全球汽車網路安全人工智慧市場規模、佔有率和成長預測（2026-2031 年）

全球汽車網路安全人工智慧市場預計將從 2026 年的 13.5 億美元成長到 2031 年的 45 億美元，預測期內複合年成長率為 27.2%。

隨著汽車從固定電子產品向始終運作的軟體平台轉型，汽車網路安全也在改變。集中式運算、空中下載 (OTA) 更新、雲端 API、應用生態系統和連網充電基礎架構不斷產生軟體變更和遙測資料。雖然傳統的基於特徵碼的控制措施仍然必要，但它們正日益被機器學習模型所補充。這些模型能夠偵測行為異常、關聯跨多個領域的事件，甚至在已知攻擊特徵出現之前就對可疑活動進行優先排序。人工智慧 (AI) 正在產品開發和生產後安全中得到廣泛應用。在車輛開發階段，AI 可以協助進行自動化資產映射、威脅分析和風險評估、漏洞優先排序以及合規性證據的產生。車輛投入運作後，基於 AI 的車輛偵測與回應 (VDR)、擴展偵測與回應 (EDR) 和車輛安全營運中心 (VSOC) 平台會監控車隊的行為，並將車載訊號與後端、行動應用和企業資料關聯起來。這項技術也帶來了雙重安全挑戰。雖然人工智慧可以幫助防禦者更快地識別攻擊，但人工智慧驅動的車輛功能本身在模型、數據和介面方面存在風險。

市場促進因素

• 軟體定義車輛需要持續偵測，而非一次性檢驗。即使車輛已經上市，汽車網路安全的範圍仍在不斷擴大。由於軟體定義車輛頻繁更新、連接外部服務並使用集中式運算平台，多個功能可能面臨相同的漏洞風險。持續監控使製造商能夠識別車隊中的異常行為，調查新披露的漏洞，並確定受影響的軟體是否部署在特定車輛中。車隊規模的遙測資料和軟體依賴關係數量龐大，難以手動分析，因此人工智慧的價值日益凸顯。
• 法規和標準的不斷完善，使得可擴展的網路安全工作流程的需求日益成長。聯合國第155號條例強制要求在參與市場運作的製造商維護網路安全管理系統。 ISO/SAE 21434標準規定了道路車輛整個生命週期的網路安全工程要求，涵蓋概念、開發、生產、營運、維護和報廢等各個階段。隨著軟體清單和供應商依賴性的不斷擴大，人工智慧驅動的評估平台可以縮短更新威脅模型、確定漏洞優先順序以及維護網路安全事件證據所需的時間。
• 人工智慧驅動的汽車正在帶來新的安全風險。汽車製造商正在部署人工智慧助理、感知系統、預測服務和自動駕駛功能。這些功能帶來了額外的風險，包括模型篡改、不可信輸入、隱私洩露、訓練資料外洩以及人工智慧服務與車輛功能之間的連接問題。在2026年國際消費電子展（CES）上，VicOne和P3展示了針對人工智慧駕駛座的汽車級安全解決方案。除了傳統的車輛網路之外，業界也越來越重視人工智慧組件的安全保護。

市場限制因素

• 誤報、可解釋性問題和安全限制阻礙了完全自動化。汽車安全系統無法主動應對所有異常情況，因為不當干預可能會影響車輛的可用性和安全性。此外，機器學習模型必須解釋為何某個特定行為被視為可疑，並區分真正的攻擊和異常但合法的車輛行為。因此，人工監控仍然至關重要，尤其是在人工智慧建議導致軟體封鎖、車隊範圍內的糾正措施或向監管機構報告時。

目錄

第1章：執行摘要

第2章：市場概述

• 市場概覽
• 市場區隔

第3章：商業趨勢

• 市場促進因素
  • 軟體定義車輛需要持續檢測，而不僅僅是一次性檢驗。
  • 法規和標準的不斷完善，使得對擴充性的網路安全工作流程的需求日益成長。
  • 配備人工智慧的車輛會帶來新的安全風險。
• 市場限制因素
  • 誤報、可解釋性問題和安全限制阻礙了完全自動化。
• 市場機遇
• 波特五力分析
• 產業價值鏈分析
• 法規和標準的發展趨勢

