FGA vs ABAC: Understanding the differences

Modern access control systems have to balance security and flexibility, especially as companies deal with large amounts of data and changing user needs. Two key methods for managing access are Fine-Grained Authorization (FGA) and Attribute-Based Access Control (ABAC). People often confuse the two, but they’re actually different approaches to controlling who gets access to what.

This article breaks down what each one is, how they differ, how they work, and how they’re used in today’s security systems.

What is FGA?

Fine-Grained Authorization (FGA) is a comprehensive framework for enforcing access control through multi-dimensional conditions that extend beyond basic user roles. It enables organizations to define permissions based on a combination of factors such as user attributes, resource properties, environmental context (e.g., time, location), and relationships between entities. For example, FGA could grant a marketing team member edit access to a project document only during business hours if they are assigned to Campaign X and accessing the file from a corporate IP address

FGA encompasses several underlying models, including Role-Based Access Control (RBAC), Relationship-Based Access Control (ReBAC), and Attribute-Based Access Control (ABAC), allowing it to adapt to diverse use cases. Its defining characteristic is the ability to evaluate dynamic policies in real-time, making access decisions that reflect current contextual data rather than static rules.

What is ABAC?

Attribute-Based Access Control (ABAC) is a subset of FGA that governs access exclusively through attributes associated with users, resources, actions, and environmental conditions. These attributes—such as job titles, data classification labels, or authentication methods—are evaluated against predefined policies to determine permissions. For example, ABAC might permit a user to view a financial report only if their role is "Finance Manager," the document is labeled "Internal Use," and the request originates from a secure network.

ABAC policies are rules that determine who can access something based on certain attributes. These attributes can be things like a user’s role, department, or location. ABAC policies are usually written in languages like XACML, a standard way to describe these rules. In XACML (eXtensible Access Control Markup Language), you can combine different attributes using logical operators like "AND" or "OR" to create more complex rules. For example, you could say, "Allow access if the user is in the 'HR' department AND their security clearance is 'Level 3'."

ABAC differs from Fine-Grained Authorization (FGA) because it doesn’t automatically handle hierarchical relationships (e.g., who owns a specific folder or document). In ABAC, if you want to include those kinds of relationships, you have to define them as attributes in your rules specifically. So, if you wanted to say, "only the owner of the folder can access it," you would need to explicitly include an attribute that identifies the folder owner in your policies. ABAC itself doesn’t assume those relationships exist unless you make it clear in the rules.

Differences between FGA and ABAC

When comparing FGA with ABAC, FGA will almost always come on top. This makes sense since FGA includes all the best features of ABAC without its restrictions and shortcomings. Let’s see the two models differ.

Scope

The primary distinction between FGA and ABAC lies in their scope.

FGA operates as a meta-framework that integrates multiple authorization models (ABAC, ReBAC, RBAC) to address complex scenarios. For example, a healthcare FGA system might use ReBAC to grant doctors access to patient records they’re assigned to, while simultaneously applying ABAC policies to restrict record modifications to weekdays between 9 AM and 5 PM.

ABAC functions as a specialized model within FGA, focusing solely on attribute-driven policies without native support for relationship-based logic. If an organization using ABAC needs to enforce permissions based on resource ownership (e.g., "users can edit documents they created"), it must explicitly define "creator" as a user attribute and "creation date" as a resource attribute—a less intuitive approach compared to ReBAC’s direct handling of relationships.

Complexity and maintenance overhead

ABAC can be difficult to manage because organizations need to track and update all the relevant attributes across their systems. For example, a large company might define attributes like employee locations, clearance levels, and project roles, requiring constant updates between their IAM (Identity and Access Management) tools and department databases. If there’s a mismatch in how attributes are defined (like different meanings of "seniority"), it can lead to access mistakes or employees getting too much access.

FGA systems make this easier by using modular policies, so teams can combine ABAC with other models when needed. For example, a retail platform could use RBAC for basic roles like inventory management, ABAC for discount approvals (e.g., "managers with 5+ years can approve markdowns over 20%"), and ReBAC for store-specific access—all within one FGA system. However, combining these models requires careful management to avoid conflicts.

Performance and scalability

ABAC can cause slowdowns in high-demand environments because it relies on multiple sources to check attributes. For example, a cloud service handling 10,000 access requests per second might experience delays if each request has to query multiple services like LDAP, CMDB, and geolocation. While caching and aggregation tools (like Okta Universal Directory) can help reduce delays, they add complexity to the system.

FGA systems using ReBAC or PBAC (Policy-Based Access Control) often perform better than pure ABAC in large-scale environments. For instance, Google’s Zanzibar manages trillions of permissions for services like Drive and YouTube by storing user-object relationships (e.g., user:1234, viewer, document:5678) and quickly checking them using optimized graphs. This method speeds up the process but requires significant effort to set up and maintain the relationship data and infrastructure.

Security

ABAC improves security by allowing context-based restrictions that traditional RBAC can't enforce. For example, a bank using ABAC could block transactions from unrecognized devices or prevent transfers over $10,000 outside business hours. However, ABAC's effectiveness relies on up-to-date and accurate data—outdated employee information or misclassified resources can create security risks.

FGA’s use of multiple models allows for a stronger security approach. For example, a healthcare app might combine:

• ReBAC to manage patient-provider relationships
• ABAC to limit access to sensitive diagnoses based on clinician specialties
• RBAC to give administrative access to hospital IT staff

This layered approach reduces the impact of compromised credentials but requires careful auditing to make sure all policies work together.

Compliance

Both models help with compliance to standards like GDPR and HIPAA, but in different ways:

• ABAC is great for data-focused compliance because it links access directly to resource attributes (e.g., "Only users with the 'GDPR-Compliance-Officer' role can access EU customer data").
• FGA helps adapt to different regions by using environmental conditions. For example, a global SaaS platform could use FGA to enforce region-specific rules, like blocking data exports from countries with trade restrictions.

What the future holds

Modern IAM solutions are increasingly combining ABAC and ReBAC under FGA. For example, Okta Fine-Grained Authorization lets you create policies that use both attributes (like user.department == 'Finance') and relationships (like document.owner == user.id). Similarly, OpenFGA (an open-source version of Zanzibar) can store attributes as relations, allowing ABAC-like logic to work within a ReBAC system. WorkOS FGA was built from the ground up with the Zanzibar ReBAC model in mind, layered with policies and context to solve for the use cases that policies handle better.

Additionally, some systems are using machine learning to improve access control. For example, an e-commerce platform could analyze user behavior, such as how often they log in or abandon their cart, to adjust product visibility in real-time—combining ABAC's attribute checks with FGA's dynamic adjustments.

As digital ecosystems grow more interconnected, the fusion of these models—powered by advances in centralized policy engines and real-time data pipelines—will define the next generation of access control systems.