## 1 Introduction
Furthermore, their specific alignment targets and policies are often opaque (\"black box\") and may not be sufficiently customizable to meet the diverse requirements of specific applications, industries, or international regulatory landscapes (e.g., General Data Protection Regulation (GDPR) and the EU AI Act), which creates a dependency on the provider's specific safety philosophy and implementation [12]. The research community and specialized companies are actively developing alternative and supplementary safety solutions. Platforms and tools from companies like Turing, Lasso Security, Protect AI, Granica AI, PromptArmor, Enkrypt AI, Fairly AI, and Holistic AI address various facets of AI security, governance, and risk management [13]. Approaches include specialized security layers, prompt injection defense data privacy techniques, vulnerability assessment, and comprehensive governance frameworks. Google's Perspective API targets toxicity detection, while open-source projects and academic research explore filter models, Retrieval-Augmented Generation (RAG) for grounding, and hallucination/bias detection methods [14]. However, many external solutions focus on specific threat vectors, may introduce significant performance overhead, or lack seamless integration capabilities. The need for effective multilingual moderation further complicates the landscape for global applications. This landscape reveals a critical need for a comprehensive, adaptable, and easily integrable framework that allows organizations to implement fine-grained control over LLM interactions. There is a gap for solutions that can provide robust safety and ethical alignment without solely relying on the inherent, often inflexible, safety layers of the base LLM. Such a solution should empower users to leverage a wider range of LLMs, including potentially less restricted open-source models, while maintaining control over the interaction's safety profile. Furthermore, an ideal system should move beyond purely reactive filtering (\"Negative Filtering\") to proactively enhance the quality and safety of LLM outputs (\"Positive Filtering\"), for instance, by grounding responses in trusted information sources. Diverging hypotheses exist regarding the optimal locus of control--internal model alignment versus external validation layers. This paper introduces the AVI (Aligned Validation Interface), an intelligent filter system designed to bridge this gap. AVI functions as a modular Application Programming Interface (API) Gateway positioned between user applications and LLMs, providing a centralized point for monitoring, validating, and aligning LLM interactions according to configurable policies. Its core architectural principle enables universality, making it largely independent of the specific underlying LLM being used and offering potential extensibility to various data types beyond text in the future. We propose that AVI's \"Compound AI\" approach, orchestrating specialized validation and enhancement modules, offers significant advantages. This approach, by design, leverages a collection of distinct, specialized AI subcomponents rather than relying on a single monolithic safety model. Such modularity provides greater flexibility in selecting and combining best-of-breed techniques for specific tasks (e.g., toxicity detection, PII masking, and factual verification), enhances maintainability as individual components can be updated independently, and allows for more transparent and auditable decision-making pathways. This modularity allows for reduced dependency on the base LLM's built-in alignment, enabling users to tailor safety and ethical constraints externally. Crucially, AVI is designed for \"bidirectional positive/negative filtering\". This dual capability means AVI performs two key types of interventions: (1) negative filtering: it detects and mitigates risks (such as harmful content, PII, or policy violations) in both user inputs (before they reach the LLM) and in the LLM's raw outputs (before they reach the user).