LLM-Moderation and Safety Layer - Phase 1

Overview

An LLM-Moderation and Safety Layer is a system designed to ensure that large language models (LLMs) function within safe, ethical, and compliant norms. Acting as a buffer between the model and users, it prevents outputs that may be harmful, biased, or violate policies.

iX Hello→ Moderation & Safety Layer → LLM Inference Services

This layer functions as a gatekeeper, implementing:

• Pre-Generation Filters: Validates prompts before sending them to the LLM.

• Post-Generation Filters: Scans responses before delivery.

Why LLM?

LLMs fill critical gaps left by traditional language systems and unlock advanced capabilities for modern businesses. Here’s why they matter:

1. Master Complex Language Tasks Human language is rich, nuanced, and context dependent. Unlike rule-based or small NLP models, LLMs can interpret intent, tone, and context, making interactions more natural and accurate.

2. Scale Across Multiple Domains A single LLM can power customer support, content creation, coding assistance, knowledge retrieval, and more eliminating the need for separate models for each task.

3. Boost Productivity LLMs automate repetitive tasks like document summarization, email drafting, and FAQ responses, saving time for professionals in legal, finance, healthcare, and product management.

4. Enable Smarter Decisions They process vast amounts of text quickly, extract actionable insights, and help businesses make data-driven decisions faster.

5. Adapt and Continuously Improve LLMs can be fine-tuned for specific industries, compliance needs, and learn from feedback to enhance accuracy, safety, and relevance.

Purpose of LLM Moderation : Safety and Compliance Features for AI Systems

1. Content Filtering: Utilize classifiers or rule-based systems to detect and block unsafe outputs like hate speech, sexual content, violence, and misinformation, by effectively flagging high-risk responses.

2. Policy Enforcement: Ensure alignment with organizational or regulatory guidelines by applying custom rules in sensitive domains such as finance, healthcare, and politics.

3. Compliance and Legal Safety: Prevent sharing of Personally Identifiable Information (PII) or confidential data. Ensure adherence to GDPR, HIPAA, and organizational security standards.

4. Protect Brand Reputation: Maintain a professional tone and prevent embarrassing or harmful outputs to build trust through safe and respectful communication.

5. Improve Accuracy and Reliability: Flag hallucinations or misleading information before reaching users to maintain trustworthy responses in business workflows.

6. Integration: Intercept user inputs and model outputs before final response delivery. Provide API hooks or middleware for easy integration across services.

7. Contextual Safety: Understand user intent and context to prevent misuse such as phishing or fraud. Use dynamic risk scoring based on conversation flow.

8. Human-in-the-Loop: Escalate flagged cases to human moderators for review and provide override mechanisms for critical decisions.

9. Audit and Logging: Maintain logs for compliance and forensic analysis, enabling transparency for regulatory audits.

How LLM Moderation & Safety Layer acts in iX Hello

Moderation Layer Overview

The moderation layer sits between the user interface and the LLM inference service. Its key functions are:

• Reviewing incoming prompts and outgoing model responses.

• Using machine learning and rule-based filters.

The moderation system can:

• Allow, redact, warn, or block content.

• Integrate smoothly with current workflows.

• Support custom policies.

• Log all actions for auditing and analysis.

Demo App Overview

Our demo app leverages Azure's AI Content Safety to moderate inputs across four categories: self-harm, violence, sexual content, and hate speech.

Chatbot Implementation

Each message sent in the chat is moderated by the language model. Messages that pass the moderation check proceed with the flagged status.

Content Moderation

Inputs categorized under self-harm, violence, sexual content, or hate speech are automatically flagged.

Alert System

If inappropriate content is detected, a pop-up alert will notify users with the message "Content flagged by moderation."

This preliminary version of the app focuses on basic functionality, marking random inputs not in the flagged categories as safe.

Flag Handling in the Application: Currently, flag management is controlled through app secrets, accessible only to authorized personnel, such as the product owner. As a user, you can't enable or disable features like moderation within the application. Presently, in the development environment, the moderation flag is enabled. Future plans may include an option within the application for easier flag management.

Conversation of the bot and the showcase of LLM-Moderation & Safety Layer