第4章 技術展望

• 利用邊緣人工智慧進行入侵偵測
• 人工智慧驅動的車輛偵測與回應
• 威脅分析和風險評估的自動化
• 智慧駕駛座和 GenAI 的 AI 安全

第5章：全球汽車網路安全人工智慧市場：按人工智慧功能分類

• 異常偵測和入侵偵測
• 車輛偵測與回應
• TARA 和合規自動化
• 確定漏洞優先權
• 預測性威脅情報
• 人工智慧模型和生成式人工智慧的保護

第6章：全球汽車網路安全人工智慧市場：按安全領域分類

• 車內安全
• 雲端和後端安全
• 車輛安全營運中心
• 與供應鏈和軟體漏洞相關的安全性問題
• 應用程式和 API 安全

第7章：全球汽車網路安全人工智慧市場：按車輛架構分類

• 聯網汽車
• 軟體定義車輛（SDV）
• 自動駕駛汽車和高度自動化汽車

第8章：全球汽車網路安全人工智慧市場：按最終用戶分類

• 汽車原廠設備製造商
• 一級和二級供應商
• 車隊和旅遊營運商
• 工程和保全服務供應商

第9章：全球汽車網路安全人工智慧市場：依部署方式分類

• 雲
• 車載/Edge
• 混合

第10章：全球汽車網路安全人工智慧市場：按地區分類

• 北美洲
  • 美國
  • 加拿大
  • 墨西哥
• 南美洲
  • 巴西
  • 阿根廷
  • 其他南美國家
• 歐洲
  • 德國
  • 英國
  • 法國
  • 義大利
  • 其他歐洲國家
• 中東和非洲
  • 沙烏地阿拉伯
  • 阿拉伯聯合大公國
  • 南非
  • 其他中東和非洲國家
• 亞太地區
  • 中國
  • 日本
  • 印度
  • 韓國
  • 其他亞太國家

第11章：競爭環境與分析

• 主要公司及策略分析
• 市佔率分析
• 產品開發、夥伴關係和平台整合
• 競爭環境儀錶板

第12章：公司簡介

• Upstream Security Ltd.
• VicOne Corporation
• PlaxidityX
• Argus Cyber Security Ltd.
• AUTOCRYPT Co., Ltd.
• C2A Security Ltd.
• Karamba Security Ltd.
• ETAS GmbH
• Vector Informatik GmbH
• Keysight Technologies, Inc.
• L&T Technology Services Limited
• VxLabs
• Microsoft Corporation
• Intellias
• HARMAN International
• Cybellum Technologies Ltd.
• Synopsys, Inc.
• Trend Micro Incorporated

第13章 近期趨勢

第14章附錄

The Global AI in Automotive Cybersecurity Market is anticipated to grow from USD 1.35 billion in 2026 to USD 4.50 billion by 2031, at a CAGR of 27.2% over the forecast period.

Automotive cybersecurity is changing as vehicles become persistent software platforms rather than fixed electronic products. Centralized compute, over-the-air updates, cloud APIs, app ecosystems and connected charging infrastructure create a continuous stream of software changes and telemetry. Traditional signature-based controls remain necessary, but they are increasingly supplemented by machine learning models that detect behavioral anomalies, correlate events across multiple domains and prioritize suspicious activity before a known attack signature exists. Artificial intelligence is being applied across both product development and post-production security. During vehicle development, AI can support automated asset mapping, Threat Analysis and Risk Assessment, vulnerability triage and compliance evidence generation. After vehicles enter service, AI-based Vehicle Detection and Response, Extended Detection and Response and Vehicle Security Operations Center platforms monitor fleet behavior and correlate in-vehicle signals with backend, mobile application and enterprise data. The technology also creates a dual security challenge. AI helps defenders identify attacks more quickly, but AI-enabled vehicle functions introduce model, data and interface risks of their own.

