Mitigating Data Leakage in High-Compliance Environments: A Governance Framework for Local-LLM Adoption

The rapid democratization of Generative AI has introduced "Shadow AI" vulnerabilities in high-compliance sectors, particularly where operational staff bypass complex security protocols to utilize cloud-based models. This paper proposes a Governance, Risk, and Compliance (GRC) framework centered on "Local-First" AI. We introduce a secure local agent, a standalone privacy architecture designed to reconcile strict data sovereignty with usability. The system makes two primary contributions: (1) A Hardened Inference Engine compiled via Nuitka, utilizing dynamic hardware abstraction and ephemeral memory decryption to physically prevent forensic data recovery on standard hardware. (2) A Compliance-Focused Anthropomorphic User Experience (AUX) designed to mitigate "Security Fatigue." By implementing a conversational agent interface rather than a terminal command line, the system leverages the psychological principle of social presence to lower the barrier to entry for non-technical operational staff (e.g., in construction or healthcare). This ensures high adoption rates of the secure, air-gapped tool while maintaining a zero-trust posture against data exfiltration.