자동차 사이버 보안 AI 시장 규모, 점유율 및 성장 예측(2026-2031년)

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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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