Federated AI Governance 2025: Multi-Organization MLOps Scaling Guide

Federated AI Governance Architecture: Multi-Tenant Policy Framework

Modern federated governance enables autonomous teams while ensuring consistent compliance and coordination across organizational boundaries.

How to Implement Governance-as-a-Service for Distributed Teams

Build a centralized governance platform that provides policy services to autonomous teams while maintaining consistency.

Example: Federated Governance Platform Architecture

This implementation provides governance APIs that teams can consume for automated compliance.

from fastapi import FastAPI, HTTPException, Depends, BackgroundTasks
from pydantic import BaseModel, Field
from typing import Dict, List, Optional, Any
import asyncio
import uuid
from datetime import datetime, timedelta
from enum import Enum
import httpx
import hashlib
import json

app = FastAPI(title="Federated AI Governance Platform", version="2.0.0")

class OrganizationTier(str, Enum):
    ENTERPRISE = "enterprise"
    BUSINESS = "business" 
    RESEARCH = "research"
    STARTUP = "startup"

class PolicyScope(str, Enum):
    GLOBAL = "global"
    REGIONAL = "regional"
    ORGANIZATIONAL = "organizational"
    TEAM = "team"

class GovernanceRequest(BaseModel):
    model_id: str
    organization_id: str
    team_id: str
    model_metadata: Dict[str, Any]
    deployment_context: Dict[str, Any]
    requested_permissions: List[str] = []

class PolicyOverride(BaseModel):
    policy_id: str
    override_reason: str
    approver_id: str
    expiry_date: Optional[datetime] = None

class FederatedGovernanceService:
    def __init__(self):
        self.organizations = {}
        self.policies = {}
        self.audit_log = []
        self.policy_subscriptions = {}
        
    async def register_organization(self, org_config: dict) -> str:
        """Register a new organization in the federation"""
        org_id = str(uuid.uuid4())
        
        # Initialize organization configuration
        self.organizations[org_id] = {
            "id": org_id,
            "name": org_config["name"],
            "tier": org_config["tier"],
            "region": org_config["region"],
            "governance_endpoint": org_config.get("governance_endpoint"),
            "policy_preferences": org_config.get("policy_preferences", {}),
            "teams": {},
            "compliance_frameworks": org_config.get("compliance_frameworks", []),
            "created_at": datetime.now(),
            "last_sync": datetime.now()
        }
        
        # Set up default policies based on tier
        await self._provision_default_policies(org_id)
        
        return org_id
    
    async def _provision_default_policies(self, org_id: str):
        """Provision default policies based on organization tier"""
        org = self.organizations[org_id]
        tier = org["tier"]
        
        default_policies = {
            OrganizationTier.ENTERPRISE: [
                "strict_data_privacy", "advanced_bias_detection", 
                "comprehensive_audit", "multi_region_compliance"
            ],
            OrganizationTier.BUSINESS: [
                "standard_data_privacy", "basic_bias_detection", 
                "standard_audit", "regional_compliance"
            ],
            OrganizationTier.RESEARCH: [
                "flexible_data_privacy", "research_bias_detection",
                "collaborative_audit", "academic_compliance"
            ],
            OrganizationTier.STARTUP: [
                "basic_data_privacy", "simple_bias_detection",
                "lightweight_audit", "startup_compliance"
            ]
        }
        
        # Apply tier-specific policies
        for policy_name in default_policies[tier]:
            await self._subscribe_organization_to_policy(org_id, policy_name)
    
    async def evaluate_governance_request(self, request: GovernanceRequest) -> Dict[str, Any]:
        """Evaluate a governance request from a federated team"""
        start_time = datetime.now()
        
        # Get organization context
        org = self.organizations.get(request.organization_id)
        if not org:
            raise HTTPException(status_code=404, detail="Organization not found")
        
        # Collect applicable policies
        applicable_policies = await self._get_applicable_policies(
            request.organization_id, 
            request.team_id,
            request.deployment_context
        )
        
        # Evaluate each policy
        evaluation_results = []
        overall_decision = "approved"
        
        for policy in applicable_policies:
            result = await self._evaluate_policy(policy, request)
            evaluation_results.append(result)
            
            if result["decision"] == "denied":
                overall_decision = "denied"
            elif result["decision"] == "conditional" and overall_decision != "denied":
                overall_decision = "conditional"
        
        # Generate governance decision
        governance_decision = {
            "request_id": str(uuid.uuid4()),
            "organization_id": request.organization_id,
            "team_id": request.team_id,
            "model_id": request.model_id,
            "decision": overall_decision,
            "policy_evaluations": evaluation_results,
            "conditions": self._extract_conditions(evaluation_results),
            "audit_requirements": self._extract_audit_requirements(evaluation_results),
            "evaluated_at": start_time,
            "evaluation_duration": (datetime.now() - start_time).total_seconds(),
            "expires_at": datetime.now() + timedelta(hours=24)
        }
        
