Policy Translation & Modeling

Convert legacy authorization models to modern Fine-Grained Authorization policies with automated translation and optimization

Policy Translation & Modeling

The heart of FGA migration is translating your existing authorization logic into modern, maintainable policies. Authonomy’s Policy Translation engine automatically converts legacy authorization models into optimized Fine-Grained Authorization (FGA) policies while preserving your business logic and security requirements.

Supported Authorization Models

🏢 Legacy RBAC Translation

Convert traditional role-based access control into flexible policy models:

Before (Legacy RBAC):

-- Traditional role table
CREATE TABLE user_roles (
    user_id INT,
    role_name VARCHAR(50)
);

-- Hardcoded role checks in application
if (user.hasRole("admin") || user.hasRole("manager")) {
    allowAccess();
}

After (FGA Policy):

# Modern FGA policy
model:
  schema: |
    type user
    type role
    type document
    
    relations:
      define admin: [user] or manager
      define manager: [user]
      define viewer: [user] or admin or manager
      
tuples:
  - user:alice#admin document:budget
  - user:bob#manager document:reports

🔒 Database ACL Migration

Transform database-level access controls into application-layer policies:

Before (Database ACLs):

-- Row-level security in PostgreSQL
CREATE POLICY department_policy ON employees
FOR ALL TO app_user
USING (department_id = current_user_department());

-- Grant-based permissions
GRANT SELECT ON sensitive_data TO finance_role;

After (FGA Policy):

# Application-layer equivalent
model:
  schema: |
    type user
    type department  
    type record
    
    relations:
      define member: [user]
      define can_read: member
      
policy_rules:
  - resource: "record:employee_data"
    relation: "can_read"
    condition: "user.department == record.department"

⚙️ Hardcoded Permission Extraction

Extract implicit authorization logic from application code:

Before (Hardcoded Logic):

// Scattered throughout codebase
function canEditInvoice(user, invoice) {
  if (user.role === 'admin') return true;
  if (user.role === 'manager' && user.department === invoice.department) return true;
  if (user.id === invoice.created_by && invoice.status === 'draft') return true;
  return false;
}

After (Centralized FGA Policy):

model:
  schema: |
    type user
    type invoice
    
    relations:
      define admin: [user]
      define department_manager: [user]  
      define creator: [user]
      define can_edit: admin or (department_manager and same_department) or (creator and is_draft)

conditions:
  same_department: "user.department == invoice.department"
  is_draft: "invoice.status == 'draft'"

Translation Process

1️⃣ Pattern Recognition

The translation engine identifies common authorization patterns:

  • Role hierarchies: Manager inherits employee permissions
  • Resource ownership: Users can edit their own content
  • Conditional access: Time-based or context-dependent permissions
  • Delegation patterns: Users granting access to others

2️⃣ Business Rule Extraction

Automatically extract business logic from various sources:

  • Code analysis: Parse permission checks in application code
  • Database schemas: Analyze foreign key relationships and constraints
  • Configuration files: Extract role definitions and permission matrices
  • Documentation: Process policy documents and access procedures

3️⃣ Policy Optimization

Generate efficient, maintainable policies:

  • Eliminate redundancy: Combine overlapping rules
  • Simplify conditions: Use the most straightforward logic
  • Optimize performance: Structure policies for fast evaluation
  • Ensure completeness: Cover all discovered authorization scenarios

Advanced Translation Scenarios

🔄 Multi-System Policy Unification

When you have authorization spread across multiple systems:

# Before: Three different systems, three different models
System A: user.role in ['admin', 'editor']
System B: user.permissions.includes('write_access')  
System C: database_role='data_modifier'

# After: Unified FGA policy
model:
  schema: |
    type user
    type resource
    
    relations:
      define admin: [user]
      define editor: [user] 
      define writer: [user]
      define can_modify: admin or editor or writer
      
# Single policy covers all three systems

🎯 Custom Business Logic Preservation

Complex business rules are preserved in FGA conditions:

