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市場調查報告書
商品編碼
2148850
人工智慧模型風險管理市場規模、佔有率、成長及全球產業分析、區域趨勢及2026-2034年預測AI Model Risk Management Market Size, Share, Growth and Global Industry Analysis, Regional Insights and Forecast to 2026-2034 |
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2025年全球人工智慧模型風險管理市場規模為20.8億美元,預計2026年將達25.2億美元,2034年將達到127億美元,預測期內複合年成長率(CAGR)為22.4%。北美市場佔據主導地位,2025年市場佔有率達到45.19%,這主要得益於企業對人工智慧的廣泛應用、強大的技術基礎設施、金融業的需求以及主要人工智慧管治提供者的存在。
人工智慧模型風險管理涵蓋了用於識別、評估、檢驗、監控、記錄和管理人工智慧模型整個生命週期中相關風險的流程和技術。隨著人工智慧與業務營運的整合日益加深,這些解決方案能夠幫助企業提升模型的準確性、透明度、可解釋性、安全性、合規性和課責。
生成式人工智慧的影響
生成式人工智慧 (Generation AI) 的出現顯著提升了對人工智慧模型風險管理的需求。這是因為大規模語言模型和人工智慧代理會帶來額外的風險,例如輸出不準確、存在偏見、涉及隱私、安全、智慧財產權以及模型行為不可控等問題。因此,各組織正在實施模型清單、部署前測試、紅隊演練、持續監控和自動化合規性報告等措施。 IBM 計劃於 2024 年 5 月將 watsonx.governance 與 Amazon SageMaker 整合,以擴展 watsonx 在 AWS 上的功能,並協助人工智慧和機器學習風險管理。
人工智慧模型風險管理市場趨勢
人工智慧管治標準的日益普及正在推動市場成長。
越來越多的組織正在將其人工智慧管治專案與 ISO/IEC 42001 和 NIST 人工智慧風險管理框架等標準接軌。這些標準提倡系統化的風險評估、課責、透明度、文件記錄、監控和控制流程。根據 BSI 2026 年 3 月發布的一份報告,26% 的組織正在採取措施以符合 ISO/IEC 42001 標準,而 62% 的高階主管表示,他們預計在未來 12 個月內增加對人工智慧的投資。
市場動態
市場促進因素
企業模式組合的快速擴張正在推動市場成長。
企業正擴大在多個部門部署機器學習模型、生成式人工智慧應用、基礎模型和自主人工智慧代理。隨著人工智慧系統在雲端、本地和第三方環境中運作,模型所有權、風險分類、核准情況、文件和監管證據的管理變得日益複雜。 2025 年 5 月,ServiceNow 發布了 AI Control Tower,旨在協助企業管治和維護其人工智慧代理、模型和工作流程。
市場限制因素
高昂的實施和整合成本可能會阻礙其廣泛應用。
人工智慧模型風險管理平台需要對軟體、客製化、諮詢、資料準備、員工培訓以及與現有人工智慧開發環境的整合進行投資。對於小規模的組織,尤其是那些人工智慧產品組合有限的組織而言,證明這些投資的合理性可能頗具挑戰性。缺乏標準化的風險指標以及監控生成式和基於代理的人工智慧系統的複雜性,將進一步加劇採用該平台所面臨的挑戰。
市場機遇
向高風險行業擴張正在創造新的成長機會。
金融服務、醫療保健、政府、關鍵基礎設施和就業相關應用領域蘊藏著巨大的機遇,因為人工智慧在這些領域的失敗可能會對財務、營運、安全或個人權利造成重大影響。監管要求日益強調風險評估、資料品質、可追溯性、網路安全、人工監督和文件記錄。 2026年1月,ServiceNow和Anthropic宣佈建立合作夥伴關係,將Claude模型整合到ServiceNow的管治人工智慧平台中,用於關鍵任務工作流程。
市場挑戰
監測生成式和基於代理的人工智慧的複雜性是一個挑戰,阻礙了市場成長。
生成式和基於代理的人工智慧的非確定性行為為傳統的模型檢驗方法帶來了挑戰。持續評估至關重要,因為人工智慧的輸出會因提示、上下文、數據和系統互動而變化。因此,在日益複雜的人工智慧環境中,組織需要建立更強大的監控、紅隊演練、可解釋性、事件管理和人工監督能力,同時確保合規性。
The global AI model risk management market size was valued at USD 2.08 billion in 2025 and is projected to reach USD 2.52 billion in 2026 and USD 12.70 billion by 2034, registering a CAGR of 22.4% during the forecast period. North America dominated the market in 2025 with a 45.19% market share, supported by extensive enterprise AI adoption, strong technology infrastructure, financial-sector demand, and the presence of leading AI governance providers.
