--- title: "How to Manage Artificial Intelligence Risks: Best Practices and Strategies for Safe Adoption" description: Managing artificial intelligence risks requires a structured framework. Here’s how, step by step image: https://newsroom.kireygroup.com/hubfs/Data%26AI_12.jpg --- [kireygroup.com](https://www.kireygroup.com/) [![](https://newsroom.kireygroup.com/hubfs/item-2020-dicembre/logo-Blog-v2.svg)](https://newsroom.kireygroup.com/en/news) - [Ita](https://newsroom.kireygroup.com/news/come-gestire-i-rischi-dell-intelligenza-artificiale-best-practice-e-strategie-per-un-adozione-sicura?hsLang=it) - Eng - [Esp](https://newsroom.kireygroup.com/es/news?hsLang=en) [Kireygroup.com](https://www.kireygroup.com/) # Get your daily dose of tech! ## We Shape Your Knowledge # [Legacy modernization in banking: discover the true cost of legacy systems](https://newsroom.kireygroup.com/en/news/legacy-modernization-in-banking-discover-the-true-cost-of-legacy-systems?hsLang=en) The banking sector is one of the world’s largest investors in digital transformation and innovation,... [Read more](https://newsroom.kireygroup.com/en/news/legacy-modernization-in-banking-discover-the-true-cost-of-legacy-systems?hsLang=en) 10 MIN READ 22 September 2026 ### Latest News - [Legacy modernization in banking: discover the true cost of legacy systems](https://newsroom.kireygroup.com/en/news/legacy-modernization-in-banking-discover-the-true-cost-of-legacy-systems?hsLang=en) - [Context Engineering, the art and science behind AI Agents](https://newsroom.kireygroup.com/en/news/context-engineering-the-art-and-science-behind-ai-agents?hsLang=en) - [Agentic AI Governance: how to effectively manage AI that can act](https://newsroom.kireygroup.com/en/news/agentic-ai-governance-how-to-effectively-manage-ai-that-can-act?hsLang=en) - [Autonomous SOC: reality, limitations, and prospects for AI-driven security](https://newsroom.kireygroup.com/en/news/autonomous-soc-reality-limitations-and-prospects-for-ai-driven-security?hsLang=en) - [Data Virtualization, the pillar of data-driven companies: what it is and how it works](https://newsroom.kireygroup.com/en/news/data-virtualization-the-pillar-of-data-driven-companies-what-it-is-and-how-it-works?hsLang=en) - [Cloud Economics: the goal is not to spend less, but to spend better](https://newsroom.kireygroup.com/en/news/cloud-economics-the-goal-is-not-to-spend-less-but-to-spend-better?hsLang=en) - [Artificial Intelligence](https://newsroom.kireygroup.com/en/news/tag/artificial-intelligence) / # How to Manage Artificial Intelligence Risks: Best Practices and Strategies for Safe Adoption #### Kirey [![](https://newsroom.kireygroup.com/hubfs/it.svg)](https://newsroom.kireygroup.com/news/come-gestire-i-rischi-dell-intelligenza-artificiale-best-practice-e-strategie-per-un-adozione-sicura?hsLang=it)    [![](https://newsroom.kireygroup.com/hubfs/ES.svg)](https://newsroom.kireygroup.com/?hsLang=es) 13 MIN READ 21 November 2025 - Share on: - - - - [All](https://newsroom.kireygroup.com/news?hsLang=en) - Categories + - [Corporate](https://newsroom.kireygroup.com/en/news/tag/corporate) - [Cybersecurity](https://newsroom.kireygroup.com/en/news/tag/cybersecurity) - [Events/Webinars](https://newsroom.kireygroup.com/en/news/tag/events-webinars) - [Cloud](https://newsroom.kireygroup.com/en/news/tag/cloud) - [Data Value](https://newsroom.kireygroup.com/en/news/tag/data-value) - [Innovation](https://newsroom.kireygroup.com/en/news/tag/innovation) - [Artificial Intelligence](https://newsroom.kireygroup.com/en/news/tag/artificial-intelligence) - [Monitoring](https://newsroom.kireygroup.com/en/news/tag/monitoring) - [DevSecOps](https://newsroom.kireygroup.com/en/news/tag/devsecops) - [Software Development](https://newsroom.kireygroup.com/en/news/tag/software-development) - [Corporate Social Responsibility](https://newsroom.kireygroup.com/en/news/tag/corporate-social-responsibility) - [Data Privacy](https://newsroom.kireygroup.com/en/news/tag/data-privacy) - [Advisory](https://newsroom.kireygroup.com/en/news/tag/advisory) - [Accessibility](https://newsroom.kireygroup.com/en/news/tag/accessibility) - [Automation](https://newsroom.kireygroup.com/en/news/tag/automation) - [Digital Banking](https://newsroom.kireygroup.com/en/news/tag/digital-banking) - [Digital Industry](https://newsroom.kireygroup.com/en/news/tag/digital-industry) - [Use case](https://newsroom.kireygroup.com/en/news/tag/use-case) - [Welfare & Social Services](https://newsroom.kireygroup.com/en/news/tag/welfare-social-services) - [Press & PR](https://newsroom.kireygroup.com/en/news/tag/press) Artificial intelligence, particularly in its generative form, **has rapidly entered businesses.