A Full Guide to Planning Your Authorization Model and Architecture

Every application—whether SaaS, API-driven, or AI-powered—needs an authorization model to run securely in production. Building one that balances security, flexibility, and usability, on the other hand, is much less straightforward and might often be harder than it looks.

Authorization isn’t just about deciding who gets access to what.

It’s about enabling collaboration, supporting complex workflows like approvals, and even handling machine identities like AI agents—all without slowing your team or product down.

This guide aims to serve as a general guide to navigating the key considerations in planning authorization - be it for a new app or a revamp of an existing system.

We’ll walk through real-world use cases and learn how to design an authorization architecture that supports user management, approval flows, operational controls, and future growth.

By the end, you’ll have a solid framework to plan your authorization model and architecture—built for the realities of modern applications in prod.

TL;DR

This guide walks you through how to model and implement an authorization system, covering:

• What you’re protecting
• Who your users are
• What access decisions are based on
• Who manages policies
• How to ensure auditability and compliance
• How to future-proof your system

This guide is quite long, and might be a bit overwhelming - but it’s not going anywhere. Keep it for the time you'll need it.

Authorization Model vs. Authorization Architecture

Before we get into planning, it’s important to clarify two terms that often get used interchangeably but mean different things: Authorization Model and Authorization Architecture.

The Authorization Model — What Rules Decide Access?

Your authorization model defines the logic that determines who can access what under which conditions. It answers questions like:

Can users access resources based on their role or ownership?
Does access depend on external factors like time, location, or project status?
Can certain actions require approval before they’re allowed?

Models like RBAC (Role-Based Access Control) and ABAC (Attribute-Based Access Control) exist as general thinking tools rather than concrete guidelines, and most applications end up mixing between them (especially as time passes and the applications evolve).

The specific model you choose matters less than what it unlocks: collaboration, fine-grained control, or dynamic conditions.

For most teams, the real question is:

“What kinds of access control do we need to support the product’s workflows and user experience?”

If you do want to get into the more technical aspects of the capabilities of every authorization model, check out “Authorization Policy Showdown: RBAC vs. ABAC vs. ReBAC”.

Authorization Architecture — How Is Access Control Implemented?

Your authorization architecture is the system and infrastructure you build to enforce the model:

• Where are policies stored and evaluated?
• Is the decision made at the API layer, in middleware, or inside services?
• Can you support real-time context like geo-location or business rules?
• Do you support humans and machines (AI agents, services, bots)?

This architecture shapes how easy it is to maintain, audit, and evolve your access control as the product grows. A good design helps you avoid performance bottlenecks, centralize policies (or distribute them, if needed), and build flexible features like approvals and temporary access.

Both the model and the architecture work together to achieve one goal:
Enabling secure, flexible, and scalable access control tailored to your product’s needs.

What Are You Protecting?

The first step in planning your authorization model and architecture is understanding exactly what you’re protecting. This may sound obvious, but many teams skip straight to designing roles or writing policy logic without clearly mapping out the resources and operations that require protection.

Authorization isn’t just about protecting your database. It’s about guarding anything in your system that a user—or another system—might interact with. That includes:

Data and Digital Assets

For most applications, data is the most obvious thing to protect. This could be user profiles, financial records, design files, or even AI-generated replies.

Take a design collaboration platform, for example, where users work together on projects that include large design files. Access to these files shouldn’t just depend on whether someone is logged in—it should depend on their relationship to the project.

This means we might need a policy such as:

Only project collaborators or the project owner should be able to view or edit these designs.

Modeling this kind of policy requires a system that understands user-to-resource relationships. A ReBAC (Relationship-Based Access Control) approach works well here, where permissions are dynamically derived based on project membership. As users are added or removed from a project, their access to associated assets updates automatically, without requiring manual policy changes.

Actions and Operations

Beyond protecting static data, your system also needs to control what actions users can perform. Some operations carry more weight than others—like triggering billing cycles, publishing content, or deleting entire projects. In a SaaS app that handles subscriptions, you might have a policy stating:

Only users with the Billing Admin role can initiate the monthly billing process.

This is a classic example of role-based access control (RBAC), where the system needs to check not just who the user is but also what role they hold. Implementing this cleanly means separating billing permissions into a dedicated role and enforcing them via API guards or middleware that validates the user’s role before allowing the action.

APIs, Services, and Microservices

As applications grow more complex, much of the access control shifts away from user-facing interfaces and into the world of APIs, services, and machine-to-machine communication. A good example is an AI service that processes documents for summarization.

If a customer uploads a document marked “Confidential,” the AI agent needs to recognize that classification and refuse to process it unless explicit consent has been granted.