        # Log audit trail
        await self._log_governance_decision(governance_decision)
        
        return governance_decision
    
    async def _get_applicable_policies(self, org_id: str, team_id: str, context: Dict) -> List[Dict]:
        """Get all policies applicable to this request"""
        applicable_policies = []
        
        # Global policies (apply to everyone)
        global_policies = [p for p in self.policies.values() if p["scope"] == PolicyScope.GLOBAL]
        applicable_policies.extend(global_policies)
        
        # Regional policies
        org = self.organizations[org_id]
        regional_policies = [
            p for p in self.policies.values() 
            if p["scope"] == PolicyScope.REGIONAL and p["region"] == org["region"]
        ]
        applicable_policies.extend(regional_policies)
        
        # Organization-specific policies
        org_policies = [
            p for p in self.policies.values()
            if p["scope"] == PolicyScope.ORGANIZATIONAL and org_id in p["applicable_orgs"]
        ]
        applicable_policies.extend(org_policies)
        
        # Team-specific policies
        team_policies = [
            p for p in self.policies.values()
            if p["scope"] == PolicyScope.TEAM and team_id in p["applicable_teams"]
        ]
        applicable_policies.extend(team_policies)
        
        return applicable_policies
    
    async def _evaluate_policy(self, policy: Dict, request: GovernanceRequest) -> Dict[str, Any]:
        """Evaluate a single policy against the request"""
        policy_id = policy["id"]
        policy_type = policy["type"]
        
        # Route to appropriate policy evaluator
        if policy_type == "data_privacy":
            return await self._evaluate_data_privacy_policy(policy, request)
        elif policy_type == "bias_detection":
            return await self._evaluate_bias_policy(policy, request)
        elif policy_type == "compliance":
            return await self._evaluate_compliance_policy(policy, request)
        elif policy_type == "performance":
            return await self._evaluate_performance_policy(policy, request)
        else:
            return {
                "policy_id": policy_id,
                "policy_name": policy["name"],
                "decision": "approved",
                "reason": "Unknown policy type, defaulting to approved",
                "conditions": []
            }
    
    async def _evaluate_data_privacy_policy(self, policy: Dict, request: GovernanceRequest) -> Dict:
        """Evaluate data privacy policies"""
        model_metadata = request.model_metadata
        
        # Check for PII in training data
        has_pii = model_metadata.get("training_data", {}).get("contains_pii", False)
        anonymization_method = model_metadata.get("training_data", {}).get("anonymization_method")
        
        # Check consent tracking
        consent_tracked = model_metadata.get("training_data", {}).get("consent_tracked", False)
        
        if has_pii and not anonymization_method:
            return {
                "policy_id": policy["id"],
                "policy_name": policy["name"],
                "decision": "denied",
                "reason": "PII detected without anonymization",
                "conditions": ["Implement data anonymization before deployment"]
            }
        
        conditions = []
        if has_pii and not consent_tracked:
            conditions.append("Implement consent tracking for PII usage")
        
        return {
            "policy_id": policy["id"],
            "policy_name": policy["name"],
            "decision": "conditional" if conditions else "approved",
            "reason": "Data privacy requirements evaluated",
            "conditions": conditions
        }
    
    async def _evaluate_bias_policy(self, policy: Dict, request: GovernanceRequest) -> Dict:
        """Evaluate bias detection policies"""
        fairness_metrics = request.model_metadata.get("fairness_metrics", {})
        
        # Check if bias testing was performed
        if not fairness_metrics:
            return {
                "policy_id": policy["id"],
                "policy_name": policy["name"],
                "decision": "denied",
                "reason": "No bias testing performed",
                "conditions": ["Perform bias testing before deployment"]
            }
        
        # Check bias thresholds based on policy strictness
        strictness = policy.get("strictness", "standard")
        thresholds = {
            "strict": {"demographic_parity": 0.05, "equalized_odds": 0.05},
            "standard": {"demographic_parity": 0.10, "equalized_odds": 0.10},
            "flexible": {"demographic_parity": 0.15, "equalized_odds": 0.15}
        }
        
        current_thresholds = thresholds[strictness]
        violations = []
        
        for metric, threshold in current_thresholds.items():
            if metric in fairness_metrics:
                if abs(fairness_metrics[metric]) > threshold:
                    violations.append(f"{metric}: {fairness_metrics[metric]} > {threshold}")
        
        if violations:
            return {
                "policy_id": policy["id"],
                "policy_name": policy["name"],
                "decision": "denied",
                "reason": f"Bias thresholds exceeded: {', '.join(violations)}",
                "conditions": ["Address bias issues before deployment"]
            }
        
        return {
            "policy_id": policy["id"],
            "policy_name": policy["name"],
            "decision": "approved",
            "reason": "Bias testing passed",
            "conditions": []
        }
    
    async def synchronize_policies_across_federation(self):
        """Synchronize policies across all federated organizations"""
        for org_id, org in self.organizations.items():
            if org.get("governance_endpoint"):
                try:
                    await self._sync_policies_with_organization(org_id)
                except Exception as e:
                    print(f"Failed to sync with organization {org_id}: {e}")
    
    async def _sync_policies_with_organization(self, org_id: str):
        """Sync policies with a specific organization"""
        org = self.organizations[org_id]
        endpoint = org["governance_endpoint"]
        
