AI Model Security Market (2026-2036)

The growth of this market is mainly driven by the rapid production deployment of generative AI applications and AI agents, the emergence of AI-specific attack techniques such as prompt injection, data poisoning, and malicious models, rising breaches involving AI models and applications, and emerging AI security regulations and standards. However, the limited maturity of AI security budgets and ownership, together with performance and latency trade-offs of runtime protection, restrain the growth of this market.

Furthermore, the rapid growth of agentic AI, the need to secure the AI model supply chain, and the integration of AI security into established cybersecurity platforms are expected to offer growth opportunities for the stakeholders in this market. However, the rapidly evolving nature of AI attack techniques remains a major challenge impacting the growth of this market. Additionally, platform consolidation through acquisitions, the shift from model-centric to agent-centric security, and automated AI red teaming are prominent trends in this market.

The AI Model Security Market comprises solutions and services that protect AI models, AI applications, and AI agents from threats across their lifecycle, from training data and model development through deployment and runtime operation. The market includes AI model scanning and supply chain security, AI runtime protection and guardrails, AI security posture management (AI-SPM), AI red teaming and vulnerability testing, data security for AI, AI agent security, and controls for employee use of AI applications. These solutions address AI-specific threats such as prompt injection and jailbreaks, data poisoning, adversarial evasion, model theft and extraction, sensitive data leakage, malicious or backdoored models, and excessive agent autonomy. The market ecosystem extends from AI security specialists and established cybersecurity platform vendors to cloud and model providers, data security vendors, security service providers, and the security, AI engineering, and risk teams that deploy these controls.

The market is experiencing strong growth as AI adoption outpaces the controls needed to secure it. According to IBM's 2025 Cost of a Data Breach Report, which studied 600 breached organizations, 13% of organizations reported breaches of AI models or applications and a further 8% did not know whether they had been compromised in this way. Among organizations that reported AI-related breaches, 97% lacked proper AI access controls, and 63% of breached organizations had no AI governance policies to manage AI or prevent shadow AI. Organizations with high levels of shadow AI incurred an average of USD 670,000 in additional breach costs. These findings indicate that AI systems are already a high-value target and that existing security controls do not adequately cover them.

AI systems introduce an attack surface that conventional application and network security tools were not designed to address. Models can be manipulated through natural-language inputs, compromised through poisoned training data or tampered model files, and induced to disclose sensitive information or take unintended actions. The OWASP Top 10 for LLM Applications 2025, published in November 2024, ranks prompt injection as the most critical risk for the second consecutive edition, while the National Institute of Standards and Technology (NIST) published an updated taxonomy of adversarial machine learning attacks and mitigations, NIST AI 100-2 E2025, in March 2025 covering both predictive and generative AI systems. The rise of AI agents that can call tools, access enterprise systems, and act autonomously further expands this attack surface, as a successful manipulation can now result in actions rather than only inaccurate outputs.

The competitive landscape has been reshaped by a wave of acquisitions in 2024 and 2025, as established cybersecurity vendors chose to acquire AI security capabilities rather than build them. Cisco acquired Robust Intelligence in 2024, Palo Alto Networks completed its acquisition of Protect AI in July 2025, and in September 2025 alone, Cato Networks acquired Aim Security, SentinelOne completed its acquisition of Prompt Security, F5 agreed to acquire CalypsoAI for USD 180 million, Check Point agreed to acquire Lakera, and CrowdStrike agreed to acquire Pangea to launch AI Detection and Response. Independent vendors continue to attract significant capital, with Noma Security raising USD 100 million in July 2025 and HiddenLayer raising a USD 100 million Series B in September 2026.

Regulation is formalizing AI security as a compliance requirement. The EU AI Act requires high-risk AI systems to achieve appropriate levels of accuracy, robustness, and cybersecurity, including resilience against data poisoning, adversarial examples, and model flaws, and requires providers of general-purpose AI models with systemic risk to ensure adequate cybersecurity protection. Following adoption of Regulation (EU) 2026/1744, high-risk obligations for stand-alone Annex III systems apply from 2 December 2027 and for AI embedded in regulated products from 2 August 2028. The European Telecommunications Standards Institute (ETSI) published baseline cybersecurity requirements for AI systems in ETSI TS 104 223 in April 2025, further supporting the development of standardized AI security controls.