Market Drivers

• Software-defined vehicles require continuous detection rather than one-time validation. Automotive cybersecurity increasingly extends beyond vehicle launch. Software-defined vehicles receive frequent updates, connect to external services and use centralized compute platforms that can expose multiple functions to the same vulnerability. Continuous monitoring allows manufacturers to identify abnormal fleet behavior, investigate newly disclosed vulnerabilities and determine whether affected software is deployed across specific vehicles. AI becomes valuable because the volume of telemetry and software dependencies is difficult to analyze manually at fleet scale.
• Regulation and standards increase the need for scalable cybersecurity workflows. UN Regulation No. 155 requires manufacturers operating in participating markets to maintain a Cybersecurity Management System. ISO/SAE 21434 provides lifecycle cybersecurity engineering requirements for road vehicles. The standard covers concept, development, production, operation, maintenance and decommissioning. As software inventories and supplier dependencies expand, AI-assisted assessment platforms can reduce the time needed to update threat models, prioritize vulnerabilities and maintain evidence for cybersecurity cases.
• AI-enabled vehicles create new security risks. Vehicle manufacturers are introducing AI-powered assistants, perception systems, predictive services and autonomous functions. These capabilities create additional risks around model manipulation, untrusted inputs, privacy, training data and connections between AI services and vehicle functions. VicOne and P3 demonstrated automotive-grade security for AI-driven cockpits at CES 2026. Industry work increasingly focuses on securing AI components alongside conventional vehicle networks.

Market Restraints

• False positives, explainability and safety constraints limit full automation. Automotive security systems cannot respond aggressively to every anomaly because inappropriate intervention can affect vehicle availability or safety. Machine learning models also need to explain why activity is considered suspicious and distinguish genuine attacks from unusual but legitimate vehicle behavior. Human oversight therefore remains important. This is particularly true when AI recommendations lead to software blocking, fleet-wide remediation or regulatory reporting.

