DeepKnown-Guard: A Proprietary Model-Based Safety Response Framework for AI Agents

Overview

DeepKnown-Guard introduces a safety framework for Large Language Models (LLMs) to address security issues in AI deployment. It uses a fine-tuned safety classification model and Retrieval-Augmented Generation to enhance risk management and output reliability.

Key Insights

• Risk Recall Rate: Achieves a 99.3% risk recall rate through a four-tier taxonomy for input risk classification.
  • Evidence: Experimental results from the paper.
  • Verifiable: Yes
• Safety Score: Attains a 100% safety score on proprietary high-risk test sets.
  • Evidence: Experimental results from the paper.
  • Verifiable: Yes
• Output Reliability: Uses Retrieval-Augmented Generation to ensure responses are grounded in real-time knowledge, preventing information fabrication.
  • Evidence: Framework description in the paper.
  • Verifiable: Yes

BFSI Relevance

• Why Relevant: Ensures AI systems in BFSI sectors are secure and reliable, crucial for maintaining trust and compliance.
• Primary Sector: Financial Services
• Subsectors: Asset Management, Risk Management
• Actionable Implications:
  • Implement AI safety frameworks to enhance security.
  • Use fine-tuned models for risk classification and management.
  • Ensure AI outputs are traceable and grounded in verified data.