AI Model Security Market Size, Share, and Industry Analysis - Global Forecast to 2036 | Market to Reach USD 17.35 Billion as Agentic AI and Regulation Drive 26.2% CAGR
Dublin, Nov. 05, 2026 (GLOBE NEWSWIRE) -- "AI Model Security Market Size, Share, and Industry Analysis - Global Forecast to 2036" has been added to ResearchAndMarkets.com's offering.
The global AI model security market was valued at USD 1.10 billion in 2025 and is estimated to reach USD 1.68 billion in 2026. By 2036, the market is projected to grow to USD 17.35 billion, representing a CAGR of 26.2% during the forecast period.
The report evaluates security solutions and services across the AI lifecycle, including model scanning, supply chain security, runtime protection, data security, agent controls, detection and response, red teaming, consulting, and managed services. It excludes AI embedded within security tools, general cybersecurity, deepfake detection, AI governance software, and MLOps platforms.
Enterprise adoption of generative AI applications and AI agents is expanding the attack surface and accelerating demand for specialized AI security capabilities. IBM reported in July 2025 that 13% of organizations had experienced breaches involving AI models or applications. Prompt injection also ranks as the most critical AI risk in the OWASP Top 10 for LLM Applications. These developments, combined with regulatory requirements and increased acquisition activity, are strengthening the global AI model security market.
Market Dynamics
Rapid deployment of generative AI and agentic systems is the primary market growth driver. Organizations increasingly require controls that address AI-specific threats, secure models before deployment, monitor production activity, and generate evidence for regulators and auditors. Emerging frameworks and standards from OWASP and NIST are also influencing purchasing criteria and enterprise security strategies.
Market expansion remains constrained by immature budgets, unclear organizational ownership, runtime performance trade-offs, and shortages of AI security expertise. Protection at runtime can add latency and cost to AI interactions, while evolving attack techniques make control effectiveness difficult to measure.
Significant opportunities are emerging in AI agent security, model supply chain protection, continuous automated red teaming, and integration with established cybersecurity platforms. The market is also shifting from model-centric security toward agent-centric protection as AI systems gain greater access to enterprise data, applications, and tools.
Segment Analysis
The report analyzes the market by offering, threat type, AI type, deployment mode, organization size, end user, and geography.
Regional Market Insights
The geographic analysis covers North America, Europe, Asia-Pacific, the Middle East & Africa, and Latin America across 26 countries and sub-regions. The assessment considers enterprise AI adoption, regulations, national AI programs, vendor presence, and customer location.
North America is expected to lead the global AI model security market in 2026, with the U.S. remaining the world's largest country market due to its concentration of AI developers, cybersecurity providers, and enterprise technology buyers. Canada is projected to grow faster than the U.S.
Europe is expected to remain the second-largest regional market, supported by compliance requirements associated with the EU AI Act. The U.K. is the region's largest country market, while Spain is forecast to grow the fastest.
Asia-Pacific is projected to achieve the highest regional growth rate through 2036. China is the region's largest market, while India is expected to become the fastest-growing country market worldwide. In the Middle East & Africa, Israel leads the market and Saudi Arabia is forecast to grow the fastest. Brazil is the largest Latin American market, while Mexico is expected to record the region's strongest growth.
Competitive Landscape
The AI model security market includes cybersecurity platform vendors, application and network security providers, cloud companies, and specialized AI security firms. Based on 2025 market share, Palo Alto Networks ranked first, followed by Microsoft, Cisco, CrowdStrike, Check Point, and Amazon Web Services.
Acquisitions remain the leading competitive strategy, followed by funding rounds and product launches. Recent activity includes acquisitions involving Protect AI, CalypsoAI, Lakera, Prompt Security, and Aim Security, along with funding rounds by Noma Security and Cyera. Vendors compete through detection accuracy, low-latency protection, AI threat research, agent and supply chain coverage, platform integration, deployment flexibility, and regulatory reporting capabilities.
Companies profiled include Palo Alto Networks, Cisco Systems, Microsoft, CrowdStrike, Check Point Software Technologies, SentinelOne, F5, Cato Networks, Zscaler, Cloudflare, Google, IBM, Amazon Web Services, HiddenLayer, Noma Security, Mindgard, Zenity, Lasso Security, Cyera, and Snyk.