Technology and Segment Insights

• Edge AI intrusion detection places machine learning close to the electronic control unit or vehicle network so suspicious behavior can be identified with minimal latency. VicOne and Trustonic launched a layered ECU-level solution in April 2026 combining xCarbon intrusion detection and prevention, a trusted execution environment and edge AI. The approach allows vehicle signals to be correlated locally while preserving a protected environment for security operations.
• AI-enabled Vehicle Detection and Response platforms combine in-vehicle telemetry with cloud, backend and external threat intelligence. PlaxidityX vCore and Upstream Security's mobility-focused XDR platform use analytics to provide fleet-wide visibility and prioritize threats. The technology is becoming important as cyber incidents increasingly span vehicles, cloud systems, mobile services and enterprise infrastructure rather than remaining inside one ECU.
• Automated Threat Analysis and Risk Assessment is another key application. TARA is traditionally expert-intensive because teams must identify assets, attack paths, controls and residual risk across complex electronic architectures. AI-assisted tools can automate portions of this work while keeping cybersecurity engineers in the review loop. L&T Technology Services' CySAF uses knowledge-grounded AI agents for asset mapping, threat analysis, control mapping and remediation generation. VicOne and Saphira are linking live supplier vulnerabilities to TARA reassessment.
• AI security for intelligent cockpits and generative AI is an emerging requirement. In-vehicle generative AI introduces interfaces that can receive open-ended user input and interact with cloud services, vehicle data or applications. Security therefore needs to address model misuse, data leakage, malicious prompts and unsafe tool access. Automotive cybersecurity vendors are beginning to combine conventional runtime protection with AI-model and cockpit security as large language model-based assistants move into production vehicles.
• The market is segmented by AI function, security domain, vehicle architecture, end user, deployment and geography. AI functions include anomaly and intrusion detection, vehicle detection and response, TARA and compliance automation, vulnerability prioritization, predictive threat intelligence, and AI model and GenAI protection. Security domains include in-vehicle security, cloud and backend security, vehicle security operations center, supply-chain and software vulnerability security, and application and API security. Vehicle architectures include connected vehicles, software-defined vehicles, and autonomous and highly automated vehicles. End users include automotive OEMs, Tier 1 and Tier 2 suppliers, fleet and mobility operators, and engineering and cybersecurity service providers. Deployments include cloud, in-vehicle and edge, and hybrid.
• Threat detection and anomaly analytics are among the most mature AI applications because connected vehicles generate large volumes of network and behavioral data. Vulnerability prioritization and TARA automation are growing rapidly as software-defined vehicle development increases the number of components and dependencies that security teams must assess. AI-model security remains earlier in commercialization but is becoming more important as generative AI enters the cockpit.
• In-vehicle security uses AI for Controller Area Network, Ethernet, electronic control unit and application monitoring. Cloud and backend security focuses on APIs, connected services and fleet platforms. Vehicle Security Operations Center solutions correlate information across both domains. Supply-chain security is also gaining importance because vulnerabilities in third-party libraries and supplier software can affect large numbers of vehicles simultaneously.
• Software-defined vehicles create the strongest use case because centralized compute and frequent software updates increase both data availability and cyber exposure. Connected conventional vehicles also use AI-based monitoring, particularly through cloud platforms. Autonomous and highly automated vehicles add further requirements because cybersecurity events can affect sensor processing, decision systems and operational availability.
• Vehicle manufacturers are the principal buyers of fleet-scale detection, Cybersecurity Management System and Vehicle Security Operations Center platforms. Tier 1 suppliers use AI-assisted cybersecurity engineering and component-level protection to meet OEM requirements. Fleet operators and mobility providers increasingly need monitoring for connected vehicle services. Engineering-service companies use AI to accelerate security assessment and compliance work.
• Cloud-based AI is well suited to fleet correlation, threat intelligence and vulnerability analysis. Edge AI supports low-latency in-vehicle detection. Hybrid architectures are likely to remain important because automotive cybersecurity requires both immediate local protection and fleet-wide context. Data sovereignty and safety requirements can also influence where models are deployed.

Competitive and Strategic Outlook

• The market includes specialist automotive cybersecurity vendors, engineering-tool providers, enterprise security companies and automotive software suppliers. Upstream Security, VicOne and PlaxidityX compete in AI-powered fleet monitoring, threat detection and response. Argus Cyber Security, ETAS, AUTOCRYPT, Karamba Security and C2A Security address different combinations of in-vehicle protection, security operations and cybersecurity lifecycle management. L&T Technology Services and VxLabs are using AI to automate assessment and compliance workflows.
• Competition is increasingly based on automotive context rather than generic machine-learning capability. Effective platforms must understand vehicle architecture, software bills of materials, communication protocols, fleet configurations, supplier dependencies and regulatory evidence. Vendors with access to large-scale vehicle telemetry and vulnerability datasets can improve prioritization and anomaly detection. Engineering-focused platforms differentiate through integration with development tools and TARA processes.
• Europe is a major market because cybersecurity requirements are closely tied to vehicle type approval and software-defined vehicle programs. UN Regulation No. 155 establishes requirements around cybersecurity and Cybersecurity Management Systems. ISO/SAE 21434 provides a widely used engineering framework across vehicle lifecycles. European OEMs and suppliers therefore need security processes that can scale across multiple platforms, suppliers and post-production software updates.
• The region also contains a dense ecosystem of automotive cybersecurity specialists and engineering suppliers. Argus Cyber Security, ETAS, Vector Informatik and other European or Europe-focused companies support in-vehicle protection, cybersecurity engineering and operational monitoring. In January 2026, Skoda partnered with Upstream Security to consolidate cyber threat intelligence and risk information across its connected-vehicle ecosystem. This reflects a shift toward unified, data-driven cybersecurity operations.
• AI adoption is expected to be strongest in Vehicle Security Operations Center analytics, vulnerability management, cybersecurity assessment and software-defined vehicle runtime protection. The increasing use of generative AI inside infotainment and cockpit systems will create an additional market for model-aware security controls. Regulatory evidence requirements will continue to support AI-assisted engineering and compliance platforms.
• Recent developments confirm accelerating market activity. In June 2026, PlaxidityX announced that its AI-powered vCore Vehicle Cyber Protection system had received the 2026 AutoTech and Wards Cybersecurity Excellence Award. In May 2026, VicOne and Saphira announced an integration linking supplier vulnerability detection with live Threat Analysis and Risk Assessment updates for automotive OEMs and Tier 1 suppliers. In April 2026, VicOne and Intellias announced a partnership integrating advanced intrusion detection and AI protection capabilities into Intellias' software-defined vehicle technology platform. In April 2026, Stellantis and Microsoft announced a five-year collaboration that includes AI-driven analytics for Stellantis' global cyber defense center covering vehicles, customers and operations. In April 2026, VicOne and Trustonic launched an ECU-level layered cybersecurity solution combining intrusion detection and prevention, a trusted execution environment and edge AI. In January 2026, Skoda selected Upstream Security to centralize cyber threat intelligence and risk information across its connected-vehicle ecosystem. In January 2026, VicOne and P3 digital services announced a CES 2026 demonstration of automotive-grade AI security for AI-driven intelligent cockpits.