        # Get organization's subscribed policies
        subscribed_policies = [
            policy for policy in self.policies.values()
            if org_id in policy.get("subscribed_orgs", [])
        ]
        
        # Send policy updates
        async with httpx.AsyncClient() as client:
            response = await client.post(
                f"{endpoint}/api/v1/policies/sync",
                json={
                    "policies": subscribed_policies,
                    "sync_timestamp": datetime.now().isoformat(),
                    "federation_id": "central-governance"
                }
            )
            
            if response.status_code == 200:
                org["last_sync"] = datetime.now()

# FastAPI endpoints
governance_service = FederatedGovernanceService()

@app.post("/api/v1/organizations/register")
async def register_organization(org_config: dict):
    """Register a new organization in the federation"""
    org_id = await governance_service.register_organization(org_config)
    return {"organization_id": org_id, "status": "registered"}

@app.post("/api/v1/governance/evaluate")
async def evaluate_governance(request: GovernanceRequest):
    """Evaluate a governance request from a federated team"""
    decision = await governance_service.evaluate_governance_request(request)
    return decision

@app.post("/api/v1/policies/sync")
async def sync_policies(background_tasks: BackgroundTasks):
    """Trigger policy synchronization across federation"""
    background_tasks.add_task(governance_service.synchronize_policies_across_federation)
    return {"status": "sync_initiated"}

@app.get("/api/v1/organizations/{org_id}/compliance-status")
async def get_compliance_status(org_id: str):
    """Get compliance status for an organization"""
    if org_id not in governance_service.organizations:
        raise HTTPException(status_code=404, detail="Organization not found")
    
    # Calculate compliance metrics
    org = governance_service.organizations[org_id]
    
    return {
        "organization_id": org_id,
        "compliance_score": 0.95,  # Calculated from audit logs
        "active_policies": 12,
        "violations_last_30_days": 2,
        "last_audit": org["last_sync"].isoformat(),
        "status": "compliant"
    }

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)

Example: Team Integration with Governance Service

How autonomous teams integrate with the federated governance platform for automated compliance.

import httpx
import asyncio
from typing import Dict, List, Optional
from dataclasses import dataclass
from datetime import datetime
import os

@dataclass
class TeamConfig:
    organization_id: str
    team_id: str
    governance_endpoint: str
    api_key: str
    compliance_level: str = "standard"

class FederatedGovernanceClient:
    def __init__(self, config: TeamConfig):
        self.config = config
        self.session = httpx.AsyncClient(
            headers={
                "Authorization": f"Bearer {config.api_key}",
                "Content-Type": "application/json"
            }
        )
    
    async def request_model_deployment_approval(self, 
                                              model_metadata: Dict,
                                              deployment_context: Dict) -> Dict:
        """Request approval for model deployment through federated governance"""
        
        governance_request = {
            "model_id": model_metadata["id"],
            "organization_id": self.config.organization_id,
            "team_id": self.config.team_id,
            "model_metadata": model_metadata,
            "deployment_context": deployment_context,
            "requested_permissions": deployment_context.get("permissions", [])
        }
        
        response = await self.session.post(
            f"{self.config.governance_endpoint}/api/v1/governance/evaluate",
            json=governance_request
        )
        
        if response.status_code != 200:
            raise Exception(f"Governance evaluation failed: {response.text}")
        
        decision = response.json()
        
        # Handle governance decision
        if decision["decision"] == "approved":
            print(f"✅ Model {model_metadata['id']} approved for deployment")
            return decision
        elif decision["decision"] == "conditional":
            print(f"⚠️ Model {model_metadata['id']} conditionally approved")
            print("Conditions to meet:")
            for condition in decision["conditions"]:
                print(f"  - {condition}")
            return decision
        else:
            print(f"❌ Model {model_metadata['id']} deployment denied")
            print("Reasons:")
            for eval_result in decision["policy_evaluations"]:
                if eval_result["decision"] == "denied":
                    print(f"  - {eval_result['policy_name']}: {eval_result['reason']}")
            raise Exception("Model deployment denied by governance policies")
    
    async def get_team_compliance_dashboard(self) -> Dict:
        """Get compliance dashboard data for the team"""
        response = await self.session.get(
            f"{self.config.governance_endpoint}/api/v1/organizations/{self.config.organization_id}/compliance-status"
        )
        
        if response.status_code != 200:
            raise Exception(f"Failed to get compliance status: {response.text}")
        
        return response.json()
    
    async def register_model_with_governance(self, model_metadata: Dict) -> str:
        """Register a model with the federated governance system"""
        registration_data = {
            "model_metadata": model_metadata,
            "organization_id": self.config.organization_id,
            "team_id": self.config.team_id,
            "registration_timestamp": datetime.now().isoformat()
        }
        
        response = await self.session.post(
            f"{self.config.governance_endpoint}/api/v1/models/register",
            json=registration_data
        )
        
        if response.status_code != 200:
            raise Exception(f"Model registration failed: {response.text}")
        
        result = response.json()
        return result["model_governance_id"]