Business Value of the Report
The findings are designed to assist cybersecurity vendors, AI security specialists, cloud providers, managed service providers, enterprise security teams, government organizations, and investors in evaluating market opportunities and preparing for the continued expansion of AI applications and autonomous agents.
Key Topics Covered:
1. Introduction
1.1. Market Definition & Scope
1.2. Market Ecosystem
1.3. Currency & Limitations
1.3.1. Currency
1.3.2. Limitations
1.4. Key Stakeholders
2. Research Methodology
2.1. Research Approach
2.2. Data Collection & Validation Process
2.2.1. Secondary Research
2.2.2. Primary Research & Validation
2.2.2.1. Primary Interviews with Industry Experts
2.2.2.2. Country-/Region-Level Analysis
2.3. Market Estimation
2.3.1. Bottom-Up Approach
2.3.2. Top-Down Approach
2.3.3. Forecast Methodology
2.4. Data Triangulation
2.5. Assumptions
3. Executive Summary
4. Market Overview
4.1. Introduction
4.2. Market Dynamics & Impact Analysis
4.2.1. Drivers
4.2.1.1. Rapid Production Deployment of Generative AI Applications and AI Agents
4.2.1.2. Emergence of AI-Specific Attack Techniques
4.2.1.3. Rising Breaches Involving AI Models and Applications
4.2.1.4. Emerging AI Security Regulations and Standards
4.2.2. Restraints
4.2.2.1. Limited Maturity of AI Security Budgets and Ownership
4.2.2.2. Performance and Latency Trade-Offs of Runtime Protection
4.2.3. Opportunities
4.2.3.1. Rapid Growth of Agentic AI
4.2.3.2. Securing the AI Model Supply Chain
4.2.3.3. Integration of AI Security into Cybersecurity Platforms
4.2.4. Challenges
4.2.4.1. Rapidly Evolving AI Attack Techniques
4.2.4.2. Measuring the Effectiveness of AI Security Controls
4.2.4.3. Shortage of AI Security Skills
4.2.5. Trends
4.2.5.1. Platform Consolidation Through Acquisitions
4.2.5.2. Shift from Model-Centric to Agent-Centric Security
4.2.5.3. Automated AI Red Teaming
4.3. AI Threat Landscape
4.3.1. AI Attack Surface Across the Lifecycle
4.3.2. OWASP Top 10 for LLM Applications and NIST Adversarial ML Taxonomy
4.3.3. Notable AI Security Incidents and Vulnerabilities
4.4. AI Security Technology Landscape
4.4.1. Runtime Protection, AI Firewalls and Guardrails
4.4.2. Model Scanning, Provenance and AI Bill of Materials
4.4.3. AI Security Posture Management
4.4.4. Agent Security and Tool Authorization
4.5. AI Security vs. Traditional Application Security: Capability Comparison
4.6. Regulatory & Standards Landscape
4.7. AI Security Buyer Analysis & Purchase Criteria
4.7.1. Buying Center and Decision Process
4.7.2. Purchase Criteria
4.7.3. AI Security Adoption Journey
4.7.4. Platform vs. Specialist Decision
4.8. Business Models & Pricing Analysis
4.9. Value Chain & Ecosystem Analysis
4.10. Porter's Five Forces Analysis
4.11. Investment, Funding & M&A Analysis
4.12. Case Studies
4.12.1. Palo Alto Networks and Protect AI: Building a Platform for AI Security
4.12.2. F5 and CalypsoAI: AI Security in the Application Delivery Path
4.12.3. Check Point and Lakera: Runtime Guardrails and Threat Intelligence
4.12.4. CrowdStrike and Cisco: Extending Security Platforms to AI Detection and Response
4.12.5. Noma Security: Scaling an Independent Specialist in Agent Security
4.12.6. EchoLeak: The Case for Runtime Protection and Data Security for AI
5. AI Model Security Market, by Offering
5.1. Introduction
5.2. Solutions
5.2.1. AI Runtime Protection & Guardrails
5.2.1.1. AI Firewalls & Gateways
5.2.1.2. Application-Embedded Guardrails
5.2.2. AI Model Scanning & Supply Chain Security
5.2.2.1. Model Scanning
