Joe Bloggs
11 Jan 2022
•
5
min read
PhishNet™ by Sentinel is your front-line defense against the ever-evolving threats of phishing attempts and malicious content, ensuring your email remains a safe, efficient tool for communication.
Tailor SecureChat's security settings to fit the unique needs of your business and customer interactions.
Utilizes advanced algorithms to detect and neutralize potential threats like prompt injections and unauthorized data access in real-time.
Ensures chatbot interactions adhere to global data privacy standards including GDPR, HIPAA, and more.
Easily integrates with existing chatbot platforms, enhancing security without disrupting user experience.
Implement robust protection effortlessly with just a single line of code. Lakera Guard offers comprehensive security for your organization, adaptable for both cloud and on-premises deployment.
Simplify AI Deployment
Eliminate the stress of security concerns and swiftly transition your innovative LLM projects into live environments. Begin your journey in under five minutes, at no initial cost.
Strengthen Your Defences Daily
Lakera’s extensive threat intelligence repository is brimming with millions of attack instances, expanding daily by over 100,000 new entries. With Lakera Guard, your security measures grow more resilient each day.
# 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)
With Sentinel, you tap into the most advanced AI security technology. Our solutions are crafted to anticipate, identify, and neutralize threats before they become a problem.
Navigating the complex world of data privacy and compliance is easier with Sentinel.
Whether you're in finance, healthcare, education, or e-commerce.
Python (version 3.6 or later) • Access to SecureChat API • An existing Python-based chatbot platform
Step 1
# Run this command in your terminal to install the SecureChat SDK
pip install securechat-sdkStep 2
# Import the SecureChat SDK into your Python script
from securechat import SecureChatClientStep 3
# Replace 'your_api_key' with the API key provided by Sentinel
securechat_client = SecureChatClient(api_key='your_api_key')Step 4
def handle_chat_message(user_message):
# Use SecureChat's threat detection on the incoming message
is_safe, analysis = securechat_client.analyze_message(user_message)
if not is_safe:
# Handle the threat according to the analysis
# For example, block the message or alert an admin
return "Message flagged for security concerns."
else:
# Continue with your chatbot's regular processing
response = your_chatbot.respond_to(user_message)
return responseStep 5
After integrating, thoroughly test the system with various scenarios to ensure SecureChat is correctly analyzing and flagging messages.
Sign Up Now and empower your AI journey with Sentinel - your partner in AI security excellence.
Your trusted resource for AI protection insights and trends
