chore(security): enhance environment configuration, CI workflows, and wallet daemon with security improvements

- Restructure .env.example with security-focused documentation, service-specific environment file references, and AWS Secrets Manager integration
- Update CLI tests workflow to single Python 3.13 version, add pytest-mock dependency, and consolidate test execution with coverage
- Add comprehensive security validation to package publishing workflow with manual approval gates, secret scanning, and release

migration_examples/MIGRATION_CHECKLIST.md

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+# GPU Acceleration Migration Checklist
+
+## ✅ Pre-Migration Preparation
+
+- [ ] Review existing CUDA-specific code
+- [ ] Identify all files that import CUDA modules
+- [ ] Document current CUDA usage patterns
+- [ ] Create backup of existing code
+- [ ] Test current functionality
+
+## ✅ Code Migration
+
+### Import Statements
+- [ ] Replace `from high_performance_cuda_accelerator import ...` with `from gpu_acceleration import ...`
+- [ ] Replace `from fastapi_cuda_zk_api import ...` with `from gpu_acceleration import ...`
+- [ ] Update all CUDA-specific imports
+
+### Function Calls
+- [ ] Replace `accelerator.field_add_cuda()` with `gpu.field_add()`
+- [ ] Replace `accelerator.field_mul_cuda()` with `gpu.field_mul()`
+- [ ] Replace `accelerator.multi_scalar_mul_cuda()` with `gpu.multi_scalar_mul()`
+- [ ] Update all CUDA-specific function calls
+
+### Initialization
+- [ ] Replace `HighPerformanceCUDAZKAccelerator()` with `GPUAccelerationManager()`
+- [ ] Replace `ProductionCUDAZKAPI()` with `create_gpu_manager()`
+- [ ] Add proper error handling for backend initialization
+
+### Error Handling
+- [ ] Add fallback handling for GPU failures
+- [ ] Update error messages to be backend-agnostic
+- [ ] Add backend information to error responses
+
+## ✅ Testing
+
+### Unit Tests
+- [ ] Update unit tests to use new interface
+- [ ] Test backend auto-detection
+- [ ] Test fallback to CPU
+- [ ] Test performance regression
+
+### Integration Tests
+- [ ] Test API endpoints with new backend
+- [ ] Test multi-backend scenarios
+- [ ] Test configuration options
+- [ ] Test error handling
+
+### Performance Tests
+- [ ] Benchmark new vs old implementation
+- [ ] Test performance with different backends
+- [ ] Verify no significant performance regression
+- [ ] Test memory usage
+
+## ✅ Documentation
+
+### Code Documentation
+- [ ] Update docstrings to be backend-agnostic
+- [ ] Add examples for new interface
+- [ ] Document configuration options
+- [ ] Update error handling documentation
+
+### API Documentation
+- [ ] Update API docs to reflect backend flexibility
+- [ ] Add backend information endpoints
+- [ ] Update performance monitoring docs
+- [ ] Document migration process
+
+### User Documentation
+- [ ] Update user guides with new examples
+- [ ] Document backend selection options
+- [ ] Add troubleshooting guide
+- [ ] Update installation instructions
+
+## ✅ Deployment
+
+### Configuration
+- [ ] Update deployment scripts
+- [ ] Add backend selection environment variables
+- [ ] Update monitoring for new metrics
+- [ ] Test deployment with different backends
+
+### Monitoring
+- [ ] Update monitoring to track backend usage
+- [ ] Add alerts for backend failures
+- [ ] Monitor performance metrics
+- [ ] Track fallback usage
+
+### Rollback Plan
+- [ ] Document rollback procedure
+- [ ] Test rollback process
+- [ ] Prepare backup deployment
+- [ ] Create rollback triggers
+
+## ✅ Validation
+
+### Functional Validation
+- [ ] All existing functionality works
+- [ ] New backend features work correctly
+- [ ] Error handling works as expected
+- [ ] Performance is acceptable
+
+### Security Validation
+- [ ] No new security vulnerabilities
+- [ ] Backend isolation works correctly
+- [ ] Input validation still works
+- [ ] Error messages don't leak information
+
+### Performance Validation
+- [ ] Performance meets requirements
+- [ ] Memory usage is acceptable
+- [ ] Scalability is maintained
+- [ ] Resource utilization is optimal