AI model risk management includes processes and technologies used to identify, assess, validate, monitor, document, and control risks associated with AI models throughout their lifecycle. These solutions help organizations improve model accuracy, transparency, explainability, security, regulatory compliance, and accountability as AI becomes increasingly embedded in business operations.
Impact of Generative AI
Generative AI is significantly increasing demand for AI model risk management because large language models and AI agents introduce additional risks related to inaccurate outputs, bias, privacy, security, intellectual property, and uncontrolled model behavior. Organizations are therefore adopting model inventories, pre-deployment testing, red teaming, continuous monitoring, and automated compliance reporting. In May 2024, IBM expanded watsonx capabilities on AWS and planned integration between watsonx.governance and Amazon SageMaker to support AI and machine-learning risk management.
AI Model Risk Management Market Trends
Increasing Adoption of AI Governance Standards Fuels Market Growth
Organizations are increasingly aligning AI governance programs with standards such as ISO/IEC 42001 and the NIST AI Risk Management Framework. These standards encourage structured risk assessment, accountability, transparency, documentation, monitoring, and control processes. In March 2026, BSI reported that 26% of organizations were taking steps toward ISO/IEC 42001 alignment, while 62% of business leaders expected to increase AI investments over the following 12 months.
Market Dynamics
Market Drivers
Rapid Growth in Enterprise Model Portfolios Drives Market Expansion
Companies are deploying growing numbers of machine-learning models, generative AI applications, foundation models, and autonomous AI agents across multiple departments. Managing model ownership, risk classification, approval status, documentation, and regulatory evidence becomes increasingly complex as AI systems operate across cloud, on-premises, and third-party environments. In May 2025, ServiceNow launched AI Control Tower to help enterprises govern and manage AI agents, models, and workflows.
Market Restraints
High Implementation and Integration Costs May Hinder Adoption
AI model risk management platforms require investment in software, customization, consulting, data preparation, employee training, and integration with existing AI development environments. Smaller organizations may face difficulty justifying these investments, particularly when their AI portfolios are still limited. Lack of standardized risk metrics and the complexity of monitoring generative and agentic AI systems can further increase implementation challenges.
Market Opportunities
Expansion Across High-Risk Industries Creates New Growth Opportunities
Financial services, healthcare, government, critical infrastructure, and employment applications are creating strong opportunities because AI failures in these sectors can produce significant financial, operational, safety, or individual-rights consequences. Regulatory requirements increasingly emphasize risk assessment, data quality, traceability, cybersecurity, human oversight, and documentation. In January 2026, ServiceNow and Anthropic announced a partnership to integrate Claude models into ServiceNow's governed AI platform for mission-critical workflows.
Market Challenges
Complexity of Monitoring Generative and Agentic AI Challenges Market Growth
The non-deterministic behavior of generative and agentic AI creates difficulties for conventional model validation approaches. AI outputs can vary based on prompts, context, data, and system interactions, making continuous evaluation essential. Organizations must therefore develop stronger monitoring, red teaming, explainability, incident management, and human-oversight capabilities while maintaining compliance across increasingly complex AI environments.
By Deployment
The market is segmented into cloud, on-premises, and hybrid. The cloud segment dominated in 2025 and is projected to grow at a 25.5% CAGR, supported by scalability, centralized governance, rapid updates, subscription-based models, and integration with AI development environments. The hybrid segment is expected to grow at a 23.3% CAGR as regulated enterprises combine cloud flexibility with on-premises control.
