Promptsecure: Secure Prompt Engineering Protocols for Regulated Genai Environments

The rapid proliferation of Generative AI (GenAI) technologies has introduced a new era of content creation, automation, and intelligence augmentation. However, the growing reliance on prompt-based interfaces within these models has surfaced critical concerns related to prompt injection, data leakage, adversarial manipulation, and regulatory non- compliance. Despite advancements in large language models (LLMs), the absence of standardized and secure prompt engineering frameworks leaves a vulnerability gap—especially in high-stakes and regulated domains such as healthcare, law, finance, and government operations. This research proposes PromptSecure, a comprehensive protocol-driven framework that introduces secure, context-aware, and auditable prompt engineering methodologies designed for GenAI deployments in regulated environments. Unlike traditional prompt tuning approaches that prioritize model performance, PromptSecure integrates principles from cybersecurity, differential privacy, and software verification to construct a hardened prompt lifecycle—from design and sanitization to execution and monitoring. The protocol encapsulates both static and dynamic prompt validation mechanisms, role-based access control for sensitive prompt execution, and traceable prompt history management using secure audit trails. PromptSecure also incorporates a layered compliance scaffold tailored to conform with GDPR, HIPAA, ISO/IEC 27001, and other global AI governance directives. Experimental evaluation within sandboxed enterprise-grade GenAI environments demonstrates PromptSecure’s capability to mitigate injection risks, enforce prompt boundaries, and retain system integrity under adversarial probing. This study fills a critical research gap at the intersection of prompt engineering and AI governance, and lays the groundwork for establishing secure-by-design GenAI practices essential for building public trust and institutional adoption of foundation models.