In 2024, cybersecurity threats evolved at a rate never seen before, reflecting the inefficiencies of the traditional Identity and Access Management IAM platforms. A popular report says, 2024 saw nearly double the data breaches from 2023 costing around $ 4.88 Million. Compromised credentials and privilege abuse are the leading causes of data breaches.
Even after IAM solutions in place, organizations are under the radar of continuous cybersecurity attacks and threats. We can clearly see that traditional IAM solutions are dealing with issues they were not designed to handle.
To advance IAM solutions, technology like Artificial Intelligence AI and Machine Learning ML are integrated. With the help of AI and ML, IAM solutions are continuously learning from the environment and are helping maintain a balance between security and efficiency. This way identities are governed effectively and privileged accesses are aligned with security policies to transform identity security for businesses.
Let us dive deep into IAM integrating with AI/Ml starting with challenges faced by traditional IAM solutions, transformation with the adoption of new tech, benefits, byproducts, and limitations.
Challenges Faced by Traditional IAM solutions:
Traditional IAM solutions involve a centralized identity management system, access control, password or hardware tokens-based authentication, and more. These days they struggle to handle hybrid or cloud-based environments, limited integration, static access control, and complex maintenance structures. Not just this, here is an extended list of challenges:
Predefined rules and policies are a major component of legacy IAM systems. These kinds of rules often lead to restrictive access controls.
Manual Identity Lifecycle management not only increases the risk of errors but also leads to delays in granting and revoking access.
Being unable to react quickly enough to complex attacks including account takeovers, insider threats, and credential stuffing. These systems are often prone to Zero-day attacks.
Have difficulty adjusting to the dynamic environment of businesses, where devices, apps, and users are ever-changing.
Often find it difficult to process and analyze the massive volumes of data produced by contemporary businesses, including device activity, user behavior, and access logs.
IAM Transformation through AI/ML Integration
The above challenges have forced the AI/ML integration with IAM. This advancement has led to improved capabilities along with better user experience, efficiency, and security. The following are the number of ways how integration is transforming the IAM solutions:
1. Behavioral Analytics to Find Anomalies -
Behavioral analytics are used by AI-powered IAM systems to create a baseline of user activity. ML algorithms keep an eye on things like usage trends, access requests, and login behaviors.
The best example of this can be seen in the healthcare industry where a system using AI, spotted an administrator accessing patient records outside their usual workflow, preventing a potential insider threat by trigging an additional verification step
This helps the system to flag a user as suspicious and initiate extra verification procedures when a difference in activity is noticed.
2. Access Control Based on Risk -
AI evaluates risk indicators in real time, allowing for dynamic, context-aware access decisions. This includes user behavior, device kind, location, and access time allowing adaptive authentication which offers access according to risk levels instead of rigid standards
3. Automating Identity Lifecycle Management -
AI algorithms have been integrated into Identity Governance and Administration (IGA) to automate identity lifecycle management. It helps identify and classify user roles, permissions, and entitlements more accurately hence automating the processes like provisioning, deprovisioning, and access reviews. Thus reducing administrative overhead and improving accuracy in managing user identities.
AI algorithms detect suspicious activities in privileged accounts, strengthening access control within an organization's IT environment. Advanced PAM analyzes privileged user behavior, session recordings, and system logs to potentially identify unauthorized access attempts or misuse of privileged credentials. As a result, a system can automatically terminate suspicious sessions, revoke excessive privileges, and notify security teams to investigate further.
5. Passwordless Authentication:
AI and ML are leading the way in enabling password-less authentication methods, such as biometrics and device-based credentials. According to Gartner, by 2025, 60% of big businesses will completely abandon traditional passwords leading to a seamless and password-less user experience with improved security.
Potential Outcomes
Yes, we know AI/ML together with IAM are moving ahead to be at the front door of cybersecurity threats and breaches. There are a lot of potential outcomes such as
Reducing Cost: The Significance of AI in IAM for Cost Savings Automated procedures increase productivity while reducing operating expenses.
Faster Threat Detection: AI cuts the time it takes to find breaches from months to hours (from 280 days to hours, as per IBM data).
Better Compliance: AI-driven analytics provide thorough audit trails, which facilitate compliance with laws like GDPR and HIPAA.
Limitations of AI in IAM:
The potential outcomes do seem fascinating and exciting but there are some concerns associated with these advanced tech stacks like
Data privacy: How are AI models trained with sensitive data?
Algorithms without bias: AI judgments need to be impartial and equitable.
Scalability: The solutions must be able to expand with the company.
Conclusion
Over the years, IAM has been saving organizations from cybersecurity threats and attacks. However, technological advancement has proved that they are not enough.
The alarming need for AI / ML integration with IAM solutions is highly needed to overcome significant challenges. These technologies will help IAM to become a proactive intelligent system. From better threat detection and real-time decision-making to greater user experience and operational efficiency.
With these advanced solutions centralized identity governance is made possible by AI-driven IAM solutions, which offer smooth integration with a variety of platforms. Additionally, AI can control machine identities, guaranteeing safe device-to-application connection, which leads to drastically improved security posture, reduced expenses, and increased productivity.
As artificial intelligence learns from the evolving environment similarly these attacks and breaches are also evolving at a pace never seen before. Thus organizations must adopt these technological updates to keep their digital ecosystem safe, sound, and secure from the ever-evolving future landscape.
To keep your organization safe, and secure from the present and upcoming cybersecurity threats, attacks, and breaches book a FREE CONSULTATION session with us at IDMEXPRESS
Sources:
IBM Cost of Data Breach Report 2024
Verizon Data Breach Investigations Report 2024
Gartner Research on Passwordless Authentication Trends 2023