Securing AI Infrastructure Starts With Securing Trust

AI has moved rapidly from experimentation to operational reality. Boards, executive teams, and technology leaders are under pressure to find where AI can help. That means improving productivity, cutting costs, and creating competitive advantage. Yet as organizations integrate large language models (LLMs), AI agents, and generative AI applications into business processes, many are discovering something important. Security, governance, and operational control now matter just as much as innovation.

AI Adoption Is Outpacing AI Security

The pace of adoption shows how quickly AI has embedded itself in enterprise environments. The Fortinet 2026 Cybersecurity Skills Gap Global Research Report found that 91 per cent of organizations are already using or experimenting with AI-powered cybersecurity solutions. That underscores the growing reliance on AI across business and security operations. At the same time, the report found that 50 per cent of organizations cite data privacy and information security as their biggest AI implementation challenge. That points to a growing need for stronger governance and security controls as adoption accelerates.

Conversations about AI often center on its capabilities. Organizations want to know what AI can do, how quickly they can deploy it, and what value it can deliver. However, the more important question is increasingly becoming: how do we secure it?

Cornelius Mare, chief information security officer, Australia, Fortinet, said, “AI adoption is accelerating because organizations recognize its potential to improve productivity, decision-making, and operational efficiency. The challenge is that every new AI model, application, and agent also introduces a new attack surface. Businesses need to think about governance, security, and resilience from the outset rather than treating them as an afterthought.”

Why the AI Model Itself Needs Protection

Many organizations focus on securing the applications that connect to AI models; however, the models themselves require protection. Unlike traditional applications, LLMs interact using natural language, creating risks that conventional security controls were not designed to address. This shift introduces a new set of security concerns that span governance, intellectual property protection, operational resilience, and cost management.

The risks extend beyond external attackers. Malicious prompts, excessive consumption attacks, model poisoning, or unauthorized access can all manipulate AI systems. Organizations must also consider how AI models interact with other systems, what data they access, and what actions they can perform. Without appropriate controls, an AI application can become a pathway to sensitive information, critical infrastructure, or business processes.

Cornelius Mare said, “Security leaders need visibility into who is accessing AI models, what those models are accessing, and how information is moving through the environment. The goal is not to restrict innovation. It is to create the guardrails that allow organizations to innovate safely.”

Interconnected AI Systems Widen the Attack Surface

A growing shortage of specialist skills compounds the challenge. According to Fortinet’s research, 60 per cent of organizations report difficulty finding cybersecurity professionals with AI-specific expertise. Another 63 per cent expect to need additional AI oversight and governance roles within the next three years. As AI becomes more deeply embedded in operations, organizations need the people, processes, and governance structures to manage these technologies effectively.

The emergence of model context protocol (MCP) servers, AI agents, and interconnected AI ecosystems adds another layer of complexity. AI models are no longer operating in isolation. They increasingly interact with external tools, application programming interfaces (APIs), databases, and internet-based services. Every connection creates potential exposure and expands the number of pathways available to threat actors.

Guidance from the OWASP Top 10 for Large Language Model Applications reflects this evolving landscape, highlighting risks including prompt injection, model poisoning, excessive resource consumption, and sensitive data disclosure. These risks require security controls that understand context and intent within natural language interactions. Traditional network or application-layer protections alone are not enough.

Balancing Security, Performance, and Trust

At the same time, organizations must maintain both security and performance. Businesses often measure AI initiatives by their ability to deliver outcomes at scale. Security controls cannot become a bottleneck that increases latency, disrupts user experience, or drives up operational costs. Instead, organizations need to embed security into AI infrastructure in a way that supports performance, governance, and compliance simultaneously.

For organizations operating AI workloads in cloud environments, visibility becomes even more important. Hybrid and multi-cloud architectures can create blind spots across data flows, permissions, and workloads. Understanding how AI applications interact with cloud resources, where sensitive information resides, and how organizations manage access remains fundamental to reducing risk.

Cornelius Mare said, “The organizations that will gain the greatest value from AI are those that approach it as both a business opportunity and a security challenge. AI infrastructure needs the same level of governance, visibility, and operational discipline that organizations apply to critical business systems.”

As AI becomes embedded across enterprise environments, securing the underlying infrastructure will become a core requirement for maintaining trust, resilience, and business continuity. The organizations that succeed will build security into every stage of the AI lifecycle. That means addressing model deployment and access management through to runtime protection, monitoring, and compliance.

Cornelius Mare said, “AI is changing how organizations operate. Securing AI infrastructure is about ensuring these systems remain trustworthy, resilient, and aligned with business objectives so organizations can innovate with confidence as adoption continues to accelerate.”

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