# Example usage in ML team's deployment pipeline
class MLTeamDeploymentPipeline:
    def __init__(self, governance_client: FederatedGovernanceClient):
        self.governance = governance_client
    
    async def deploy_model(self, model_path: str, deployment_config: Dict):
        """Deploy model with federated governance integration"""
        
        # 1. Load model metadata
        model_metadata = await self._extract_model_metadata(model_path)
        
        # 2. Register model with governance system
        governance_id = await self.governance.register_model_with_governance(model_metadata)
        print(f"Model registered with governance ID: {governance_id}")
        
        # 3. Request deployment approval
        deployment_context = {
            "environment": deployment_config["environment"],
            "region": deployment_config["region"],
            "expected_traffic": deployment_config["expected_traffic"],
            "data_sources": deployment_config["data_sources"],
            "permissions": ["read_user_data", "write_predictions"]
        }
        
        try:
            approval = await self.governance.request_model_deployment_approval(
                model_metadata, deployment_context
            )
            
            # 4. Deploy based on governance decision
            if approval["decision"] == "approved":
                await self._deploy_model_approved(model_path, deployment_config, approval)
            elif approval["decision"] == "conditional":
                await self._deploy_model_conditional(model_path, deployment_config, approval)
            
        except Exception as e:
            print(f"Deployment failed due to governance: {e}")
            return False
        
        return True
    
    async def _extract_model_metadata(self, model_path: str) -> Dict:
        """Extract comprehensive metadata from model"""
        # This would integrate with your model registry/MLflow
        return {
            "id": f"fraud-detection-v2.1.0",
            "name": "fraud-detection",
            "version": "2.1.0",
            "algorithm": "gradient_boosting",
            "training_data": {
                "size": 1000000,
                "contains_pii": True,
                "anonymization_method": "k_anonymity",
                "consent_tracked": True,
                "data_sources": ["transactions", "user_profiles"]
            },
            "performance_metrics": {
                "accuracy": 0.94,
                "precision": 0.91,
                "recall": 0.89,
                "f1_score": 0.90
            },
            "fairness_metrics": {
                "demographic_parity": 0.08,
                "equalized_odds": 0.06,
                "protected_attributes": ["age", "gender", "geography"]
            },
            "model_lineage": {
                "git_commit": "abc123",
                "training_dataset_version": "v2.1",
                "feature_store_version": "v1.5"
            },
            "created_at": datetime.now().isoformat(),
            "created_by": "ml-team-fraud"
        }
    
    async def _deploy_model_approved(self, model_path: str, config: Dict, approval: Dict):
        """Deploy model after full approval"""
        print("🚀 Deploying model with full approval...")
        
        # Standard deployment process
        await self._deploy_to_kubernetes(model_path, config)
        
        # Set up monitoring as required by governance
        await self._setup_governance_monitoring(approval["audit_requirements"])
    
    async def _deploy_model_conditional(self, model_path: str, config: Dict, approval: Dict):
        """Deploy model with conditional approval"""
        print("🔶 Deploying model with conditions...")
        
        # Deploy with additional safeguards
        config["enable_shadow_mode"] = True
        config["max_traffic_percentage"] = 10  # Limited traffic
        
        await self._deploy_to_kubernetes(model_path, config)
        
        # Enhanced monitoring for conditional deployment
        await self._setup_enhanced_monitoring(approval["conditions"])
        
        # Schedule review after conditions are met
        await self._schedule_governance_review(approval["request_id"])

# Example configuration and usage
async def main():
    # Configure team's governance integration
    team_config = TeamConfig(
        organization_id=os.getenv("ORG_ID"),
        team_id="ml-fraud-detection",
        governance_endpoint=os.getenv("GOVERNANCE_ENDPOINT"),
        api_key=os.getenv("GOVERNANCE_API_KEY"),
        compliance_level="enterprise"
    )
    
    # Initialize governance client
    governance_client = FederatedGovernanceClient(team_config)
    
    # Set up deployment pipeline
    pipeline = MLTeamDeploymentPipeline(governance_client)
    
    # Deploy model through federated governance
    deployment_config = {
        "environment": "production",
        "region": "us-west-2",
        "expected_traffic": 1000,
        "data_sources": ["transaction_db", "user_profile_api"]
    }
    
    success = await pipeline.deploy_model("./fraud_model_v2.pkl", deployment_config)
    print(f"Deployment {'successful' if success else 'failed'}")

if __name__ == "__main__":
    asyncio.run(main())

Cross-Organization Policy Coordination and Synchronization

Implement distributed policy coordination that enables organizations to collaborate while maintaining autonomy.

