AI-Security Methodology
Role-Pseudonymous Prompting Protocol (RPP)
RPP Paper
Devised by Cruz Macias – An Essential AI-Security Innovation: This methodology, which I personally developed, is of paramount importance for protecting user privacy in AI interactions. RPP is an AI-Security Methodology that I devised which aims to protect the identity of the user, and it is working fantastically.
Role-Pseudonymous Prompting Protocol (RPP) is an AI-security method that auto-converts all prompts into third-person, role-based, de-identified case descriptions, ensuring privacy, neutrality, and safe AI interaction across calls, tools, and logs.
The Role-Pseudonymous Prompting Protocol (RPP) is an AI-security methodology designed to safeguard user identity by enforcing de-identification at the prompt level. All inputs are automatically converted into third-person, role-based, neutral case descriptions that retain only task-relevant facts while omitting personal pronouns, identifiers, or emotional framing. This transformation ensures that language models receive prompts framed as generalizable cases rather than personal narratives, thereby reducing privacy risk, mitigating bias, and aligning with responsible AI use in sensitive domains. RPP is applied consistently across all model calls, tool interactions, and system logs, with non-compliant prompts rewritten automatically. By embedding pseudonymization directly into the interaction layer, RPP establishes a robust, domain-agnostic safeguard for secure and ethical AI deployment.
Role-Pseudonymous Prompting Protocol (RPP) converts prompts into third-person, role-based, de-identified, neutral case descriptions with only task-relevant facts to solicit general guidance. Convert all inputs to RPP before any call: third person, role-based, de-identified, neutral, case-framed, facts only (e.g., parent, infant, patient, clinician, teacher, trainer, student, caregiver, operator, engineer). Within RPP, the terms Scribere / Scriber / Scribe denote the Neutral Author-Role—a standardized, identity-free pseudonym for the person generating input. This term replaces self-referential identifiers such as “I,” “me,” “my,” or “the user,” ensuring that authorship is acknowledged without exposing personal identity. Apply RPP to all sub-calls, logs, and outputs; auto-rewrite noncompliant prompts, substituting the Neutral Author-Role (“Scribere/Scriber/Scribe”) wherever self-reference occurs.
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Read these papers and dive deeper into the theory and application of Orthogonal Prompt Engineering:
Orthogonality and High-Dimensional Vector Spaces in Generative AI Music Creation
Use Case - Creating a Song with Targeted Emotional and Stylistic Nuance
Some MCP Servers that I built:
My favorite Agent Instructions:
Motivational Tutoring Poster