## 1 Introduction  
In this paper, we propose the Aligned Validation Interface (AVI), an intelligent, LLM-agnostic API gateway that provides modular, bidirectional filtering for the safe and ethical deployment of large language models. Unlike existing solutions, AVI combines negative filtering (e.g., prompt injection and PII detection) with positive filtering through RAG-based contextual grounding, enabling fine-grained policy control across diverse application domains. This framework introduces a scalable and adaptable architectural pattern that enhances trust, transparency, and compliance in LLM interactions. The rapid proliferation and integration of large language models (LLMs) represent a paradigm shift across numerous domains, including scientific research, business operations, education, content creation, and daily communication [1]. These powerful models, exemplified by architectures like GPT-4, Claude, Llama, Mistral, and Gemini [2], demonstrate remarkable capabilities in understanding and generating human-like text, translating languages, summarizing complex information, and even writing creative content and code. Their potential to enhance productivity, accelerate discovery, and democratize access to information is immense, driving their widespread adoption by organizations and individuals globally [3]. However, this transformative potential is intrinsically linked with significant and multifaceted risks that challenge their safe, ethical, and reliable deployment [4; 5]. As LLMs become more deeply embedded in critical applications and user interactions, concerns regarding their security vulnerabilities, potential for generating harmful or biased content, propensity for factual inaccuracies (hallucinations), and risks to data privacy have become paramount [6; 7]. The flexibility and generative power that make LLMs so valuable create attack surfaces and potential failure modes that are different significantly from traditional software systems. Security risks include prompt injection attacks, where malicious inputs can hijack the model's intended function, data poisoning during training, and potential denial-of-service vectors [7]. Ethical concerns are equally pressing, encompassing the generation and amplification of biased language reflecting societal prejudices (gender, race, etc. ), the creation of toxic or hateful content, the spread of misinformation and disinformation at scale, and the potential for manipulative use [8]. Furthermore, LLMs trained on vast datasets may inadvertently leak sensitive personal identifiable information (PII) or confidential corporate data present in their training corpus or user prompts [8]. Finally, the inherent nature of current generative models leads to the phenomenon of \"hallucinations\"--the generation of plausible but factually incorrect or nonsensical statements--undermining their reliability, particularly in knowledge-intensive applications [9]. The OWASP Top 10 for LLM Applications project (now, the OWASP Gen AI Security Project) underscores the industry-wide recognition of these critical vulnerabilities [9]. These challenges are particularly acute in sectors like finance, healthcare, legal services, and public administration, where trust, accuracy, and compliance are non-negotiable [10]. In response to these challenges, various approaches to LLM safety and content moderation have emerged. Major LLM developers like OpenAI and Anthropic have incorporated safety mechanisms directly into their models and APIs, often through extensive alignment processes like Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI [10]. OpenAI offers a Moderation API, while Anthropic's models are designed with inherent safety principles [11]. While valuable, relying solely on this built-in alignmentpresents limitations. These internal safety layers can sometimes be overly restrictive, leading to refusals to answer legitimate queries or exhibiting a form of \"over-censorship\" that hinders utility [11].