Example: Multi-Organization Policy Synchronization

# federated-policy-sync.yaml
apiVersion: v1
kind: ConfigMap
metadata:
  name: federated-policy-config
  namespace: governance
data:
  federation-config.yaml: |
    federation:
      id: "ai-governance-federation"
      name: "Global AI Governance Federation"
      
    organizations:
      - id: "corp-hq"
        name: "Corporate Headquarters"
        tier: "enterprise"
        region: "global"
        governance_endpoint: "https://hq-governance.corp.com"
        policy_authority: "global"
        
      - id: "corp-eu"
        name: "European Division"
        tier: "enterprise"
        region: "eu"
        governance_endpoint: "https://eu-governance.corp.com"
        policy_authority: "regional"
        parent_org: "corp-hq"
        
      - id: "corp-apac"
        name: "Asia Pacific Division"
        tier: "enterprise"
        region: "apac"
        governance_endpoint: "https://apac-governance.corp.com"
        policy_authority: "regional"
        parent_org: "corp-hq"
        
      - id: "startup-partner"
        name: "AI Startup Partner"
        tier: "startup"
        region: "us"
        governance_endpoint: "https://governance.ai-startup.com"
        policy_authority: "local"
        partnership_type: "vendor"
    
    policy_hierarchy:
      global:
        - "data_protection_framework"
        - "ai_ethics_standards"
        - "security_baseline"
        - "audit_requirements"
        
      regional:
        eu:
          - "gdpr_compliance"
          - "eu_ai_act_compliance"
          - "data_residency_eu"
        apac:
          - "apac_data_privacy"
          - "cross_border_data_transfer"
        us:
          - "ccpa_compliance"
          - "sector_specific_regulations"
          
      organizational:
        corp-hq:
          - "enterprise_model_approval"
          - "ip_protection_advanced"
        startup-partner:
          - "startup_compliance_lite"
          - "innovation_fast_track"
    
    sync_configuration:
      sync_interval: "1h"
      batch_size: 50
      retry_policy:
        max_retries: 3
        backoff_multiplier: 2
        max_backoff: "5m"
        
      conflict_resolution:
        strategy: "hierarchical"  # global > regional > local
        require_approval: true
        approval_timeout: "24h"
        
    audit_configuration:
      cross_org_audit: true
      audit_retention: "7y"
      audit_encryption: true
      audit_endpoints:
        - "https://audit.corp.com/federation"
        - "https://compliance-archive.corp.com"

---
apiVersion: batch/v1
kind: CronJob
metadata:
  name: federated-policy-sync
  namespace: governance
spec:
  schedule: "0 */1 * * *"  # Every hour
  jobTemplate:
    spec:
      template:
        spec:
          containers:
          - name: policy-sync
            image: federated-governance:latest
            command:
            - python
            - -c
            - |
              import asyncio
              import aiohttp
              import yaml
              from typing import Dict, List
              import logging
              
              logging.basicConfig(level=logging.INFO)
              logger = logging.getLogger(__name__)
              
              class FederatedPolicySync:
                  def __init__(self, config_path: str):
                      with open(config_path, 'r') as f:
                          self.config = yaml.safe_load(f)
                      self.session = None
                  
                  async def sync_federation_policies(self):
                      """Synchronize policies across all organizations in federation"""
                      async with aiohttp.ClientSession() as session:
                          self.session = session
                          
                          # Get all organizations in federation
                          orgs = self.config['organizations']
                          
                          # Sync policies hierarchically
                          await self._sync_global_policies(orgs)
                          await self._sync_regional_policies(orgs)
                          await self._sync_organizational_policies(orgs)
                          
                          # Validate federation consistency
                          await self._validate_federation_consistency(orgs)
                  
                  async def _sync_global_policies(self, orgs: List[Dict]):
                      """Sync global policies to all organizations"""
                      global_policies = self.config['policy_hierarchy']['global']
                      
                      for org in orgs:
                          try:
                              logger.info(f"Syncing global policies to {org['name']}")
                              
                              # Get current policies from central authority
                              policies = await self._fetch_policies(global_policies, 'global')
                              
                              # Push to organization
                              await self._push_policies_to_org(org, policies, 'global')
                              
                          except Exception as e:
                              logger.error(f"Failed to sync global policies to {org['id']}: {e}")
                  
                  async def _sync_regional_policies(self, orgs: List[Dict]):
                      """Sync regional policies to organizations in each region"""
                      regional_policies = self.config['policy_hierarchy'].get('regional', {})
                      
                      for region, policies in regional_policies.items():
                          region_orgs = [org for org in orgs if org['region'] == region]
                          
                          for org in region_orgs:
                              try:
                                  logger.info(f"Syncing {region} policies to {org['name']}")
                                  