This is where context-aware policies become critical—your system must support dynamic, runtime evaluations based on metadata like classification levels or user-provided consent tokens.

The AI agent effectively becomes an actor in your authorization model, making its own access decisions based on policy responses.

Business-Critical Operations

Some actions in your application carry significant business risk or compliance requirements and require much more than a simple role check. Publishing financial reports, launching marketing campaigns, or initiating large payments often involve multiple stakeholders and approval flows.

Imagine a marketing platform where publishing a new ad campaign requires approval from both a Marketing Lead and a Compliance Officer.

This kind of operation needs policy logic that supports multi-party approval, tracks approval status, and enforces order—Marketing may approve first, but Compliance must give the final green light. Modeling this requires moving beyond static roles or attributes and introducing workflow-aware policies and approval gates, where access is granted only after the necessary sequence of approvals is completed.

The Bottom Line

Your authorization layer is about far more than just "Can this user read or write this record?" It's about modeling your application's real-world logic. The clearer you are about what you're protecting—and why—the easier it becomes to choose the right authorization model and architecture to enforce it cleanly and securely.

Who Uses Your Application? What Types of Access Do They Need?

When planning your authorization model, it’s tempting to think about “users” as just your end customers—people logging into your app, clicking around, and using features. But in reality, the list of actors who interact with your application has expanded dramatically.

Multiple layers of users, both human and machine, each have different needs, expectations, and required levels of control. Each group comes with its own set of requirements, risks, and workflows that shape your access control decisions.

A very detailed overview of this subject is available in our blog: “Best Practices for Effective User Permissions and Access Delegation”. Here’s a bit of a summary:

End Users

End users are your core customers—the people signing up, logging in, and using your product. Their access needs can vary based on subscription tiers (free vs. premium), personal ownership of data, or collaboration permissions. Many apps also support delegated access, allowing users to invite others into projects or share resources. This introduces complexity around who owns what and who can modify shared assets.

To support these use cases, your authorization system must support Relationship-Based Access Control (ReBAC) for ownership models, RBAC, and ABAC for role and condition based access, as well as no-code approval flow/operation approval elements.

Internal users

Internal users—such as customer support agents, account managers, or operations staff—need specialized access to assist customers or manage accounts. These users often need elevated but scoped permissions and sometimes temporary access for debugging. Your system must support fine-grained roles, impersonation flows, and time-bound or just-in-time access, all while ensuring comprehensive audit logging for accountability.

Organizational Stakeholders

Managers, compliance officers, finance teams, and executives interact with the system for approvals, oversight, and decision-making—not for daily operations. These users need access to specific workflows or resources but only at certain points (e.g., when approval is pending).

To support these needs, your authorization model should enable multi-tenant isolation and real-time conditional access based on resource state (e.g., pending approval, submitted, draft).

Developers

Your developers, DevOps, and SREs interact with APIs, infrastructure, production data, and debug tools. The system needs to isolate environments (prod vs. staging), enforce least-privilege principles, and enable temporary escalations for incident response—all while maintaining detailed audit trails and avoiding accidental data leaks.

Non-Human Actors: AI Agents, LLMs, and Automated Systems

Modern applications increasingly rely on non-human actors—AI agents, LLMs, webhooks, and automated systems—that perform critical tasks, make decisions, and interact with sensitive data. These actors don’t just need access control; they require security across the full AI interaction lifecycle—from inputs to actions, data access, and outputs.

This means they need to be able to handle:

• Prompt Filtering (Input Control)

  AI agents and LLMs process both user prompts and system-generated inputs. Your system must enforce real-time, attribute-based policies that validate and authorize prompts based on user role, subscription tier, or other context—blocking problematic inputs before they reach the model.

• RAG Data Protection (Knowledge Base Access)

  When AI agents pull data from retrieval-augmented generation (RAG) systems or vector databases, they must respect fine-grained access controls. Your authorization layer must enforce pre-query filtering (who can access what) and post-query sanitization (what is returned).

• Secure External Access (Action Control)

  AI agents don’t just read—they act. Whether making API calls, modifying accounts, or triggering workflows, each action must be scoped, auditable, and tightly governed by policy. Machine identities allow you to define permitted operations and trigger human approvals for high-risk actions.

• Response Enforcement (Output Control)

  Finally, the system must inspect and control AI-generated responses before delivering them to users. Output filtering prevents the accidental exposure of sensitive information or unsafe content, ensuring AI responses are safe, compliant, and aligned with business requirements.

Who Authors and Manages Policies?

