A recent industry analysis reveals a major gap in AI infrastructure security between marketing hype and actual enterprise needs.

The AI security market is facing a reality check, a moment of “the emperor has no clothes.” While spending grows, the infrastructure required for secure AI workloads is often overlooked. According to Latio’s Q2 2025 AI Security Market Report, 93% of security professionals recognize AI’s potential to enhance cybersecurity, but a staggering 77% of organizations feel unprepared to defend against AI-driven threats. More tellingly, when surveyed about their immediate AI security priorities, practitioners revealed a troubling disconnect between vendor messaging and real-world needs.

I’ve witnessed this disconnect firsthand through conversations with enterprise customers rushing to deploy AI workloads. The patterns mirror what we saw during container adoption — excitement trumping security, with painful lessons learned only after incidents occur.

Why Most AI Security Tools Miss the Real Threat

Most AI security vendors are solving yesterday’s problems while tomorrow’s AI infrastructure threats are already materializing. The report reveals that the majority of AI security solutions started by focusing on employee data loss prevention (DLP), essentially building fancy monitoring tools to watch what employees paste into ChatGPT. Others focus on AI “firewalls” that filter inputs and outputs of LLMs, which are useful but limited in scope. Meanwhile, the real risk is rapidly shifting toward AI applications that can access sensitive data and take actions on behalf of users.

This misdirection isn’t accidental. DLP-style solutions are easier to build and tap into CISOs’ familiar fear patterns. But as Microsoft Copilot and Google‘s Gemini mature with built-in guardrails, these monitoring solutions are becoming commoditized.

The fundamental issue isn’t just about AI security tools; it’s about whether the underlying infrastructure can support secure, scalable AI workloads with runtime protection. Many organizations are building AI applications on container foundations that were never designed for the isolation requirements that sensitive AI workloads demand.

How AI Infrastructure Choices Impact Security Risk

The report’s most crucial insight centers on “application runtime protection” — the security challenges that emerge when AI systems move beyond simple chatbots to become autonomous agents. Consider the risk evolution: A basic ChatGPT session carries minimal risk, but AI agents present entirely different threat landscapes. For example, AI coding assistants that can execute arbitrary code during development, or agents that query internal databases and trigger financial transactions. These scenarios demand infrastructure-level security controls.

Here’s where infrastructure decisions become critical. AI workloads processing sensitive data require the same kernel-level isolation that enterprises depend on for their most critical applications. The shared kernel model that many container-based AI deployments rely on creates attack vectors that simply can’t be adequately secured with application-layer protections alone.

According to Microsoft’s 2024 container security overview, container-based workloads face growing security threats, with security teams often unable to track which containers are running or vulnerable at any given time. For AI workloads handling sensitive data, these visibility gaps become critical vulnerabilities.

Infrastructure Features AI Security Requires Today

AI applications demand three infrastructure characteristics that traditional container deployments struggle to provide:

  • True kernel-level isolation: AI models processing proprietary data need kernel-level separation, not just namespace isolation. When an AI system can access customer records or financial data, shared kernel vulnerabilities become business-critical risks.
  • High-performance architecture without compromise: AI inference workloads are latency-sensitive. The I/O overhead inherent in many container architectures can make real-time AI applications unusable.
  • Dynamic resource allocation: Unlike traditional applications, AI workloads have highly variable resource needs. Infrastructure that can dynamically allocate GPU and memory resources based on actual demand eliminates the massive overprovisioning waste that plagues standard container deployments.

Should You Build, Buy, or Wait for AI Security Solutions?

The investment landscape tells a revealing story. While organizations are spending over $320 billion on AI infrastructure in 2025, the AI security market represents just $25 billion — a fraction focused on securing these massive infrastructure investments. This disparity highlights the gap between infrastructure spending and security preparation.

The answer to whether organizations should invest in dedicated AI security tools depends on your AI adoption velocity and infrastructure maturity. If you’re building AI applications that handle sensitive data, dedicated tools offer specialization that incumbents can’t match. But architecture decisions matter more than vendor selection. Organizations that build AI infrastructure with hypervisor-level isolation and secure multitenancy from the ground up have significant advantages over those trying to retrofit security into existing container deployments.

Traditional security vendors are rapidly adding AI capabilities, but they’re playing catch-up to solutions designed from the ground up for this threat landscape. The most effective approaches combine proven security principles, like hypervisor-based isolation, with modern implementations optimized for AI workload characteristics.

Looking Into the Crystal Ball: Future AI Security Trends

Based on the report’s findings, three trends are emerging:

  • DLP-focused AI security will consolidate rapidly as platforms build native protections. 
  • Runtime application protection will drive the majority of market growth, with infrastructure security becoming a key differentiator. 
  • “Security by design” versus “security as afterthought” will separate market leaders from followers.

How Should Organizations Strengthen Their AI Security Strategy Today?

The AI security market’s growing pains mirror past technology adoption cycles. Organizations that will thrive are those that focus on their actual risk profile and infrastructure foundation rather than chasing security trends.

Start by asking these questions: Where is AI actually deployed in your organization? What data can it access? Can your current infrastructure provide adequate isolation for these workloads? The answers should drive your AI security strategy, not vendor marketing.

Apply time-tested infrastructure principles while adapting to AI’s unique requirements. Those who build secure, efficient foundations will outperform those who bolt security onto inadequate infrastructure.

The emperor may not have clothes, but recognizing that reality is the first step toward building AI security that actually works.

Frequently Asked Questions

What is infrastructure-level AI security?

Infrastructure-level AI security refers to isolating and protecting AI workloads at the kernel or hypervisor level rather than relying only on application-layer tools.

Why aren't traditional containers secure enough for AI?

Traditional containers share a Linux kernel, which introduces attack surfaces. AI workloads that access sensitive data need stronger isolation to prevent escapes.

What’s the difference between runtime protection and DLP in AI security?

DLP monitors for data leaks, often at the surface level. Runtime protection secures the execution environment of AI agents, reducing the risk of malicious actions or code execution.

How can organizations secure AI infrastructure?

By adopting infrastructure that supports kernel-level isolation, dynamic resource allocation, and low-latency performance without overprovisioning.

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‍A version of this blog was previously published on The New Stack on June 23, 2025.