## 3 Materials and Methods  
This study introduces and evaluates the Aligned Validation Interface (AVI), a conceptual framework designed to enhance the safety, reliability, and ethical alignment of interactions with large language models (LLMs). The AVI system operates as an intelligent API gateway, intercepting communication between user applications and downstream LLMs to apply configurable validation and modification policies. This architecture ensures LLM agnosticism and facilitates centralized control and monitoring. Underlying AVI is a Compound AI approach, orchestrating multiple specialized modules rather than relying on a single monolithic safety model. This modular design enhances flexibility and allows for the integration of diverse validation techniques (Figure 1). The primary goal of AVI is to enable safer, more reliable, and ethically aligned interactions by proactively validating and modifying the communication flow based on configurable policies, offering an alternative or supplement to relying solely on the often opaque and inflexible internal safety mechanisms built into many proprietary LLMs [37, 38]. AVI aims to provide organizations with granular control over LLM interactions, adaptability to specific operational contexts and regulatory requirements (e.g., GDPR and the EU AI Act [39]), and compatibility with a diverse ecosystem of LLMs, thereby promoting responsible AI innovation and deployment across critical sectors such as finance, healthcare, and education [40]. Source: made by authors based on [36]. \end{table}
Table 2: Cont. Architecturally, AVI functions as an API gateway, positioning it as a mandatory checkpoint (Figure 2). The typical data processing sequence involves: (1) receiving an incoming request; (2) pre-processing and input validation via the Input Validation Module (IVM); (3) optional context retrieval and augmentation via the Contextualization Engine (CE/RAG); (4) forwarding the processed request to the designated LLM; (5) intercepting the LLM's response; (6) post-processing, assessment, and validation of the response via the Response Assessment Module (RAM) and Output Validation Module (OVM); (7) delivering the final, validated response. This gateway architecture provides LLM agnosticism, centralized policy enforcement, simplified application integration, and comprehensive logging capabilities. The Compound AI philosophy underpins this, utilizing an ensemble of specialized modules for distinct validation and enhancement tasks, promoting flexibility, maintainability, and the integration of diverse techniques (rule-based, ML-based, heuristic) [41]. The core modules within the AVI framework are designed as follows: The Input Validation Module (IVM) analyzes incoming requests for security threats (e.g., prompt injection), PII, and policy violations (e.g., forbidden topics) (Figure 3). Conceptually, it combines rule-based systems (regex and dictionaries), ML classifiers (e.g., fine-tuned models based on architectures like Llama 3, Mistral, or DeBERTa V3 for semantic threat analysis), and NER models (e.g., spaCy-based [41] or transformer-based) for PII detection [42]. The Output Validation Module (OVM) scrutinizes the LLM's generated response for harmful content (toxicity and hate speech), potential biases, PII leakage, and policy compliance, using similar validation techniques tailored for generated text. The Response Assessment Module (RAM) evaluates the response quality, focusing on factual accuracy (hallucination detection) and relevance. Methodologies include NLI models [43] or semantic similarity (e.g., SBERT [44]) for checking consistency with RAG context, internal consistency analysis, and potentially LLM self-critique mechanisms using CoT prompting [45]. The Contextualization Engine (CE/RAG) enables \"Positive Filtering\" by retrieving relevant, trusted information to  
Figure 1: High-level diagram of AVI as an API gateway. Source: made by authors. ground the LLM's response.