The threat to data security from outside has never been greater. The scope, number and frequency of data breaches are increasing year by year. The number of identities and activities requiring access is also growing rapidly, as is the amount of teleworking. As a result, the scale of threats from within is also growing. This widening of the threat landscape is compounded by factors such as new identity types (including the Internet of Things (loT), operational technologies (OT), and mobile access) and the transition to cloud and hybrid-based applications. With the growing threat of external and internal cyber risks, identity management is also becoming increasingly important with the aim of securing identities and access rights more securely. As a result, the regulatory and compliance landscape is also becoming increasingly complex and strict, as evidenced by the introduction of numerous new regulations and guidelines such as the Sarbanes-Oxley Act (SOX), the Health Insurance Portability and Accountability Act (HIPAA), the European Data Protection Regulation (DSGVO) and the Federal Information Security Management Act (FISMA). Existing IAM systems and IGA solutions (a subcategory of IAM systems) are not up to the increasing challenges and requirements on this scale.
As external and internal threats and the number of identities increase, the field of identity analysis continues to evolve to meet the challenges. An identity analysis solution that uses artificial intelligence (AI) and machine learning (ML) enables companies to quickly analyze large amounts of data and a wide range of activities. AI-supported analysis allows to identify access patterns of low, medium and high risk users across the enterprise. In addition, they can automate secure and low-risk decisions, creating room for risk and security teams to make nuanced risky decisions. This helps maximize resources and reduce access and security errors. By overlaying and integrating identity analysis with existing identity management and governance solutions, organizations can dramatically increase the efficiency and value of their IAM and IGA investments.
This white paper discusses developments in the area of external and internal cyber threats and the shortcomings of traditional IAM and IGA processes and solutions. It also describes how ForgeRock's real-time, AI-based Autonomous Identity solution provides continuous visibility, control and remediation of access rights across the enterprise.
As external and internal threats and the number of identities increase, the field of identity analysis continues to evolve to meet the challenges. An identity analysis solution that uses artificial intelligence (AI) and machine learning (ML) enables companies to quickly analyze large amounts of data and a wide range of activities. AI-supported analysis allows to identify access patterns of low, medium and high risk users across the enterprise. In addition, they can automate secure and low-risk decisions, creating room for risk and security teams to make nuanced risky decisions. This helps maximize resources and reduce access and security errors. By overlaying and integrating identity analysis with existing identity management and governance solutions, organizations can dramatically increase the efficiency and value of their IAM and IGA investments.
This white paper discusses developments in the area of external and internal cyber threats and the shortcomings of traditional IAM and IGA processes and solutions. It also describes how ForgeRock's real-time, AI-based Autonomous Identity solution provides continuous visibility, control and remediation of access rights across the enterprise.
