적대적 알고리즘 경쟁 및 방어형 AI 시장 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the adversarial algorithmic competition and defensive AI market size is expected to grow from USD 3.42 billion in 2025 to USD 4.33 billion in 2026 and is forecast to reach USD 15.99 billion by 2031 at 29.86% CAGR over 2026-2031.

This report is Segmented by Offering (Software, and Services), Security Assessment Focus (Prompt Injection and Jailbreak Testing, and More), Deployment (Cloud, On-Premises, and Hybrid), Enterprise Size (Large Enterprises, and More), End-User Industry (BFSI, Healthcare and Life Sciences, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Adversarial Algorithmic Competition and Defensive AI Market Trends and Insights

Rapid Adoption Of Generative and Agentic AI in Regulated Workflows

Regulated sectors have moved past early pilots, and the adversarial algorithmic competition and defensive AI market are benefiting from how AI is now tied to customer service, compliance, fraud review, and clinical support tasks that require stronger oversight. In financial services, the Cambridge Center for Alternative Finance reported in April 2026 that 71% of surveyed institutions were actively adopting GenAI, 52% were actively adopting agentic AI, and 48% identified adversarial AI threats as a top concern. Healthcare showed a similar pattern, as NVIDIA reported that 69% of organizations were using generative AI or large language models in 2026, up from 54% in 2025, while 40% said HIPAA, FDA approval processes, and GDPR were shaping their agentic AI approach. This matters because agentic systems do not stay within a single model boundary; they connect to tools, databases, and application layers, widening the number of points where misuse can occur. As a result, the adversarial algorithmic competition and defensive AI market is seeing stronger demand for testing that covers full workflows rather than isolated model behavior.

Rising Frequency Of Prompt Injection, Jailbreak, and Model Tampering Attacks

Attack methods are becoming more effective, and that is a direct growth driver for the adversarial algorithmic competition and defensive AI market. A June 2026 study in Scientific Reports recorded aggregate jailbreak attack success rates of 91.1% to 94.6% across several open-weight 7-billion-parameter model families, using prompts that remained readable enough to bypass simple filtering rules. The 2025 review of prompt injection attacks also noted that 53% of enterprise deployments rely on retrieval-augmented generation and agentic pipelines, which increases the risk that poisoned or manipulated external content can influence model behavior at runtime. The same review highlighted that system prompt leakage and vector and embedding weaknesses had become distinct areas of concern, showing that the attack taxonomy is expanding rather than settling into a stable pattern. Research discussed in that framework also showed that 5 carefully crafted poisoned documents among millions could achieve a 90% attack success rate, which helps explain why the adversarial algorithmic competition and the defensive AI market are moving toward continuous, automated red teaming.

Shortage of Specialized Adversarial AI Security Talent

The adversarial AI skills gap remains a practical limit on how fast the adversarial algorithmic competition and defensive AI market can scale. The Linux Foundation found in its 2025 global tech talent report that 68% of surveyed organizations were understaffed in AI and ML, while only 25% reported dedicated AI security management capabilities. Fortinet reinforced this pattern in 2025, reporting that 97% of IT decision-makers planned to deploy AI security solutions, yet 48% cited the lack of staff with enough AI expertise as the primary implementation challenge. CyberSeek also showed in June 2025 that the United States cybersecurity workforce supply-demand ratio stood at 74%, and recruiting periods for AI-specific security roles ran 21% longer than those for traditional security positions. This is pushing organizations toward automated tools and managed services, which support some spending categories inside the adversarial algorithmic competition and defensive AI market, even while it slows full in-house adoption.