An often overlooked but critical question is who actually writes, manages, and updates your policies. Many teams default to thinking it’s a “developer-only” task buried in code or configuration files. But as your product, team, and compliance requirements grow, this approach breaks down fast.

Policy Authoring Isn’t Just for Developers

While engineers may define initial access control logic, they’re rarely the best long-term owners of authorization policies—especially when those policies enforce business rules, compliance requirements, or workflow approvals. Product managers, security teams, and compliance officers often understand the why behind a policy better than the dev team. Yet, without the right tools, they’re stuck waiting on engineering to make critical changes.

Bringing in Product, Compliance, and Security Stakeholders

Effective access control often involves stakeholders from multiple teams:

• Product managers define collaboration models, feature access, and approvals.
• Compliance teams enforce regulatory requirements like GDPR or HIPAA.
• Security teams monitor sensitive operations and ensure least-privilege access.

Your system should allow these teams to co-own and influence policy creation rather than forcing everything through engineering bottlenecks.

Low-Code / No-Code Policy Management vs. Policy-as-Code

Modern authorization systems need to support both:

• Policy-as-Code for engineers managing complex logic, version control, and CI/CD workflows.
• Low-code or no-code interfaces for non-technical teams to define or update business logic, approval flows, or temporary access requirements—without needing to open a pull request for each change.

Versioning, Testing, and Rollback

Authorization policies—like application code—must be versioned, testable, and reversible. A simple typo or logic change can lock out users or expose sensitive data.

Your system should support:

• Policy previews and impact analysis
• Testing environments
• Automatic rollback mechanisms if something goes wrong
• Approval workflows before critical policy changes go live

Centralized vs. Distributed Policy Enforcement

Finally, consider where your policies live and how they’re enforced:

• Centralized policy engines simplify audits, testing, and consistent enforcement but may become bottlenecks or single points of failure.
• Distributed enforcement (embedded checks in microservices) improves performance and resilience but risks fragmentation and policy drift.

Most mature architectures combine both—centralized decision-making for sensitive operations and distributed enforcement for speed and scalability.

The Bottom Line

The teams who write your policies should be the teams responsible for the outcomes—whether that’s user experience, compliance, or security. Your architecture should empower them to author, test, and manage policies safely, without depending entirely on engineering cycles.

What Are Access Decisions Based On?

Once you know who your users are and what they’re trying to access, the next critical question is: What factors does your system consider when making access decisions?

For many teams, access control starts and stops at static roles—like Admin or Editor. But that’s rarely enough anymore. Real-world access decisions often depend on dynamic context, external systems, workflow state, and human approval for sensitive operations.

When designing your model and architecture, you must take these factors into account upfront:

Dynamic Context and External Data

Applications often need to go beyond "user has X role" and make contextual decisions based on real-time data, including:

• User attributes: department, seniority, subscription tier
• Resource state: draft, submitted, under review, archived
• Environmental context: time of day, geo-location, device fingerprint
• External systems: HR databases, billing systems, project management tools

For example:

“Only paying users from verified organizations can generate production reports between 9 AM and 6 PM.”

Supporting this means your authorization system must handle attribute-based access control (ABAC) and support external data lookups during evaluation. This can be achieved by tools like OPAL, that support detecting changes to both policy and policy data in realtime, and pushing live updates to your authorization layer.

Approval Flows & Operation-Level Approvals

Some actions shouldn’t execute just because the user technically has permission—they need a second layer of control, like approvals or escalation workflows.

These include:

• Publishing content that requires review by Legal and Compliance
• Large payments or refunds needing multi-party approval
• Data exports triggering a manager review

Your system should support:
Multi-stage, multi-party approvals, just-in-time access requests ("I need access to this document today for this task"), and operation-aware policies that only grant permission once approvals are complete.

Building such capabilities from scratch, let alone making them user friendly and accessible to non-technical users can be quite a complex task, easily solvable by using something like Permit’s operation approval embeddable elements.

Relationships, Hierarchies, and Role Derivation

Many access decisions depend not just on static roles or conditions, but on relationships between users and resources, and organizational hierarchies.

For example:

• "Users can edit a project if they are a collaborator or the project owner."
• "Managers can view reports for any employee in their department."
• "Regional admins inherit permissions for their entire region, while local admins have more limited scope."

These types of rules require your system to support Relationship-Based Access Control (ReBAC) policies, dynamically derive roles from organizational structure or resource relationships, and enforce hierarchical permissions.

The Bottom Line

If your authorization model only answers “Does this role have access?”, you’ll quickly hit a wall as your app grows.

The real power comes from dynamic, context-driven access decisions—allowing your system to enforce business logic, respect approvals and workflows, pull in real-time external data and adapt as products and compliance needs evolve.

