This technical white paper introduces Kernel 10, a deterministic governance layer designed to mitigate stochastic drift and semantic instability in Large Language Model (LLM) deployments. Kernel 10 operates as a model-agnostic, post-inference constraint shell that enforces context-dependent deterministic rules and generates cryptographically verifiable audit artifacts. Through large-scale simulation-based validation (500,000 interactions) and advanced statistical analysis (two-way ANCOVA), the framework evidences—under controlled simulated conditions—a significant reduction in constraint violations and uncontrolled variance. Kernel 10 is positioned as a governance and auditability framework aligned with Articles 14 and 15 of the EU AI Act, explicitly without modifying model internals or replacing mandatory human oversight.