Other drivers and restraints analyzed in the detailed report include:

1. Regulatory Pressure For AI Safety, Auditability, and Robustness Testing
2. Expansion of AI Governance Programs in Large Enterprises
3. High Cost Of Continuous Testing, Tooling, and Expert Services

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

Software held 61.22% share of the adversarial algorithmic competition and defensive AI market in 2025. That position came from red teaming platforms, model security testing tools, deepfake detection products, and defensive monitoring systems that are replacing manual and fragmented workflows. The biggest shift inside software is platform convergence, as buyers prefer a single environment that can scan models, monitor posture, run adversarial tests, and support runtime controls. Palo Alto Networks showed that direction when it introduced Prisma AIRS in April 2025 and later expanded the platform during Cyber Week 2026 with broader coverage for AI agents, applications, models, and datasets. The value of that integrated design is not only technical, because the adversarial algorithmic competition and the defensive AI market now reward platforms that can also generate audit-ready evidence aligned with compliance expectations. Buyers in regulated sectors increasingly want proof that testing records, governance actions, and runtime findings can be documented in a form that satisfies internal risk reviews and external obligations.

Services are projected to expand at a 30.91% CAGR through 2031, making it the fastest-growing segment in the adversarial algorithmic competition and defensive AI market. The core reason is simple: many enterprises still lack internal teams that can keep up with evolving attack methods, new model releases, and frequent workflow updates. CrowdStrike moved early on that demand when it launched AI Red Team Services in November 2024, positioning the service around proactive assessments for AI systems and large language models aligned with OWASP-style attack paths. Managed service demand also rises because each prompt change, model update, or external tool connection can create a fresh testing requirement that internal teams may not be ready to handle on schedule. That is why the services side of the adversarial algorithmic competition and defensive AI industry is expanding fastest, even while software remains the larger revenue pool.

Threat Intelligence and Threat Analysis accounted for the largest security assessment sub-segment, with a 19.14% share in 2025. Enterprises still begin with threat understanding because model misuse, prompt injection, privacy leakage, poisoning, and synthetic media abuse do not follow one common attack pattern. Large clients now expect multi-focus programs that combine several testing lenses rather than isolated checks against a single known weakness. The Cambridge Center for Alternative Finance reported that 50% of financial institutions and 57% of regulators saw adversarial AI-related cyber threats as a top concern, which helps explain why early threat analysis remains a priority. In practice, buyers use this assessment layer to decide where deeper testing should sit across models, prompts, training pipelines, and tool-connected agents. That front-end role keeps threat analysis central to commercial demand even as newer categories gain speed.

Continuous AI Security Monitoring is projected to expand at a 31.02% CAGR through 2031, making it the fastest-growing focus area in the adversarial algorithmic competition and defensive AI market. Static pre-deployment testing can miss issues that only appear after the model begins to handle live data, changing prompts, and real user behavior. OpenSSF stated in August 2025 that security checks should be embedded across the full ML lifecycle, from data ingestion through monitoring at inference time. Microsoft reinforced that move in May 2026 by open-sourcing RAMPART, which turns agent safety scenarios and adversarial findings into repeatable CI pipeline tests rather than one-off exercises. As more teams treat security testing as an engineering control instead of a periodic review, this part of the adversarial algorithmic competition and defensive AI market is likely to keep outpacing every other assessment category.

Complete Report Scope:

• By Offering
  • Software
    • AI Red Teaming Platforms
    • Model Security Testing Platforms
    • Deepfake Detection Platforms
    • Defensive AI Monitoring Platforms
  • Services
• By Security Assessment Focus
  • Prompt Injection and Jailbreak Testing
  • Model Theft and Privacy Testing
  • Training Pipeline and Data Poisoning Testing
  • Deepfake and Synthetic Media Defense
  • Continuous AI Security Monitoring
• By Deployment
  • Cloud
  • On-Premises
  • Hybrid
• By Enterprise Size
  • Large Enterprises
  • Small and Medium Enterprises
• By End-user Industry
  • BFSI
  • Healthcare and Life Sciences
  • Information Technology and Telecom
  • Retail and E-commerce
  • Industrial Manufacturing
  • Government and Public Sector
  • Other End-user Industries
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Rest of South America
  • Europe
    • Germany
    • United Kingdom
    • France
    • Italy
    • Spain
    • Russia
    • Rest of Europe
  • Asia-Pacific
    • China
    • India
    • Japan
    • South Korea
    • Australia
    • Rest of Asia-Pacific
  • Middle East and Africa
    • Middle East
      • Saudi Arabia
      • United Arab Emirates
      • Rest of Middle East
    • Africa
      • South Africa
      • Nigeria
      • Rest of Africa

