pip install secureaiml

from secureml import SecureModel
import joblib

# Load your model
model = joblib.load("model.pkl")

# Secure it in one line
secure_model = SecureModel(model)
secure_model.sign_and_save("model.sml", identity="[email protected]")

# Load and verify
verified_model = SecureModel.load("model.sml", verify=True)
predictions = verified_model.predict(X_test)

from secureml import SecureModel
import joblib

# Train your model (any framework)
from xgboost import XGBClassifier
model = XGBClassifier()
model.fit(X_train, y_train)

# Secure it
secure_model = SecureModel(model)
secure_model.sign_and_save(
    "fraud_detection_model.sml",
    identity="[email protected]",
    version="2.0.0",
    description="Production fraud detection model"
)

# Load and verify
model = SecureModel.load("fraud_detection_model.sml", verify=True)
if model.is_verified:
    predictions = model.predict(X_test)

from secureml.api.advanced import AdvancedSecureModel
from secureml.utils.config import SecurityConfig, SecurityLevel, ComplianceFramework

# Configure enterprise security
config = SecurityConfig.from_level(SecurityLevel.ENTERPRISE)
config.enable_fingerprinting = True
config.enable_merkle_trees = True
config.compliance_frameworks = [ComplianceFramework.SOC2, ComplianceFramework.ISO27001]

# Create advanced secure model
advanced = AdvancedSecureModel(model, config=config)

# Sign with AWS KMS
advanced.add_signature(
    identity="[email protected]",
    use_cloud_kms=True,
    kms_key_id="arn:aws:kms:us-east-1:123456789:key/abc-def",
    cloud_provider="aws"
)

# Validate compliance
compliance_report = advanced.validate_compliance(
    frameworks=[ComplianceFramework.SOC2, ComplianceFramework.HIPAA],
    generate_report=True,
    report_path="compliance_report.json"
)

print(f"Compliance Status: {compliance_report['overall_status']}")

┌─────────────────────────────────────────────────────┐
│            Your Application                         │
└─────────────────────────────────────────────────────┘
                      ↓
┌─────────────────────────────────────────────────────┐
│            SecureML API Layer                       │
│  • Simple API  • Advanced API  • CLI                │
└─────────────────────────────────────────────────────┘
                      ↓
┌─────────────────────────────────────────────────────┐
│         SecureML Enterprise Features                │
│  • HSM/KMS  • Compliance  • Audit  • Forensics      │
└─────────────────────────────────────────────────────┘
                      ↓
┌─────────────────────────────────────────────────────┐
│         OpenSSF Model Signing (Core)                │
│         Sigstore Infrastructure                     │
└─────────────────────────────────────────────────────┘

from secureml import SecureModel
import xgboost as xgb

model = xgb.XGBClassifier()
model.fit(X_train, y_train)

secure_model = SecureModel(model)
secure_model.sign_and_save("xgb_model.sml", identity="[email protected]")

import torch
from secureml import SecureModel

model = torch.nn.Sequential(...)
torch.save(model.state_dict(), "model.pth")

secure_model = SecureModel.load_from_path("model.pth")
secure_model.sign_and_save("pytorch_model.sml", identity="[email protected]")

from transformers import AutoModel
from secureml import SecureModel

model = AutoModel.from_pretrained("bert-base-uncased")
model.save_pretrained("./my_model")

secure_model = SecureModel.load_from_path("./my_model")
secure_model.sign_and_save("bert_model.sml", identity="[email protected]")

# Basic installation
pip install secureaiml

# With ML framework support
pip install secureaiml[xgboost,pytorch,sklearn]

# With CLI tools
pip install secureaiml[cli]

# Everything (all ML frameworks + CLI + dev tools)
pip install secureaiml[all]

# Sign a model
secureml sign model.pkl --identity "[email protected]" --output model.sml

# Verify a model
secureml verify model.sml

# Get model info
secureml info model.sml

# Validate compliance
secureml compliance model.sml --framework soc2 --framework iso27001

# Generate audit report
secureml audit --start-date 2024-01-01 --end-date 2024-12-31 --output audit.json

import mlflow
from secureml.integrations.mlflow_integration import SecureMLflowModel

with mlflow.start_run():
    model = train_model()

    # Log with SecureML
    secure_model = SecureMLflowModel(model)
    secure_model.log_model(
        "model",
        signature=True,
        identity="[email protected]"
    )

from secureml.integrations.huggingface_integration import SecureHFModel

secure_model = SecureHFModel.from_pretrained("bert-base-uncased")
secure_model.sign(identity="[email protected]")
secure_model.push_to_hub("my-org/secure-bert", signed=True)