1 Introduction
In today’s digital environment, organizations are managing vast amounts of data and operating across complex, often global, networks. Remote work, cloud services, and the increasing adoption of third-party services have led to a massive increase in access points within organizations. This shift has made traditional perimeter-based security models, which focused on protecting network boundaries, largely obsolete. As a result, the emphasis has moved towards identity-centric security, where access governance plays a central role.
Access Governance (AG) is an IAM focused risk management discipline that facilitates overall management of access rights across an organization’s IT environment. AG provides necessary (mostly self-service) tools for businesses to manage access entitlements, run reports, access certification campaigns, and Segregation of duties (SoD) checks. Access Governance Prescriptive Analystics refers to the layer above access governance that offers business-related insights to automate approvals, support effective decision making, remove rubber stamping, and potentially enhance compliant access governance. Data analytics and machine learning techniques enable pattern recognition to deliver valuable intelligence for process optimization, role design, automated reviews, and anomaly detection.
Access governance involves defining, managing, and enforcing policies on who can access what resources, under what conditions, and for what purpose. Effective access governance requires comprehensive identity and access management (IAM) systems that allow organizations to manage user identities, automate provisioning, enforce least-privilege access, and monitor access activity. The aim is to ensure that access to resources is secure, controlled, and continuously aligned with both business needs and regulatory requirements.
Many organizations today operate in hybrid environments that combine on-premises infrastructure with cloud-based systems as well as operating multiple cloud platforms. This mixed environment complicates access governance because each platform may have its own access control mechanisms, requiring interoperability and consistent policy enforcement across diverse systems. As organizations expand, so do their user populations, which now often include not only employees but also contractors, partners, and sometimes customers. Each of these groups requires specific access levels, making it essential to manage identities and permissions effectively. Furthermore, the rise of IoT devices has introduced a multitude of non-human identities that also require access governance.
The zero trust architecture and other compliance as well as regulatory requirements such as GDPR, HIPAA, and CCPA place stringent demands on organizations to manage access to sensitive data, enforce privacy controls, and maintain audit trails. Failure to meet these requirements can result in legal penalties, making compliance a major driver of access governance efforts. Meeting these demands requires continuous monitoring and reporting capabilities, which can be challenging without the right tools and processes.
Manual access management processes are not sustainable, especially in large organizations. Automated access provisioning, deprovisioning, and certification is essential for effective access governance. However, automation itself can be a challenge to implement effectively, particularly in organizations that have legacy systems or lack centralized identity management. Modern access governance solutions leverage advanced technologies such as artificial intelligence (AI) and machine learning (ML) to streamline access management processes and identify unusual or risky access patterns. These tools can automate routine tasks, such as provisioning and deprovisioning, password sync, periodic and event triggered certification campaigns, and can also provide valuable insights into access behaviour, helping organizations identify potential risks before they escalate.
Another key component is advanced role management, which extends beyond basic role assignments to include advanced functions such as role modelling, where Artificial Intelligence (AI) and Machine Learning (ML) supported approaches can be leveraged to create and refine role structures that reflect the dynamic needs of the organization. This also includes managing the entire role lifecycle, from creation to deletion, as well as ensuring that ownership of roles is clearly defined and managed throughout the organization. The ability to support multi-tier role models allows for more granular and hierarchical role structures that align with complex organizational needs. Role mining is another critical function that can be enhanced by AI and machine learning. This enables the identification and optimization of roles based on user behavior patterns, making the governance model more efficient.
The landscape of Access Governance (AG) is at a critical juncture where evolution is not just necessary, but inevitable. At its core, AG has traditionally been implemented to address the challenges posed by applications that rely on static entitlements. There needs to be an approach to review entitlements that are entered into systems like Microsoft Active Directory or SAP ECC. These static entitlements create significant governance challenges because they require constant oversight to ensure compliance to the principle of least privilege, which is essential to avoid over-entitlement and enforce Segregation of Duties (SoD) controls. The need for these controls arises from the static nature of entitlements that, once granted, remain in place until they are manually reviewed or revoked.
In this KuppingerCole Executive View report, we take a look at the latest advancements in Oracle Access Governance and how these capabilities can affect real life use case scenarios.