Geography Analysis

North America accounted for 32.18% of the adversarial algorithmic competition and defensive AI market in 2025, making it the largest regional revenue base. The region benefits from a dense mix of AI-first enterprises, advanced cybersecurity vendors, and regulated sectors that are moving faster on formal AI oversight. NIST strengthened that environment through its AI Risk Management Framework work, including the GenAI profile released in 2024 and the April 2026 concept note for a critical infrastructure profile that is now shaping procurement language. Canada also added momentum through its 2026 SME AI deployment toolkit linked to the G7 process, which supports more structured adoption among mid-sized organizations. Mexico remains earlier in the cycle, but financial services digitization and the regional expansion of United States-led AI platforms are helping build a broader demand base for adversarial algorithmic competition and the defensive AI market.

Asia-Pacific is projected to grow at a 31.46% CAGR through 2031, the fastest among all regions in the adversarial algorithmic competition and defensive AI market. Its momentum comes not only from AI adoption but also from the fact that enterprises often face multiple compliance frameworks across the region simultaneously. Japan enacted Act No. 53 of 2025 on June 4, 2025, and later established the National AI Basic Plan in September 2025, which encourages more structured assurance for AI development and use. China introduced its first policy framework on agentic AI in May 2026, and amendments to the Cybersecurity Law clarified AI governance within cybersecurity regulation, raising maximum fines to CNY 50 million (USD 6.9 million) or 5% of prior-year turnover. South Korea's AI Basic Act adds another binding layer in 2026, and together these frameworks are increasing the frequency with which regional enterprises procure testing and assurance capabilities.

Europe remains structurally important in the adversarial algorithmic competition and defensive AI market because regulation has moved from policy discussion into direct implementation. The EU AI Act becomes fully applicable on August 2, 2026, and its treatment of high-risk systems has created a clear procurement trigger in financial services, healthcare, critical infrastructure, and education. Germany, the United Kingdom, and France anchor current demand, while Southern and Eastern Europe are following compliance timelines more closely than pure AI maturity curves. The Middle East and Africa, led by Saudi Arabia and the UAE, and South America, led by Brazil and Argentina, are still smaller contributors today, but they are becoming useful entry markets for vendors that want early platform positions before larger compliance cycles arrive.