By Risk Management Function
The market includes AI model inventory & governance, model validation & testing, risk control & model monitoring, and regulatory compliance, audit & reporting. AI model inventory and governance held the largest share in 2025 because enterprises require centralized visibility into model ownership, documentation, risk classification, and lifecycle status. Risk control and model monitoring is projected to record the fastest growth at a 24.9% CAGR.
By Industry
The market covers BFSI, IT & telecom, retail, healthcare, manufacturing, and others. BFSI dominated in 2025 due to extensive AI use in credit assessment, fraud detection, underwriting, trading, pricing, and compliance. Healthcare is projected to grow at the fastest 25.4% CAGR, driven by AI adoption in diagnostics, medical imaging, clinical decision support, and drug discovery.
North America led the market with a USD 0.94 billion valuation in 2025 and a 45.19% share. The region benefits from major AI developers, cloud providers, financial institutions, and governance technology companies. The U.S. market is estimated at approximately USD 0.87 billion in 2026.
Europe is projected to reach USD 0.63 billion in 2026, supported by the EU AI Act, ISO/IEC 42001 adoption, and increasing AI governance requirements. The U.K. market is estimated at USD 0.15 billion, while Germany is projected at USD 0.14 billion in 2026.
Asia Pacific is expected to reach USD 0.57 billion in 2026 and record the highest regional CAGR of 25.6%. China, Japan, India, South Korea, Singapore, and Australia are expanding enterprise AI deployment and responsible AI initiatives. China is projected at USD 0.14 billion, Japan at USD 0.11 billion, and India at USD 0.07 billion in 2026.
South America is projected to reach USD 0.08 billion in 2026, supported by AI adoption in banking, telecom, retail, and public services. Brazil is expected to reach USD 0.04 billion.
Middle East & Africa is estimated to reach USD 0.10 billion in 2026, supported by government AI strategies, cloud infrastructure, banking, telecom, healthcare, and public-sector digitalization. The GCC market is projected at USD 0.03 billion.
Competitive Landscape
The AI model risk management market is semi-consolidated, with IBM Corporation, Microsoft Corporation, Alphabet, ServiceNow, and SAS Institute holding significant positions. Companies are integrating AI inventories, validation, monitoring, explainability, policy enforcement, regulatory reporting, and governance into unified platforms. Other important players include DataRobot, ModelOp, Credo AI, Holistic AI, and ValidMind.
Key Industry Developments
In August 2025, ModelOp partnered with Angularis.ai to deliver AI Governance-as-a-Service. In May 2025, DataRobot expanded its NVIDIA collaboration for enterprise AI development and governance. In May 2025, Microsoft enhanced Azure AI Foundry with observability, monitoring, automated evaluation, red teaming, and governance capabilities. In April 2025, Box and IBM expanded their partnership around AI governance and lifecycle management.
Report Coverage
The AI model risk management market report covers market size and forecasts for 2025, 2026, and 2034, along with segmentation by deployment, risk management function, and industry. The report examines market trends, drivers, restraints, opportunities, challenges, generative AI impact, regulatory developments, technology advancements, product launches, partnerships, mergers and acquisitions, competitive strategies, regional markets, and profiles of major companies.
Conclusion
The global AI model risk management market is positioned for substantial expansion, increasing from USD 2.08 billion in 2025 to USD 2.52 billion in 2026 and reaching USD 12.70 billion by 2034. Growing enterprise AI portfolios, generative AI adoption, regulatory requirements, AI governance standards, and rising exposure to model-related financial and operational risks are creating sustained demand. Cloud deployment, continuous model monitoring, healthcare AI governance, and high-risk industry applications will remain important growth areas through 2034, while implementation costs, fragmented governance responsibilities, and the complexity of generative and agentic AI remain key challenges.
Segmentation By Deployment, Risk Management Function, Industry, and Region
By Deployment
By Risk Management Function
By Industry
By Region