Understand where AI can create identity and access management risks in various scenarios

Artificial intelligence (AI) has become an indispensable tool in various industries, including identity and access management (IAM), as technology advances. IAM systems, which govern users' digital identities and access to resources, have benefited from AI's capabilities in enhancing security and efficiency. In this article, we will explore AI's potential risks in identity and access management and ways to mitigate them.

IAM systems backed by AI offer several benefits in three significant aspects: authentication, identity management, and secure access.

Digital identities 

Digital identities may have had a chance to function effectively without artificial intelligence, where they might be automatically trusted. However, in the AI-driven future, digital identities are losing their credibility and becoming fundamentally unreliable.

Artificial intelligence as an identity

Identity verification solutions have become quite powerful. They decrease the wait time for access requests, control the countless login attempts, and, of course, employ AI. However, in theory, all verification methods rely on the same assumption: authentic identification.

Vulnerabilities in AI Algorithms:

One of the primary risks of using AI in IAM lies in the vulnerabilities of AI algorithms. AI models are susceptible to adversarial attacks, where malicious actors attempt to deceive the system by providing inputs crafted to mislead the AI. If attackers succeed in manipulating the AI model, they could bypass authentication processes, granting unauthorized access to sensitive data. To mitigate this risk, continuous monitoring, regular updates, and rigorous testing of AI algorithms are essential to identify and patch vulnerabilities.

Overreliance on AI:

While AI can significantly improve IAM efficiency, overreliance on AI could pose risks. Organizations may become overly dependent on AI systems for access decisions, neglecting the importance of human oversight and intervention. In cases where the AI model encounters novel scenarios or adversarial attacks, human judgment and expertise become indispensable. Maintaining a balance between AI-driven automation and human supervision is essential to ensure the effectiveness and reliability of IAM processes.

Reduce the attack surface by managing the data privacy and biased data:

Data Privacy and Security:

AI in IAM demands collecting and analyzing vast amounts of personal data, including biometrics, behavioral patterns, and user activity. The increased collection and processing of sensitive information raise concerns about data privacy and security. This data could be compromised if not adequately protected, leading to identity theft, fraud, or unauthorized access. Robust encryption, secure data storage, and strict access controls are crucial to safeguarding sensitive user information.

Bias and Discrimination:

AI systems rely heavily on historical data for learning, and if the training data used to build IAM models contain biased information, it can lead to discriminatory outcomes. This bias could disproportionately affect certain user groups, leading to potential discrimination in access decisions. Organizations must implement measures to address bias during data collection, model training, and ongoing monitoring to ensure fairness in access decisions.