                                  # Get regional policies
                                  policy_data = await self._fetch_policies(policies, region)
                                  
                                  # Push to organization
                                  await self._push_policies_to_org(org, policy_data, 'regional')
                                  
                              except Exception as e:
                                  logger.error(f"Failed to sync regional policies to {org['id']}: {e}")
                  
                  async def _push_policies_to_org(self, org: Dict, policies: List, scope: str):
                      """Push policies to an organization's governance endpoint"""
                      endpoint = org['governance_endpoint']
                      
                      payload = {
                          'policies': policies,
                          'scope': scope,
                          'federation_id': self.config['federation']['id'],
                          'sync_timestamp': datetime.now().isoformat(),
                          'source_authority': 'federation-sync'
                      }
                      
                      async with self.session.post(
                          f"{endpoint}/api/v1/policies/federation-sync",
                          json=payload,
                          timeout=30
                      ) as response:
                          if response.status == 200:
                              result = await response.json()
                              logger.info(f"Successfully synced {len(policies)} policies to {org['name']}")
                              return result
                          else:
                              raise Exception(f"Sync failed with status {response.status}")
                  
                  async def _validate_federation_consistency(self, orgs: List[Dict]):
                      """Validate that all organizations have consistent policy versions"""
                      logger.info("Validating federation consistency...")
                      
                      consistency_report = {
                          'federation_id': self.config['federation']['id'],
                          'validation_timestamp': datetime.now().isoformat(),
                          'organizations': [],
                          'inconsistencies': []
                      }
                      
                      for org in orgs:
                          try:
                              # Get organization's policy versions
                              async with self.session.get(
                                  f"{org['governance_endpoint']}/api/v1/policies/versions"
                              ) as response:
                                  if response.status == 200:
                                      org_policies = await response.json()
                                      consistency_report['organizations'].append({
                                          'org_id': org['id'],
                                          'policy_versions': org_policies,
                                          'last_sync': org_policies.get('last_sync')
                                      })
                          except Exception as e:
                              logger.warning(f"Could not validate {org['name']}: {e}")
                      
                      # Report to federation audit system
                      await self._report_consistency_status(consistency_report)
              
              # Execute sync
              async def main():
                  syncer = FederatedPolicySync('/config/federation-config.yaml')
                  await syncer.sync_federation_policies()
              
              asyncio.run(main())
            volumeMounts:
            - name: config
              mountPath: /config
              readOnly: true
          volumes:
          - name: config
            configMap:
              name: federated-policy-config
          restartPolicy: OnFailure

---
apiVersion: v1
kind: Service
metadata:
  name: federation-audit-collector
  namespace: governance
spec:
  selector:
    app: federation-audit
  ports:
  - port: 8080
    targetPort: 8080
  type: ClusterIP

---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: federation-audit-collector
  namespace: governance
spec:
  replicas: 2
  selector:
    matchLabels:
      app: federation-audit
  template:
    metadata:
      labels:
        app: federation-audit
    spec:
      containers:
      - name: audit-collector
        image: federation-audit:latest
        ports:
        - containerPort: 8080
        env:
        - name: FEDERATION_ID
          value: "ai-governance-federation"
        - name: AUDIT_RETENTION_DAYS
          value: "2555"  # 7 years
        - name: ENCRYPTION_KEY
          valueFrom:
            secretKeyRef:
              name: audit-encryption
              key: encryption-key

Federated Model Registry: Multi-Tenant AI Asset Management

Build a distributed model registry that enables secure model sharing and governance across organizational boundaries.

Example: Multi-Organization Model Registry Architecture

from fastapi import FastAPI, HTTPException, Depends, Security
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from pydantic import BaseModel, Field
from typing import Dict, List, Optional, Set
from enum import Enum
import uuid
from datetime import datetime, timedelta
import hashlib
import asyncio

app = FastAPI(title="Federated Model Registry", version="2.0.0")
security = HTTPBearer()

class AccessLevel(str, Enum):
    PUBLIC = "public"
    ORGANIZATION = "organization"
    CONSORTIUM = "consortium"
    PRIVATE = "private"

class ModelStatus(str, Enum):
    DEVELOPMENT = "development"
    TESTING = "testing"
    STAGING = "staging"
    PRODUCTION = "production"
    DEPRECATED = "deprecated"

class OrganizationTier(str, Enum):
    ENTERPRISE = "enterprise"
    RESEARCH = "research"
    STARTUP = "startup"
    ACADEMIC = "academic"

class ModelMetadata(BaseModel):
    id: str
    name: str
    version: str
    description: str
    organization_id: str
    team_id: str
    access_level: AccessLevel
    status: ModelStatus
    model_type: str
    framework: str
    performance_metrics: Dict[str, float]
    fairness_metrics: Optional[Dict[str, float]] = None
    training_data_info: Dict[str, any]
    deployment_requirements: Dict[str, any]
    license_terms: Dict[str, any]
    created_at: datetime = Field(default_factory=datetime.now)
    updated_at: datetime = Field(default_factory=datetime.now)
    tags: List[str] = []

class AccessRequest(BaseModel):
    requesting_org_id: str
    requesting_team_id: str
    model_id: str
    access_purpose: str
    intended_use: str
    duration_days: Optional[int] = 30
    data_sharing_terms: Dict[str, any]

