Enforce Governance and meet Compliance for a zero-trust, least-privilege security posture
Least privilege is no longer a best practice—it is a baseline expectation for modern security, compliance, and identity programs. Yet implementing least privilege at scale remains challenging due to dynamic roles, tribal knowledge, and entitlement sprawl. Birthrights provide a strong foundation, but they must be complemented with need-based access, exception governance, and intelligence-driven automation. This guide outlines a practical, layered approach to entitlement management—grounded in real-world IAM operations—that enables CISOs and identity leaders to reduce risk, improve audit posture, and deliver secure access aligned with how work actually happens.
Least privilege is no longer optional in today’s security landscape. At the same time, implementing least privilege is not a single switch—it is a journey with multiple paths to the same goal: ensuring every user has exactly the access they need, no more and no less. At its core, this reflects the fundamental organizational principle of “need-to-know” access and entitlements.
One of the most common and foundational approaches to achieving least privilege starts with birthrights.
Birthrights are the baseline access and entitlements a user should receive based on their role, function, or need-to-know within the organization. This approach is intuitive and easy to explain, but not always easy to execute. Defining birthrights requires strong rule-based logic, documentation of tribal knowledge, and organization-wide alignment on what constitutes “default” access for a given role or profile. When done well, birthrights form the backbone of identity governance—but they are only the beginning.
Birthright access refers to the baseline set of entitlements automatically granted to a user based on their role, function, or organizational context to support least privilege access.
Beyond security and risk reduction, well-defined birthrights significantly improve the employee experience. A new hire or joiner can hit the ground running on day one, delivering a “superior first day at work” experience. For movers—employees transitioning between roles—birthrights ensure a smooth transition without scrambling for access needed to perform effectively in the new role.
Beyond birthrights, organizations often grant access based on specific needs, which may be temporary (time-bound) or permanent. Examples include short-term project assignments, acting roles, or employees taking on multiple responsibilities that do not neatly fit into predefined roles. While these entitlements may resemble birthrights in practice, they are often difficult to model using static role definitions and purely rule-based frameworks.
In reality, many organizations also rely on ad-hoc access grants—exceptions that bypass formal role structures altogether. While sometimes unavoidable, these exceptions can undermine least privilege if left unmanaged and unreviewed, leading to over-privileged users, entitlement sprawl, and increased risk.
In practice, organizations typically adopt following approach while striving to adhere to the least privilege paradigm.
This is the classic and well-understood approach to assigning privileges based on:
For example, a manager in the Sales department may be assigned a “Sales-Manager” entitlement, which automatically grants the appropriate license, role, and permission set in Salesforce.
On paper, this approach appears prudent, practical, and mature. However, it commonly suffers from several challenges:
As a result of these challenges, system administrators frequently resort to a shortcut: finding an existing employee with a “similar” role and copying their entitlements. While expedient, this practice leads directly to entitlement sprawl and compounds the problem over time.
While rule-based assignment sounds ideal, it is often difficult to achieve in practice. This is where machine learning (ML) and artificial intelligence (AI) offer a more scalable and pragmatic alternative.
Modern Identity, Governance and Administration (IGA) platforms increasingly leverage ML and AI to mine existing access patterns, usage behavior, and peer comparisons. This enables organizations to discover implicit roles, infer undocumented birthrights, and reduce the administrative burden of manual role engineering.
AI and ML models can also continuously evaluate whether assigned entitlements are actually being used – surfacing unused access, identifying over-privileged users, and helping organizations move closer to true least privilege over time.
Some common AI/ML-driven approaches include:
Instead of manually documenting tribal knowledge, machine learning models can mine entitlement assignment rules directly from existing data in the Identity Provider (IdP). These inferred rules can then be incorporated into deterministic rule sets within an Identity Governance and Administration (IGA) system to support Joiner, Mover, and Leaver processes.
Key considerations:
Machine learning can also emulate the behavior of a human system administrator by identifying peer groups with similar profiles and access patterns. When a new identity is created (Joiner) or an employee changes roles (Mover), the system can recommend entitlements based on statistically relevant peers.
Unlike manual copying, ML-driven peer analysis can be constrained by:
This results in more consistent, defensible, and policy-aligned access decisions.
Access requests remain a good old common mechanism for granting access to meet specific or ad-hoc business needs. These requests may be permanent or time-bound and, when governed through a well-defined approval process—often integrated with Service Desk or ITSM workflows—can provide a controlled and auditable way to grant necessary access while adhering to least privilege.
The application of machine learning and AI for continuous monitoring further strengthens this model by detecting over-entitlement, toxic access combinations, and unused privileges, ensuring that manual access grants do not evolve into long-term security risks.
In practice, effective least privilege is rarely achieved through a single method. It emerges from a layered strategy that combines well-defined birthrights, need-based access, controlled exception handling, and intelligent automation and feedback loop powered by machine learning and AI.
Organizations that succeed with least privilege recognize that access models must evolve alongside how work actually gets done. By blending deterministic rules with data-driven intelligence and lifecycle-aware governance, enterprises can reduce risk, limit entitlement sprawl, improve employee experience, and continuously move closer to the true least privilege—without slowing the business down.
Want to see how Hire2Retire—a leading lightweight IGA platform—enables birthright access and least privilege entitlement management through no-code automation and an intuitive, adaptive user experience?
Bramh Gupta is the founder and CEO of RoboMQ. He has a background in large scale real-time manufacturing systems, telecommunications and design and architecture of highly scalable and resilient enterprise systems. He is passionate about real-time integration and the value that it brings to business operations and critical decision making.
Bramh holds an MBA from the Kellogg School of Business and Industrial Engineering degree from the National Institute of Technology, Jamshedpur. Bramh combines his business insights and architectural skills to design and create highly scalable, integration platforms and tools that are needed to power the API economy.
Bramh Gupta is the founder and CEO of RoboMQ. He has a background in large scale real-time manufacturing systems, telecommunications and design and architecture of highly scalable and resilient enterprise systems. He is passionate about real-time integration and the value that it brings to business operations and critical decision making.
Bramh holds an MBA from the Kellogg School of Business and Industrial Engineering degree from the National Institute of Technology, Jamshedpur. Bramh combines his business insights and architectural skills to design and create highly scalable, integration platforms and tools that are needed to power the API economy.