# Original complex logic:
# "Sales managers can approve discounts up to 15% for their region
#  during quarterly promotion periods, but only for customers 
#  with good payment history"

model:
  conditions:
    is_sales_manager: "user.role == 'sales_manager'"
    same_region: "user.region == customer.region" 
    promotion_period: "date.quarter_end - date.now <= 30"
    good_payment: "customer.payment_score >= 7"
    discount_limit: "discount.percentage <= 15"
    
  relations:
    define can_approve_discount: 
      is_sales_manager and same_region and promotion_period 
      and good_payment and discount_limit

🔗 Cross-System Authorization

Handle permissions that span multiple applications:

# User permissions flow across system boundaries
model:
  schema: |
    type user
    type crm_account  
    type billing_system
    type support_ticket
    
    relations:
      define account_manager: [user]
      define can_access_billing: account_manager from crm_account
      define can_view_tickets: can_access_billing from billing_system

Policy Model Design Patterns

📋 Resource Hierarchy Modeling

Model nested resource structures efficiently:

# Organization → Department → Project → Document hierarchy
model:
  schema: |
    type organization
    type department  
    type project
    type document
    type user
    
    relations:
      define admin: [user]
      define member: [user] or admin
      define parent: [organization]
      define parent: [department] from parent
      define parent: [project] from parent  
      define viewer: member from parent

🎭 Role-Based Templates

Create reusable policy templates for common scenarios:

# Customer Support Role Template
support_agent_template:
  relations:
    define can_read: [user:support_agent]
    define can_update: can_read and ticket_assigned_to_agent
    define can_escalate: can_read and escalation_time_reached
    
  conditions:
    ticket_assigned_to_agent: "ticket.assignee == user.id"
    escalation_time_reached: "ticket.created_at < (now - 24h)"

Handling Edge Cases

🚨 Legacy System Peculiarities

Common challenges and solutions:

Challenge: System with no user authentication

# Solution: Model system-level permissions
model:
  schema: |
    type system
    type operation
    
    relations:
      define trusted: [system]
      define can_execute: trusted

Challenge: Time-based permissions

# Solution: Temporal conditions  
conditions:
  business_hours: "time.hour >= 9 and time.hour <= 17"
  weekday: "time.day_of_week in [1,2,3,4,5]"
  
relations:
  define can_access: user and business_hours and weekday

Challenge: Geographic restrictions

# Solution: Location-based conditions
conditions:
  allowed_region: "user.location.country in ['US', 'CA', 'MX']"
  compliance_zone: "data.classification != 'restricted' or same_country"
  
relations:
  define can_view: allowed_region and compliance_zone

Translation Validation

✅ Automated Testing

Every translated policy is automatically validated:

  • Syntax checking: Ensure policy syntax is correct
  • Logic verification: Test policy logic against known scenarios
  • Performance testing: Validate policy evaluation speed
  • Security analysis: Check for potential privilege escalation

🔍 Business Logic Verification

Validate that translation preserves business intent:

  • Permission parity testing: Verify legacy and FGA decisions match
  • Edge case handling: Test boundary conditions and error scenarios
  • Stakeholder review: Business teams validate translated business rules

Migration Strategy Integration

📈 Gradual Policy Rollout

Translation supports incremental migration:

  • Policy versioning: Maintain multiple policy versions during transition
  • A/B testing: Compare legacy vs. FGA decisions for specific user groups
  • Feature flags: Enable FGA policies for specific features or endpoints
  • Rollback capabilities: Quickly revert to legacy authorization if needed

🎯 Risk-Based Prioritization

Use translation analysis to prioritize migration:

  • High-risk systems: Focus on systems with security gaps first
  • Business critical: Prioritize revenue-impacting applications
  • Compliance driven: Address regulatory requirements early
  • Technical debt: Factor in maintenance burden of current systems

Getting Started with Translation

🚀 Quick Start

  1. Run discovery to identify authorization patterns
  2. Review translation suggestions in the policy modeling interface
  3. Validate business logic with stakeholder teams
  4. Test policies in shadow mode before enforcement

🛠️ Advanced Configuration

For complex environments:

  • Custom translation rules: Handle unique authorization patterns
  • Policy templates: Create reusable patterns for your organization
  • Integration hooks: Connect translation with existing development workflows

Next Steps

Once your policies are translated and validated, proceed to Migration Testing & Validation to safely test your new authorization logic in production.

Questions about policy translation? Contact our policy experts for personalized guidance on your migration strategy.