Your authorization architecture needs to be built for these realities, not just static roles.

How Does Your Application Handle Auditing and Compliance?

Strong access control isn’t just about making the right decision at the right time—it’s also about proving why that decision was made. Auditing and compliance are core to any mature authorization architecture, especially for products operating in regulated industries or handling sensitive data.

Developers need to think about how they’ll log, trace, and explain access decisions—before compliance comes knocking.

Real-Time Decision Logging

Your system should log every access decision in real-time—not just the fact that access was granted or denied, but why. A complete decision log includes:

• Who made the request (Or, as we discussed in the case of AI agents - on behalf of whom)
• What resource or action was targeted
• What policy rules were evaluated
• What data or context influenced the outcome (role, attributes, external data)
• Whether approvals or justifications were involved

These logs are invaluable for debugging, incident response, and demonstrating compliance.

Decision Path Traceability

For high-stakes operations, teams often need to reconstruct the full decision path:

• “Why was this user able to delete this record?”
• “Who approved this $50K transfer?”
• “What conditions made this action valid at the time?”

Your system should support explainability features that allow teams to see the exact policy chain and data inputs that led to a decision—critical for compliance audits, investigations, and customer trust.

Regulatory Alignment (GDPR, HIPAA, SOC 2, and More)

Different industries face different compliance regimes, but common requirements include:

• Role-based and least-privilege access controls
• Secure data handling and audit trails
• Access justification and approval tracking
• Retention policies for logs and records

Your authorization architecture should make it easy to map controls to these requirements, rather than retrofitting after the fact.

How Will Your Authorization System Evolve Over Time? (Future-Proofing)

Building Authorization is an ongoing thing. It will have to evolve alongside your product, team, and user base. What works “Ok” on day one will almost certainly become a bottleneck or security risk as your application scales and your business logic becomes more complex. Future-proofing your authorization architecture means designing it to handle change gracefully, without costly rewrites or operational risks.

Your User Types Will Expand

As your application matures, the range of actors interacting with your system will grow far beyond just end users and admins. External partners, contractors, third-party services, and AI agents will all require carefully scoped access. A future-proof authorization model must support machine identities, human users, and external integrations equally—allowing you to extend access safely as your ecosystem grows.

Your Business Logic Will Get More Complex

In the early stages, simple role checks may cover most use cases. But over time, your business logic will inevitably become more layered and conditional. You’ll need to support complex workflows with multi-stage approvals, enforce access conditions based on subscription tiers or organizational hierarchies, and handle exceptions like temporary access or delegated permissions.

Systems that rely solely on static role definitions struggle in the face of this growth. Prioritize dynamic evaluation and supporting attribute and relationship-based access (Or at least prepare your system to handle this complexity without a full overhaul).

Regulations and Security Expectations Will Tighten

As your product scales into new markets or industries, the regulatory landscape becomes more demanding. Compliance with frameworks like GDPR, HIPAA, SOC 2, and emerging AI regulations will place new requirements on your authorization layer. You’ll need to support user consent models, enforce data minimization, ensure transparency around AI-driven decisions, and maintain detailed audit logs. Future-proofing means building an authorization system that can adapt to changing regulatory demands—allowing you to stay compliant without constant rework.

Decouple Policies from Code

One of the most impactful steps you can take to future-proof your authorization is decoupling access policies from your application code. Hardcoded permissions and role checks may seem simple early on but quickly become rigid and costly to change.

By externalizing your policies—whether through policy-as-code frameworks or no-code policy management tools—you enable your team to update rules, test changes, and deploy new logic without shipping a new version of your app.

The Bottom Line

The reality is that your authorization needs will only grow more complex as your product succeeds. Future-proofing your system ensures you’re ready for that complexity. The more flexible and decoupled your architecture, the easier it will be to scale, adapt, and stay secure without slowing down your product or your team.

From Planning to Production

Authorization is not just a gatekeeping mechanism—it’s a core part of your product’s architecture that directly shapes collaboration, user experience, compliance, and security.

The more your application grows, the more critical it becomes to model access in a way that reflects real-world workflows, relationships, and dynamic conditions.

The strongest authorization systems don’t just answer “Can this user perform this action?”

They help teams enforce complex business logic, manage approvals, control AI behavior, and adapt to shifting regulatory landscapes—all while staying flexible enough to evolve as the product changes.

Designing your authorization model and architecture with this mindset from the start sets you up for scale. It allows you to support new use cases, integrate external actors and AI agents, and maintain confidence that sensitive operations remain secure and auditable.

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