migration_examples/api_migration.py

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+#!/usr/bin/env python3
+"""
+API Migration Example
+
+Shows how to migrate FastAPI endpoints to use the new abstraction layer.
+"""
+
+# BEFORE (CUDA-specific API)
+# from fastapi_cuda_zk_api import ProductionCUDAZKAPI
+# 
+# cuda_api = ProductionCUDAZKAPI()
+# if not cuda_api.initialized:
+#     raise HTTPException(status_code=500, detail="CUDA not available")
+
+# AFTER (Backend-agnostic API)
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+from gpu_acceleration import GPUAccelerationManager, create_gpu_manager
+import numpy as np
+
+app = FastAPI(title="Refactored GPU API")
+
+# Initialize GPU manager (auto-detects best backend)
+gpu_manager = create_gpu_manager()
+
+class FieldOperation(BaseModel):
+    a: list[int]
+    b: list[int]
+
+@app.post("/field/add")
+async def field_add(op: FieldOperation):
+    """Perform field addition with any available backend."""
+    try:
+        a = np.array(op.a, dtype=np.uint64)
+        b = np.array(op.b, dtype=np.uint64)
+        result = gpu_manager.field_add(a, b)
+        return {"result": result.tolist()}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/backend/info")
+async def backend_info():
+    """Get current backend information."""
+    return gpu_manager.get_backend_info()
+
+@app.get("/performance/metrics")
+async def performance_metrics():
+    """Get performance metrics."""
+    return gpu_manager.get_performance_metrics()

migration_examples/basic_migration.py

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+#!/usr/bin/env python3
+"""
+Basic Migration Example
+
+Shows how to migrate from direct CUDA calls to the new abstraction layer.
+"""
+
+# BEFORE (Direct CUDA)
+# from high_performance_cuda_accelerator import HighPerformanceCUDAZKAccelerator
+# 
+# accelerator = HighPerformanceCUDAZKAccelerator()
+# if accelerator.initialized:
+#     result = accelerator.field_add_cuda(a, b)
+
+# AFTER (Abstraction Layer)
+import numpy as np
+from gpu_acceleration import GPUAccelerationManager, create_gpu_manager
+
+# Method 1: Auto-detect backend
+gpu = create_gpu_manager()
+gpu.initialize()
+
+a = np.array([1, 2, 3, 4], dtype=np.uint64)
+b = np.array([5, 6, 7, 8], dtype=np.uint64)
+
+result = gpu.field_add(a, b)
+print(f"Field addition result: {result}")
+
+# Method 2: Context manager (recommended)
+from gpu_acceleration import GPUAccelerationContext
+
+with GPUAccelerationContext() as gpu:
+    result = gpu.field_mul(a, b)
+    print(f"Field multiplication result: {result}")
+
+# Method 3: Quick functions
+from gpu_acceleration import quick_field_add
+
+result = quick_field_add(a, b)
+print(f"Quick field addition: {result}")

migration_examples/config_migration.py

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+#!/usr/bin/env python3
+"""
+Configuration Migration Example
+
+Shows how to migrate configuration to use the new abstraction layer.
+"""
+
+# BEFORE (CUDA-specific config)
+# cuda_config = {
+#     "lib_path": "./liboptimized_field_operations.so",
+#     "device_id": 0,
+#     "memory_limit": 8*1024*1024*1024
+# }
+
+# AFTER (Backend-agnostic config)
+from gpu_acceleration import ZKOperationConfig, GPUAccelerationManager, ComputeBackend
+
+# Configuration for any backend
+config = ZKOperationConfig(
+    batch_size=2048,
+    use_gpu=True,
+    fallback_to_cpu=True,
+    timeout=60.0,
+    memory_limit=8*1024*1024*1024  # 8GB
+)
+
+# Create manager with specific backend
+gpu = GPUAccelerationManager(backend=ComputeBackend.CUDA, config=config)
+gpu.initialize()
+
+# Or auto-detect with config
+from gpu_acceleration import create_gpu_manager
+gpu = create_gpu_manager(
+    backend="cuda",  # or None for auto-detect
+    batch_size=2048,
+    fallback_to_cpu=True,
+    timeout=60.0
+)