need to install reliable software that performs excellently and ensures that an ID is not fake. Artificial intelligence and machine learning makes it possible to appropriately process, verify, and authenticate users’ identities at scale.

6. Scale ID authentication with Machine Learning

Machine learning is a better processing system than an untrained human to look at the identity documents. Identity documents like driving licenses, passports, are scanned to verify multiple elements of an ID. For example, confirmation of original microprint text and security threads, barcodes, magnetic strips, data validity tests, biometrics to link the user to the ID identification. Machine Learning is a subset of Artificial Intelligence (AI). Using machine learning organizations can create an efficient and accurate process of user identification. The ML system contains tones of internal data mechanisms that can store information about the operations and software. The data is automatically transmitted to the user’s information and identifies them regularly. This entire process saves time and efficiently secure your crown jewels from an unauthorized person(s).

7. Multiple models of ID authentication

New datasets are fed into the algorithm to test outcomes. The process is called a feedback loop. By which organizations can test the results are consistent and improving or not. Then results are fed into the algorithm so that the software continuously learns and adjusts new data. There are various models of ID authentication include

Regression analysis: This ID authentication approach continually tests and analyses the results to improvise the algorithm.

Semi-supervised learning: Completely relying on automated machine learning can result in “failing” documents that have manufacturing errors.

Data mining: Investigating large databases to transform raw data into useful information. For efficiency, extract clean data to save time with this process.

8. Use Biometrics for Identity management

To strengthen the identity verification process, organizations implement Biometric identity verification methods such as facial or voice recognition. It is only possible if you installed proper AI or machine learning in your system. The biometric identification approach is not only right for the customer, but it is also making security protocol rigorous. A biometric security system mimics exactly as human neurons process and understand difficult information such as faces and language and identify a person. Correspondingly, deep learning technology software, understand a large amount of complex data. Facial-recognition technology is another approach that uses deep learning to learn to match the image on the ID to a user’s face. The algorithm looks for specific patterns, whether it is a basic shape (eyes. Mouth, nose) or complex shapes (Complete faces and distinctive shapes).


Artificial Intelligence (AI) and Machine Learning (ML) play an essential role in Identity and Access Management (IAM) in any organization. Some vendors already deployed AI and ML for IAM. In this article, I comprehensively described how AI has a significant impact on the Identity management system of an organization and how to deal with non-human identities. The fact is, AI is uniquely suited for cybersecurity and the IAM system because these have multiple connections.And a wide array of activities to monitor to prevent the threats earlier before the problem reaches danger level and challenging to overcome.


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