OpenAI LLM Risk Mitigation Framework

OpenAI published a structured framework for identifying and mitigating risks in LLM deployment.

Risk Categories

1. Hallucination: False information presented factually. Mitigated by RAG, citation requirements, and confidence calibration.
2. Bias: Unfair treatment across demographics. Mitigated by diverse training data, bias evaluation, and output filtering.
3. Misuse: Malicious use of capabilities. Mitigated by usage monitoring, rate limiting, and content filtering.
4. Security: Prompt injection, data extraction. Mitigated by input sanitization, output validation, and access controls.

Mitigation Layers

Layer 1: Input filtering blocks malicious prompts before they reach the model. Layer 2: Model-level safeguards through alignment training. Layer 3: Output filtering catches harmful or unsafe completions. Layer 4: Monitoring detects abuse patterns over time.

Best Practice

No single mitigation is sufficient. Production systems require all four layers operating simultaneously.