1. Microsoft Corporation
2. Google LLC
3. Amazon Web Services Inc.
4. International Business Machines Corporation
5. Palo Alto Networks Inc.
6. CrowdStrike Holdings, Inc.
7. SentinelOne, Inc.
8. Darktrace plc
9. Check Point Software Technologies Ltd.
10. Fortinet, Inc.
11. Rapid7, Inc.
12. Trend Micro Incorporated
13. Cisco Systems, Inc.
14. BlackBerry Limited
15. Elastic N.V.
16. Vectra AI, Inc.
17. Noma Security Ltd.
18. Protect AI, Inc.
19. Adversa AI Ltd.
20. HiddenLayer, Inc.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 Rapid Adoption of Generative and Agentic AI in Regulated Workflows
  • 4.2.2 Rising Frequency of Prompt Injection, Jailbreak, and Model Tampering Attacks
  • 4.2.3 Regulatory Pressure for AI Safety, Auditability, and Robustness Testing
  • 4.2.4 Expansion of AI Governance Programs in Large Enterprises
  • 4.2.5 Demand for Continuous Red Teaming in DevSecOps and MLOps Pipelines
  • 4.2.6 Increasing Use of AI in High Stakes Decision Systems
• 4.3 Market Restraints
  • 4.3.1 Shortage of Specialized Adversarial AI Security Talent
  • 4.3.2 High Cost of Continuous Testing, Tooling, and Expert Services
  • 4.3.3 Explainability Gaps and Liability Uncertainty in Autonomous AI Defenses
  • 4.3.4 Fragmented Data Provenance and Cross Border Compliance Constraints
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Industry Value-Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power of Buyers
  • 4.8.2 Bargaining Power of Suppliers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Offering
  • 5.1.1 Software
    • 5.1.1.1 AI Red Teaming Platforms
    • 5.1.1.2 Model Security Testing Platforms
    • 5.1.1.3 Deepfake Detection Platforms
    • 5.1.1.4 Defensive AI Monitoring Platforms
  • 5.1.2 Services
• 5.2 By Security Assessment Focus
  • 5.2.1 Prompt Injection and Jailbreak Testing
  • 5.2.2 Model Theft and Privacy Testing
  • 5.2.3 Training Pipeline and Data Poisoning Testing
  • 5.2.4 Deepfake and Synthetic Media Defense
  • 5.2.5 Continuous AI Security Monitoring
• 5.3 By Deployment
  • 5.3.1 Cloud
  • 5.3.2 On-Premises
  • 5.3.3 Hybrid
• 5.4 By Enterprise Size
  • 5.4.1 Large Enterprises
  • 5.4.2 Small and Medium Enterprises
• 5.5 By End-user Industry
  • 5.5.1 BFSI
  • 5.5.2 Healthcare and Life Sciences
  • 5.5.3 Information Technology and Telecom
  • 5.5.4 Retail and E-commerce
  • 5.5.5 Industrial Manufacturing
  • 5.5.6 Government and Public Sector
  • 5.5.7 Other End-user Industries
• 5.6 By Geography
  • 5.6.1 North America
    • 5.6.1.1 United States
    • 5.6.1.2 Canada
    • 5.6.1.3 Mexico
  • 5.6.2 South America
    • 5.6.2.1 Brazil
    • 5.6.2.2 Argentina
    • 5.6.2.3 Rest of South America
  • 5.6.3 Europe
    • 5.6.3.1 Germany
    • 5.6.3.2 United Kingdom
    • 5.6.3.3 France
    • 5.6.3.4 Italy
    • 5.6.3.5 Spain
    • 5.6.3.6 Russia
    • 5.6.3.7 Rest of Europe
  • 5.6.4 Asia-Pacific
    • 5.6.4.1 China
    • 5.6.4.2 India
    • 5.6.4.3 Japan
    • 5.6.4.4 South Korea
    • 5.6.4.5 Australia
    • 5.6.4.6 Rest of Asia-Pacific
  • 5.6.5 Middle East and Africa
    • 5.6.5.1 Middle East
      • 5.6.5.1.1 Saudi Arabia
      • 5.6.5.1.2 United Arab Emirates
      • 5.6.5.1.3 Rest of Middle East
    • 5.6.5.2 Africa
      • 5.6.5.2.1 South Africa
      • 5.6.5.2.2 Nigeria
      • 5.6.5.2.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 Microsoft Corporation
  • 6.4.2 Google LLC
  • 6.4.3 Amazon Web Services Inc.
  • 6.4.4 International Business Machines Corporation
  • 6.4.5 Palo Alto Networks Inc.
  • 6.4.6 CrowdStrike Holdings, Inc.
  • 6.4.7 SentinelOne, Inc.
  • 6.4.8 Darktrace plc
  • 6.4.9 Check Point Software Technologies Ltd.
  • 6.4.10 Fortinet, Inc.
  • 6.4.11 Rapid7, Inc.
  • 6.4.12 Trend Micro Incorporated
  • 6.4.13 Cisco Systems, Inc.
  • 6.4.14 BlackBerry Limited
  • 6.4.15 Elastic N.V.
  • 6.4.16 Vectra AI, Inc.
  • 6.4.17 Noma Security Ltd.
  • 6.4.18 Protect AI, Inc.
  • 6.4.19 Adversa AI Ltd.
  • 6.4.20 HiddenLayer, Inc.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment