It is not surprising that the increasing complexity and number of identities held by organizations has been accompanied by a surge in identity-related security breaches. In fact, a 2023 report revealed that 90% of organizations reported a security incident related to digital identities over the previous year.
As organizations face growing challenges in identity and access management (IAM), it’s clear that a stronger approach is required to combat the spread of identity-based attacks. And while AI is part of the problem, it could also be part of the solution.
Understanding the Identity Challenge
Identity is arguably the largest attack surface for an organization, extending across locations, accounts and devices. This includes all resources that are accessed with user credentials, such as apps and programs that employees rely on for their day-to-day roles. In particular, the adoption of cloud applications and remote working has led to a proliferation of permissions and privileged identities that all need to be managed and secured.
The bottom line is, the more applications a business needs to operate, the more identities each user will have to access multiple accounts and devices. Every one of these access points creates a new identity attack vector that criminals can exploit. The large, disparate – and often disconnected – mix of on-premise and cloud applications has made it extremely difficult for organizations to effectively map identity and access data.
In other words, today’s work-from-any-where world has created the perfect storm for widespread identity attacks. In order to breach a vast network of systems and data, criminals only need to compromise the identity of a single individual, or a little noticed service account, through any one of these access points. And yet, less than 6% of organizations have full visibility across their service accounts.
The rapid advancement of artificial intelligence (AI) and machine learning (ML) has added further fuel to the fire in identity theft, whether it’s voice deepfakes or automated vulnerability scanning. But in the current threat landscape, we have an opportunity to fight AI – and other identity theft – with AI.
The Power of AI in IAM
Where traditional security approaches are no longer sufficient for identity security, AI-based solutions could hold the key. AI and ML are increasingly being integrated into security systems to comprehensively capture and analyze data and automate IAM decision-making – enhancing an organization’s threat detection and response capabilities while driving efficiencies in managing and controlling user identities and access.
AI can play a critical role in authentication processes, by analyzing factors such as device information, biometrics and user behavior to accurately verify the identity of users. In AI-protected IAM systems, privileges and roles are automatically assigned to users, based on the precise level of access required at a specific time.
A key benefit of advanced AI algorithms is their ability to continuously monitor identity and access activity to detect anomalies and atypical user behavior to adapt to changes and mitigate cyber threats in real-time. Suspicious activities or deviations – such an unusual login locations or multiple failed login attempts – trigger an immediate alert, and endpoints are quarantined as needed to prevent unauthorized system access.
What’s more, by analyzing large volumes of data and recognizing patterns and trends, AI and ML can also help an organization’s cybersecurity specialists anticipate future threats and take proactive measures to minimize risk.
Why Data Infrastructure Matters
When it comes to identity and access management, AI is only as effective as the underlying data structure. As organizations integrate AI into their IAM, foundational technologies – such as knowledge graphs and digital twins – are crucial to enable advanced AI capabilities in identity management.
Knowledge graphs effectively map intricate relationships between data points, helping visualize and manage complex networks of user identities, roles, permissions, and system resources. This provides AI with the context needed to make informed decisions around appropriate access control. Knowledge graphs also ensure high data quality and integration from various sources, which is fundamental to the accuracy of AI analyses. With built-in adaptability, they allow AI systems to scale and modify operations dynamically as new roles, rules and relationships are added.
Digital twins create a virtual replica of an organization’s IAM environment, enabling AI to simulate and predict potential changes in identity management – aiding with impact assessment and planning for future threats. Additionally, they support real-time monitoring and predictive analytics, so that AI can respond swiftly to anomalies or security threats – helping prevent breaches before they occur.
Both knowledge graphs and digital twin technologies deliver an ongoing stream of updated data that AI can use for continuous learning. This ensures its ongoing ability to optimize access controls and security controls to adapt to new threats and technological changes. Knowledge graphs also facilitate compliance with regulations by providing detailed, auditable data records – essential for transparency and accountability.
Together, these technologies can provide a powerful foundation for AI enablement in IAM solutions, bolstering security, compliance and efficiency in managing organizational identities. This creates a comprehensive, proactive and resilient AI-driven IAM ecosystem, underpinned by a dynamically updated model of related identity and access data, to ensure that the right individuals have the right access at the right time.