Conclusion

• The global AI in automotive cybersecurity market is growing rapidly as vehicles become software-defined and increasingly AI-enabled. Growth is driven by continuous detection requirements, regulation and standards, and new risks introduced by AI-powered vehicle functions. Europe leads regulatory-driven adoption, supported by UN Regulation No. 155 and ISO/SAE 21434. North America and Asia Pacific are also expanding as connected and software-defined vehicle penetration rises. False positives, explainability and safety constraints remain the key restraints. Edge AI, vehicle detection and response, automated TARA and GenAI security are shaping the technology direction. The market is projected to grow from USD 1.35 billion in 2026 to USD 4.50 billion by 2031 at a CAGR of 27.2%.

Key Benefits of this Report

• Insightful Analysis: Detailed market insights across regions, customer segments, policies, socio-economic factors, consumer preferences, and industry verticals.
• Competitive Landscape: Understand strategic moves by key players to identify optimal market entry approaches.
• Market Drivers and Future Trends: Assess major growth forces and emerging developments shaping the market.
• Actionable Recommendations: Support strategic decisions to unlock new revenue streams.
• Caters to a Wide Audience: Suitable for startups, research institutions, consultants, SMEs, and large enterprises.

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• Industry and market insights, opportunity assessment, product demand forecasting, market entry strategy, geographical expansion, capital investment decisions, regulatory analysis, new product development, and competitive intelligence.

Report Coverage

• Historical data from 2021 to 2024, Base year 2025, and Forecast years from 2026 to 2031
• Growth opportunities, challenges, supply chain outlook, regulatory framework, and trend analysis
• Competitive positioning, strategies, and market share evaluation, and trade analysis
• Revenue growth and forecast assessment across segments and regions
• Company profiling including strategies, products, financials, and key developments

TABLE OF CONTENTS

1. EXECUTIVE SUMMARY

2. MARKET SNAPSHOT

• 2.1. Market Overview
• 2.2. Market Segmentation

3. BUSINESS LANDSCAPE

• 3.1. Market Drivers
  • 3.1.1. Software-Defined Vehicles Require Continuous Detection Rather Than One-Time Validation
  • 3.1.2. Regulation and Standards Increase the Need for Scalable Cybersecurity Workflows
  • 3.1.3. AI-Enabled Vehicles Create New Security Risks
• 3.2. Market Restraints
  • 3.2.1. False Positives, Explainability and Safety Constraints Limit Full Automation
• 3.3. Market Opportunities
• 3.4. Porter's Five Forces Analysis
• 3.5. Industry Value Chain Analysis
• 3.6. Regulatory and Standards Landscape