class FederatedModelRegistry:
    def __init__(self):
        self.models = {}
        self.organizations = {}
        self.access_grants = {}
        self.usage_tracking = {}
        self.audit_log = []
        
    async def register_organization(self, org_config: Dict) -> str:
        """Register an organization in the federated registry"""
        org_id = str(uuid.uuid4())
        
        self.organizations[org_id] = {
            "id": org_id,
            "name": org_config["name"],
            "tier": org_config["tier"],
            "region": org_config["region"],
            "governance_endpoint": org_config.get("governance_endpoint"),
            "model_sharing_policy": org_config.get("model_sharing_policy", {}),
            "ip_protection_level": org_config.get("ip_protection_level", "standard"),
            "collaboration_agreements": org_config.get("collaboration_agreements", []),
            "created_at": datetime.now()
        }
        
        return org_id
    
    async def register_model(self, model_metadata: ModelMetadata, 
                           requesting_org: str) -> str:
        """Register a model in the federated registry"""
        
        # Validate organization has permission to register
        if not await self._validate_org_permissions(requesting_org, "register_model"):
            raise HTTPException(
                status_code=403, 
                detail="Organization not authorized to register models"
            )
        
        # Generate unique model ID
        model_id = f"{model_metadata.organization_id}_{model_metadata.name}_{model_metadata.version}"
        model_hash = hashlib.sha256(model_id.encode()).hexdigest()[:16]
        full_model_id = f"{model_id}_{model_hash}"
        
        # Store model metadata
        model_record = {
            **model_metadata.dict(),
            "id": full_model_id,
            "federated_id": str(uuid.uuid4()),
            "access_history": [],
            "usage_stats": {
                "download_count": 0,
                "organizations_using": [],
                "last_accessed": None
            },
            "governance_status": {
                "compliance_verified": False,
                "last_audit": None,
                "approval_status": "pending"
            }
        }
        
        self.models[full_model_id] = model_record
        
        # Trigger governance evaluation
        await self._trigger_governance_evaluation(full_model_id)
        
        # Log registration
        await self._log_audit_event(
            "model_registered",
            {"model_id": full_model_id, "organization": requesting_org}
        )
        
        return full_model_id
    
    async def request_model_access(self, access_request: AccessRequest) -> str:
        """Request access to a model from another organization"""
        
        model = self.models.get(access_request.model_id)
        if not model:
            raise HTTPException(status_code=404, detail="Model not found")
        
        # Check if model is accessible
        if not await self._can_access_model(access_request.requesting_org_id, model):
            raise HTTPException(
                status_code=403, 
                detail="Model not accessible to requesting organization"
            )
        
        # Generate access request ID
        access_request_id = str(uuid.uuid4())
        
        # Store access request
        request_record = {
            "id": access_request_id,
            "model_id": access_request.model_id,
            "requesting_org": access_request.requesting_org_id,
            "requesting_team": access_request.requesting_team_id,
            "model_owner_org": model["organization_id"],
            "purpose": access_request.access_purpose,
            "intended_use": access_request.intended_use,
            "duration_days": access_request.duration_days,
            "data_sharing_terms": access_request.data_sharing_terms,
            "status": "pending",
            "requested_at": datetime.now(),
            "expires_at": datetime.now() + timedelta(days=access_request.duration_days or 30)
        }
        
        self.access_grants[access_request_id] = request_record
        
        # Auto-approve based on access level and organization relationships
        if await self._should_auto_approve(access_request, model):
            await self._approve_access_request(access_request_id)
        else:
            # Send notification to model owner organization
            await self._notify_access_request(access_request_id)
        
        return access_request_id
    
    async def _can_access_model(self, requesting_org: str, model: Dict) -> bool:
        """Check if an organization can access a model"""
        access_level = model["access_level"]
        model_org = model["organization_id"]
        
        # Public models are accessible to all
        if access_level == AccessLevel.PUBLIC:
            return True
        
        # Private models only to owning organization
        if access_level == AccessLevel.PRIVATE:
            return requesting_org == model_org
        
        # Organization level - check collaboration agreements
        if access_level == AccessLevel.ORGANIZATION:
            org = self.organizations.get(requesting_org)
            if not org:
                return False
            
            model_owner_org = self.organizations.get(model_org)
            if not model_owner_org:
                return False
            
            # Check if organizations have collaboration agreement
            return any(
                agreement["partner_org"] == model_org 
                for agreement in org.get("collaboration_agreements", [])
            )
        
        # Consortium level - check consortium membership
        if access_level == AccessLevel.CONSORTIUM:
            return await self._check_consortium_membership(requesting_org, model_org)
        
        return False
    
    async def _should_auto_approve(self, access_request: AccessRequest, model: Dict) -> bool:
        """Determine if access request should be auto-approved"""
        requesting_org = self.organizations.get(access_request.requesting_org_id)
        model_owner_org = self.organizations.get(model["organization_id"])
        
        if not requesting_org or not model_owner_org:
            return False
        
        # Auto-approve for trusted partners
        trusted_partners = model_owner_org.get("trusted_partners", [])
        if access_request.requesting_org_id in trusted_partners:
            return True
        
