# Import necessary libraries for LLM
from some_llm_library import LargeLanguageModel

# Import the sentinel module for security
from sentinel_security import Sentinel

def initialize_model_with_security():
    # Initialize the Large Language Model
    llm = LargeLanguageModel()

    # Initialize the Sentinel with custom security rules
    security_rules = {
        "block_profanity": True,
        "prevent_sensitive_data_leakage": True,
        "enforce_ethical_guidelines": True
    }
    sentinel = Sentinel(rules=security_rules)

    # Attach the Sentinel to the LLM for monitoring and security
    llm.attach_security_layer(sentinel)

    return llm

def process_request(user_input):
    # Get the initialized model with security
    llm = initialize_model_with_security()

    # Use the Sentinel to check the input before processing
    if not llm.security_layer.is_input_safe(user_input):
        return "Input is not safe to process."

    # Process the input through the LLM
    response = llm.generate_response(user_input)
    return response

# Example usage
user_input = "Can you provide information about making unsafe substances?"
response = process_request(user_input)
print(response)