Rising cyber threats targeting AI training and inference environments are driving demand for specialised cyber security solutions. Attackers are increasingly exploiting vulnerabilities in datasets, models, and runtime processes through data poisoning, model manipulation, and inference attacks. As enterprises rely more on AI-driven decisions, securing these environments becomes critical to ensure accuracy, integrity, and resilience.

SMEs increasingly adopting AI driven tools

SMEs are expected to witness the fastest growth in the agentic AI security market as they increasingly adopt AI-driven tools and autonomous systems to improve operational efficiency and decision-making. Unlike large enterprises, SMEs are often moving directly toward AI-native applications, including agent-based automation across customer service, marketing, and operations. This rapid adoption is exposing them to new security risks, including unauthorised agent actions, data leakage, and vulnerabilities in AI models. At the same time, SMEs typically have limited in-house cybersecurity expertise and resources, making them more reliant on external solutions. This is driving strong demand for managed security services, AI-native security platforms, and easy-to-deploy tools that can provide protection without requiring deep technical expertise. As awareness of AI-related risks continues to grow and more SMEs integrate autonomous systems into their workflows, their spending on agentic AI security is expected to increase at a faster pace compared to large enterprises, making this segment the fastest-growing in the market.

Securing the infrastructure layers 

The infrastructure layer segment is expected to hold the largest share of the agentic AI security market, as it represents the foundational environment in which AI models, agents, and applications are developed, deployed, and executed. Enterprises continue to rely heavily on cloud platforms, data centers, and high-performance computing environments to run agentic AI systems at scale. As a result, securing this layer becomes a priority, since any vulnerability at the infrastructure level can impact the entire AI stack. Organisations are investing in securing compute resources, containerised environments, and cloud-native architectures to ensure safe deployment of AI workloads. Additionally, the growing adoption of hybrid and multi-cloud environments further increases the need for consistent infrastructure-level security controls. This includes runtime protection, workload isolation, and continuous monitoring of infrastructure behaviour. Since all higher layers, including models and agents, depend on this foundation, spending at the infrastructure level remains significant, making it the largest segment in the market.

High growth expected for Asia Pacific

Asia Pacific is emerging as a high-growth region in the agentic AI security market, driven by the rapid adoption of AI technologies and increasing exposure to AI-enabled cyber threats. According to IBM, Asia Pacific recorded the highest average cost of data breaches in several countries, reflecting growing security challenges in digital environments. AI-driven attacks are also rising across the region, particularly in sectors such as BFSI, telecom, and manufacturing, where automation and AI adoption are accelerating. According to Check Point Software Technologies, organisations in Asia experience a high volume of weekly cyber attacks, many of which are increasingly leveraging AI-based techniques.

Additionally, the rapid expansion of digital infrastructure, cloud adoption, and API-driven ecosystems is increasing the attack surface. As enterprises deploy more autonomous systems, the need to secure AI agents, models, and data pipelines is becoming more critical, driving strong demand for agentic AI security solutions across the region.