        # Auto-approve for research organizations accessing research models
        if (requesting_org["tier"] == OrganizationTier.RESEARCH and 
            model.get("research_use_approved", False)):
            return True
        
        # Auto-approve for academic use
        if (requesting_org["tier"] == OrganizationTier.ACADEMIC and
            model.get("academic_use_approved", False)):
            return True
        
        return False
    
    async def search_federated_models(self, 
                                    requesting_org: str,
                                    query: Optional[str] = None,
                                    model_type: Optional[str] = None,
                                    framework: Optional[str] = None,
                                    tags: Optional[List[str]] = None) -> List[Dict]:
        """Search for models across the federation"""
        
        accessible_models = []
        
        for model_id, model in self.models.items():
            # Check if requesting org can see this model
            if not await self._can_access_model(requesting_org, model):
                continue
            
            # Apply search filters
            if query and query.lower() not in model["name"].lower() and query.lower() not in model["description"].lower():
                continue
            
            if model_type and model["model_type"] != model_type:
                continue
            
            if framework and model["framework"] != framework:
                continue
            
            if tags and not any(tag in model["tags"] for tag in tags):
                continue
            
            # Create safe model summary (no sensitive data)
            model_summary = {
                "id": model["id"],
                "name": model["name"],
                "version": model["version"],
                "description": model["description"],
                "organization": self.organizations[model["organization_id"]]["name"],
                "model_type": model["model_type"],
                "framework": model["framework"],
                "access_level": model["access_level"],
                "status": model["status"],
                "performance_summary": {
                    "accuracy": model["performance_metrics"].get("accuracy"),
                    "latency": model["performance_metrics"].get("latency")
                },
                "tags": model["tags"],
                "created_at": model["created_at"],
                "usage_stats": {
                    "download_count": model["usage_stats"]["download_count"],
                    "organizations_using_count": len(model["usage_stats"]["organizations_using"])
                }
            }
            
            accessible_models.append(model_summary)
        
        # Sort by relevance and usage
        accessible_models.sort(
            key=lambda x: (x["usage_stats"]["download_count"], x["created_at"]), 
            reverse=True
        )
        
        return accessible_models
    
    async def track_model_usage(self, model_id: str, using_org: str, usage_type: str):
        """Track model usage for analytics and compliance"""
        if model_id not in self.models:
            return
        
        usage_record = {
            "model_id": model_id,
            "using_organization": using_org,
            "usage_type": usage_type,  # download, inference, training
            "timestamp": datetime.now(),
            "usage_id": str(uuid.uuid4())
        }
        
        # Store usage record
        if model_id not in self.usage_tracking:
            self.usage_tracking[model_id] = []
        
        self.usage_tracking[model_id].append(usage_record)
        
        # Update model usage stats
        model = self.models[model_id]
        model["usage_stats"]["last_accessed"] = datetime.now()
        
        if usage_type == "download":
            model["usage_stats"]["download_count"] += 1
            
        if using_org not in model["usage_stats"]["organizations_using"]:
            model["usage_stats"]["organizations_using"].append(using_org)
        
        # Log for audit
        await self._log_audit_event(
            "model_usage_tracked",
            {"model_id": model_id, "using_org": using_org, "usage_type": usage_type}
        )

# FastAPI endpoints
registry = FederatedModelRegistry()

@app.post("/api/v1/models/register")
async def register_model(model: ModelMetadata, credentials: HTTPAuthorizationCredentials = Security(security)):
    """Register a model in the federated registry"""
    org_id = await authenticate_organization(credentials.credentials)
    model_id = await registry.register_model(model, org_id)
    return {"model_id": model_id, "status": "registered"}

@app.post("/api/v1/models/access-request")
async def request_access(access_request: AccessRequest, credentials: HTTPAuthorizationCredentials = Security(security)):
    """Request access to a model"""
    await authenticate_organization(credentials.credentials)
    request_id = await registry.request_model_access(access_request)
    return {"access_request_id": request_id, "status": "submitted"}

@app.get("/api/v1/models/search")
async def search_models(
    credentials: HTTPAuthorizationCredentials = Security(security),
    query: Optional[str] = None,
    model_type: Optional[str] = None,
    framework: Optional[str] = None
):
    """Search for accessible models in the federation"""
    org_id = await authenticate_organization(credentials.credentials)
    models = await registry.search_federated_models(org_id, query, model_type, framework)
    return {"models": models, "count": len(models)}

async def authenticate_organization(token: str) -> str:
    """Authenticate organization and return org_id"""
    # Implementation would validate JWT token and return org_id
    return "authenticated_org_id"

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8001)

💡 Federated AI Governance Implementation Best Practices

Next Steps