AI safety reduces harmful, unreliable, biased, or unintended behavior in generative AI systems. Enterprise practice combines output validation, controlled data, privacy protection, and proactive misuse prevention. section 13.1 section 1.1
Assess safety across four connected areas:
- AI safety concerns address misuse and unintended consequences from deploying generative AI, including inaccurate or irrelevant output. section 13.1
- Fairness addresses bias in data, model assumptions, and algorithmic design, with methods for detecting and correcting discriminatory effects. section 13.1
- Harm categories distinguish user harms such as privacy breaches and incorrect information, societal harms such as misinformation, and malicious use such as deepfakes or cyberattacks. section 13.1
- Fairness and discrimination analysis separates individual from distributional harms and considers procedural, distributive, and interactional fairness. section 13.1
Using RAG to improve enterprise safety
A retrieval-augmented generation system can ground responses in current enterprise data, provide sources, reduce training-data bias, and align retrieved information with internal policies and industry regulations. Controlling the retrieval corpus also makes privacy compliance more manageable than relying on uncontrolled public or generalized data. section 8.8