4. TECHNOLOGICAL OUTLOOK

• 4.1. Edge AI Intrusion Detection
• 4.2. AI-Enabled Vehicle Detection and Response
• 4.3. Automated Threat Analysis and Risk Assessment
• 4.4. AI Security for Intelligent Cockpits and GenAI

5. GLOBAL AI IN AUTOMOTIVE CYBERSECURITY MARKET BY AI FUNCTION

• 5.1. Anomaly and Intrusion Detection
• 5.2. Vehicle Detection and Response
• 5.3. TARA and Compliance Automation
• 5.4. Vulnerability Prioritization
• 5.5. Predictive Threat Intelligence
• 5.6. AI Model and GenAI Protection

6. GLOBAL AI IN AUTOMOTIVE CYBERSECURITY MARKET BY SECURITY DOMAIN

• 6.1. In-Vehicle Security
• 6.2. Cloud and Backend Security
• 6.3. Vehicle Security Operations Center
• 6.4. Supply-Chain and Software Vulnerability Security
• 6.5. Application and API Security

7. GLOBAL AI IN AUTOMOTIVE CYBERSECURITY MARKET BY VEHICLE ARCHITECTURE

• 7.1. Connected Vehicles
• 7.2. Software-Defined Vehicles
• 7.3. Autonomous and Highly Automated Vehicles

8. GLOBAL AI IN AUTOMOTIVE CYBERSECURITY MARKET BY END USER

• 8.1. Automotive OEMs
• 8.2. Tier 1 and Tier 2 Suppliers
• 8.3. Fleet and Mobility Operators
• 8.4. Engineering and Cybersecurity Service Providers

9. GLOBAL AI IN AUTOMOTIVE CYBERSECURITY MARKET BY DEPLOYMENT

• 9.1. Cloud
• 9.2. In-Vehicle / Edge
• 9.3. Hybrid

10. GLOBAL AI IN AUTOMOTIVE CYBERSECURITY MARKET BY GEOGRAPHY

• 10.1. North America
  • 10.1.1. United States
  • 10.1.2. Canada
  • 10.1.3. Mexico
• 10.2. South America
  • 10.2.1. Brazil
  • 10.2.2. Argentina
  • 10.2.3. Rest of South America
• 10.3. Europe
  • 10.3.1. Germany
  • 10.3.2. United Kingdom
  • 10.3.3. France
  • 10.3.4. Italy
  • 10.3.5. Rest of Europe
• 10.4. Middle East and Africa
  • 10.4.1. Saudi Arabia
  • 10.4.2. United Arab Emirates
  • 10.4.3. South Africa
  • 10.4.4. Rest of Middle East and Africa
• 10.5. Asia Pacific
  • 10.5.1. China
  • 10.5.2. Japan
  • 10.5.3. India
  • 10.5.4. South Korea
  • 10.5.5. Rest of Asia Pacific

11. COMPETITIVE ENVIRONMENT AND ANALYSIS

• 11.1. Major Players and Strategy Analysis
• 11.2. Market Share Analysis
• 11.3. Product Development, Partnerships and Platform Integration
• 11.4. Competitive Dashboard

12. COMPANY PROFILES

• 12.1. Upstream Security Ltd.
• 12.2. VicOne Corporation
• 12.3. PlaxidityX
• 12.4. Argus Cyber Security Ltd.
• 12.5. AUTOCRYPT Co., Ltd.
• 12.6. C2A Security Ltd.
• 12.7. Karamba Security Ltd.
• 12.8. ETAS GmbH
• 12.9. Vector Informatik GmbH
• 12.10. Keysight Technologies, Inc.
• 12.11. L&T Technology Services Limited
• 12.12. VxLabs
• 12.13. Microsoft Corporation
• 12.14. Intellias
• 12.15. HARMAN International
• 12.16. Cybellum Technologies Ltd.
• 12.17. Synopsys, Inc.
• 12.18. Trend Micro Incorporated

13. RECENT DEVELOPMENTS

14. APPENDIX

• 14.1. Currency
• 14.2. Assumptions
• 14.3. Base and Forecast Years Timeline
• 14.4. Abbreviations