** The accessibility of tools such as Large Language Models (LLMs), their ease of use, and the promise of immediate productivity and efficiency gains have sparked **a real rush toward adoption.** However, when AI is introduced without a clear strategy, shared guidelines, or a structured risk assessment, the danger is **losing control over innovation.**  This is where AI governance comes into play, specifically in the need for **a structured approach to managing artificial intelligence risks.** The goal is not to slow down innovation or discourage AI use; on the contrary, a robust governance program ensures that AI is employed **effectively, safely, and aligned with the company’s principles, values, and objectives.**  ## The Risks of Artificial Intelligence: From Data Loss to Privacy Violations   Without clear governance, AI adoption can become a risk accelerator rather than an enabler of innovation and business growth.  ### Data Breaches The first potential consequence of unstructured AI adoption is the loss of sensitive data. Even a simple interaction with a generative AI tool, such as entering text containing confidential client information or contract details, can expose data that the organization is obliged to protect. Once outside the secure perimeter, the data may be stored or processed by external models and is no longer under the organization’s control.    ### Shadow AI  Shadow AI refers to the unauthorized use of AI tools by individual users or teams outside the IT or compliance perimeter. The consequences are similar to those described above: data exposure, lack of control, and potential security issues.  ### Lack of Transparency and Algorithmic Bias   Generative large models operate as black boxes, generating responses, suggestions, or decisions without revealing the criteria behind them. The risk is that organizations may rely on systems whose reasoning is unexplainable and potentially influenced by biases present in the training data. ### Compliance Risks  AI use must comply with existing regulations such as GDPR and the [European AI Act](https://newsroom.kireygroup.com/en/news/ai-regulation-worldwide-and-its-impact-on-europe-s-innovation-capacity?hsLang=en), which introduces new rules regarding transparency, risk classification, human oversight, and traceability. Additional obligations on data sovereignty are becoming increasingly strict, especially in regulated sectors and for strategic or sensitive data.  ## AI Governance Program: the Starting Point    In structured organizations, the first step for effective risk management is to clarify [**what AI represents for the company**](https://www.kireygroup.com/en/products/artificial-intelligence), the vision for its use, and the values guiding its deployment in systems that can make decisions and generate content autonomously.  At this stage, it is useful to ask key questions:  - What **corporate principles govern AI adoption** (e.g., transparency, human oversight, accountability, inclusivity)?  - Who is **responsible for AI governance** (at the executive, operational, technical, and legal levels)?  - Which **regulations** apply to the company (GDPR, AI Act, sector-specific regulations)?  - What **ethical boundaries** must be respected, regardless of legal requirements?  Once these fundamentals are defined, they should be formalized in an **AI strategy** outlining the company’s objectives for AI use and defining accountability structures, including potential ethical committees or dedicated teams.  A strong AI governance program also serves as a reference point for **external relationships across the AI supply chain:** partners, vendors, and technology providers. In a modular AI ecosystem—comprising third-party solutions, plugins, APIs, and LLMs—a clear framework of principles ensures they are consistently **applied throughout the value chain.