5.2.2.2. AI Bill of Materials & Provenance
5.2.3. AI Security Posture Management (AI-SPM)
5.2.4. AI Red Teaming & Vulnerability Testing
5.2.5. Data Security for AI
5.2.5.1. Training Data Protection
5.2.5.2. Prompt & Output Data Loss Prevention
5.2.6. AI Agent Security
5.2.6.1. Agent Identity & Access Management
5.2.6.2. Agent Runtime Monitoring & MCP Security
5.2.7. AI Access & Usage Control
5.2.7.1. Shadow AI Discovery
5.2.7.2. GenAI Usage Policy Enforcement
5.2.8. AI Detection & Response
5.2.9. AI Coding Assistant & Code Security
5.3. Services
5.3.1. AI Red Teaming & Assessment Services
5.3.2. Consulting & Compliance Services
5.3.3. Managed AI Security Services
6. AI Model Security Market, by Threat Type
6.1. Introduction
6.2. Prompt Injection & Jailbreaks
6.3. Sensitive Data Leakage
6.4. Data & Model Poisoning
6.5. Malicious Models & Supply Chain Compromise
6.6. Model Theft & Extraction
6.7. Adversarial Evasion
6.8. Excessive Agency & Tool Misuse
6.9. Resource Abuse & Denial of Service
7. AI Model Security Market, by AI Type
7.1. Introduction
7.2. Predictive & Traditional Machine Learning
7.3. Generative AI & LLM Applications
7.4. AI Agents & Agentic Systems
7.5. Edge & Embedded AI
8. AI Model Security Market, by Deployment Mode
8.1. Introduction
8.2. Cloud
8.3. On-Premises
8.4. Hybrid
9. AI Model Security Market, by Organization Size
9.1. Introduction
9.2. Large Enterprises
9.3. Small & Medium-Sized Enterprises
10. AI Model Security Market, by End User
10.1. Introduction
10.2. BFSI
10.3. Government & Defense
10.4. Healthcare & Life Sciences
10.5. IT & Telecommunications
10.6. Retail & E-Commerce
10.7. Manufacturing
10.8. Energy & Utilities
10.9. Other End Users
11. AI Model Security Market, by Geography
11.1. Introduction
11.2. North America
11.2.1. U.S.
11.2.2. Canada
11.3. Europe
11.3.1. U.K.
11.3.2. Germany
11.3.3. France
11.3.4. Netherlands
11.3.5. Switzerland
11.3.6. Ireland
11.3.7. Nordic Countries
11.3.8. Spain
11.3.9. Italy
11.3.10. Rest of Europe
11.4. Asia-Pacific
11.4.1. China
11.4.2. Japan
11.4.3. India
11.4.4. South Korea
11.4.5. Singapore
11.4.6. Australia & New Zealand
11.4.7. Rest of Asia-Pacific
11.5. Latin America
11.5.1. Brazil
11.5.2. Mexico
11.5.3. Rest of Latin America
11.6. Middle East & Africa
11.6.1. Israel
11.6.2. UAE
11.6.3. Saudi Arabia
11.6.4. Rest of Middle East & Africa
12. Competitive Landscape
12.1. Overview
12.2. Key Growth Strategies
12.3. Competitive Benchmarking
12.4. Competitive Dashboard
12.4.1. Industry Leaders
12.4.2. Market Differentiators
12.4.3. Vanguards
12.4.4. Emerging Companies
12.5. Market Share Analysis, by Key Player (2025)
13. Company Profiles
13.1. Profile Coverage
13.1.1. Business Overview
13.1.2. Financial Overview
13.1.3. Product Portfolio
13.1.4. Strategy and Market Position
13.1.5. Strategic Developments
13.1.6. SWOT Analysis
13.2. Companies Covered
13.2.1. Palo Alto Networks, Inc.
13.2.2. Cisco Systems, Inc.
13.2.3. Microsoft Corporation
13.2.4. CrowdStrike Holdings, Inc.
13.2.5. Check Point Software Technologies Ltd.
13.2.6. SentinelOne, Inc.
13.2.7. F5, Inc.
13.2.8. Cato Networks Ltd.
13.2.9. Zscaler, Inc.
13.2.10. Cloudflare, Inc.
13.2.11. Google LLC
13.2.12. International Business Machines Corporation
13.2.13. Amazon Web Services, Inc.
13.2.14. HiddenLayer, Inc.
13.2.15. Noma Security
13.2.16. Mindgard Ltd.
13.2.17. Zenity
13.2.18. Lasso Security
13.2.19. Cyera
13.2.20. Snyk Limited
14. Appendix
14.1. Customization Options
14.2. Available Related Reports
List of Tables [518]
List of Figures [71]
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