TRACE: A Multi-dimensional Framework for Performance-cost Evaluation of AI Governance Strategies in Large Language Models

Large language models deployed in production environments require robust governance to mitigate factual inaccuracies, toxic content, and privacy violations, yet practitioners lack systematic methods to evaluate the effectiveness of governance interventions relative to their computational cost.We introduce TRACE (Transparent Risk Assessment and Cost Evaluation), a multi-dimensional framework that quantifies governance performance across factuality, toxicity, and PII exposure while simultaneously measuring operational overhead.We evaluate five governance strategies-system prompting, output filtering, retrieval-augmented generation with citation, constitutional AI, and ensemble methods-across three state-of-the-art models (GPT-4o, Claude Sonnet 4, Llama-3-70B) using 1,817 test cases.Ensemble methods deliver the highest overall improvements (71% factuality gain, 88% toxicity reduction, 91% PII protection) with 54-65% variance reduction, though at a cost of 1,420 ms latency overhead.Output filtering offers the most efficient trade-off, providing 77-78% risk reduction at only 280 ms overhead.Critically, risk dimensions show near-zero correlation (r=-0.02),indicating that gains in one dimension do not translate to others and necessitating holistic governance strategies.By systematically quantifying safety-cost trade-offs across multiple risk dimensions, TRACE enables practitioners to make evidence-based decisions about governance investments.