**  ## The Artificial Intelligence Risk Management Framework  AI risk management is a central component of the governance program: if governance establishes the principles and rules of the game, risk management **identifies concrete risks, measures the company’s exposure, and implements control and mitigation mechanisms.**  In a business context, AI risk management acts as a prevention system, **continuously monitoring AI solutions** to detect potential issues before they become real problems. The need for a structured framework arises because **AI risks are often invisible**, interconnected, and can manifest unpredictably.  The framework can be custom-built for the organization or inspired by established models such as the **NIST AI Risk Management Framework** or **ISO/IEC 42001 guidelines.** These sources provide a solid foundation for adapting best practices to the company’s operational context.  ### Discovery and Risk Mapping  Framework development begins with a discovery phase to identify the AI systems in use and simultaneously map associated risks. This process involves multidisciplinary teams since it addresses not only technical risks related to data and algorithms but also operational, reputational, legal, and security risks.  ### Risk Classification and Scoring Next comes the evaluation and classification phase. Each risk is analyzed based on probability and impact, producing a scoring system that prioritizes resources efficiently and focuses attention on risks with potentially the most significant consequences.  ### Risk Control and Mitigation  An effective framework includes **mechanisms to control and mitigate identified risks**. This operational phase translates governance principles into practical rules governing **interactions with AI systems**, from model development and third-party solution acquisition to data usage in existing systems.  For example, internal development may require **minimum accuracy thresholds,** robustness tests, and automated checks to identify biases in training data. For external solutions, the framework might specify **vendor due diligence criteria**, contractual clauses for audits and transparency, and documentation requirements for datasets used.  ### Monitoring and Auditing  Operational monitoring is designed to detect deviations from expected performance and anomalous AI behavior in real time. Automated dashboards track critical metrics—accuracy, data quality, response time, and more—shifting supervision from a reactive task to a predictive process.  ## A Strategic Asset that Evolves Over Time  An effective AI risk management framework is not a static document or a mere compliance exercise; it is a strategic asset that the company builds upon to drive **its AI adoption.** Consequently, it must be continuously updated to reflect regulatory changes, emerging risks, and technological advancements, which evolve almost daily. Only in this way can a company ensure consistency, responsiveness, and control, even in complex and rapidly changing environments.  At Kirey, we support companies **throughout their AI adoption journey,** not only by developing [customized solutions](https://www.kireygroup.com/en/competencies/data-ai) but also by **defining governance models**, risk management frameworks, and integration strategies with business processes.  If you want to learn more, [contact us](https://www.kireygroup.com/en/contact): our specialists are ready to help you build a winning approach for AI implementation in your organization.  - [Previous post](https://newsroom.kireygroup.com/en/news/alessio-botta-joins-kireys-board?hsLang=en) - [Read all posts](https://newsroom.kireygroup.com/en/news?hsLang=en) - [Next post](https://newsroom.kireygroup.com/en/news/cloud-skill-gap-the-real-barrier-to-innovation-is-expertise?hsLang=en) ## Related posts: #### [Context Engineering, the art and science behind AI...](https://newsroom.kireygroup.com/en/news/context-engineering-the-art-and-science-behind-ai-agents?hsLang=en) With the spread of LLMs, the discipline of prompt engineering emerged, studying the most effective w... 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