PRISM: Process-level Real-time Internal Safety Monitor for AI Systems

PRISM proposes a complementary approach to AI safety that monitors the quality of processing during generation, not just outputs. The framework comprises eleven dimensions of processing quality validated across five independent AI architectures (Claude, ChatGPT, Gemini, Grok, Copilot) with zero disagreements on direction across 55 data points, grounded computationally in token-level signals including entropy trajectory, branching factor, and KL divergence. A preliminary validation on GPT-2 produced a 7.2x difference in branching factor between coherent and distorted text. The framework is content-agnostic and includes a working proof-of-concept implementation.