## 2 Literature Review
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Principle** & **Description** & **Industry Example** \\ \hline Fairness & AI systems should treat all people fairly and avoid discrimination or bias. & In finance, AI-based credit scoring systems are audited to ensure equitable loan approvals across demographic groups. \\ \hline Reliability and Safety & AI systems should function reliably and safely, even under unexpected conditions. & In healthcare, diagnostic AI tools undergo rigorous testing to prevent harmful misdiagnoses. \\ \hline Privacy and Security & AI systems must ensure user data is protected and privacy is maintained. & In retail, recommendation engines are designed with data encryption and anonymization to protect customer information. \\ \hline Inclusiveness & AI should empower and engage a broad spectrum of users, including those with disabilities. & In education, AI-driven learning platforms include voice and visual support tools for students with special needs. \\ \hline Transparency & AI systems should be understandable, and users should know how decisions are made. & In legal services, document analysis tools include explainable AI models that clarify how case precedents are selected. \\ \hline Accountability & Developers and organizations must be accountable for how their AI systems operate. & In transportation, autonomous vehicle companies must track and take responsibility for decisions made by onboard AI systems. \\ \hline \hline \end{tabular} Source: made by authors based on [30].  
\end{table} 
Table 1: Microsoft’s responsible AI principles: descriptions and industry examples. limitations--modern models can handle tens of thousands of tokens, but even this may be insufficient for complex tasks. As a result, generative AI currently serves more as an auxiliary tool rather than an autonomous participant in the design process. For example, the rapid rise in popularity of ChatGPT4.0 is not just a result of marketing efforts but also reflects a genuine technological breakthrough in the development of generative neural networks [34]. Over the past two and a half years, model quality has significantly improved due to larger training datasets and extended pre-training periods. However, this stage of technological evolution is nearing completion and is now being complemented by a new direction: enhancing output quality by increasing computational resources allocated per individual response. These are known as \"reasoning models,\" which are capable of more advanced logical reasoning. In parallel, ongoing efforts are being made to implement incremental algorithmic improvements that steadily enhance the accuracy and reliability of language models. To effectively manage AI-specific risks in software development and application, it is essential first to have the ability to manage IT risks more broadly. Many of the current challenges associated with AI may evolve over time as the technologies continue to advance. However, the example and methodology presented in this article offer a systematic approach that can help tailor risk management strategies to real-world business needs [35; 36]. Each company has its own level of maturity in working with AI, and understanding this factor is becoming a critical component of a successful AI adoption strategy. To better position AVI within the existing landscape of LLM safety and governance solutions, Table 2 provides a conceptual comparison with several prominent tools and approaches. This comparison focuses on key architectural and functional characteristics, such as LLM-agnosticism, modularity, the types of filtering implemented, and the degree of policy configurability. It is important to note that this is a qualitative comparison based on publicly available documentation and the described operational principles of these systems, rather than a direct numerical benchmarking of performance, which is an avenue for future research (see Section 4.6).