Over the last year, the confidence of IT professionals has increased when it comes to AI security. Recent research revealed that 86% of security leaders feel prepared to defend against data poisoning attacks, that is, threats that corrupt the very training data on which AI models rely.
But here’s the reality: 26% of UK and US companies report they’ve already fallen victim to these attacks. That’s one in four organisations learning the hard way that AI can open yet another new dimension of vulnerability.
The reason isn’t a lack of sophisticated tools or talented people. It’s that we’re treating AI security as something separate from network security, when in reality, they’re inseparable. AI runs on your network, consumes your data and makes decisions that cascade across your entire infrastructure. If your network isn’t unified and IT doesn’t have complete visibility into both network activity and AI actions, your defences will always have gaps. But when the network acts as a secure foundation with clear data governance strategies in place, companies are better positioned to deploy AI without impacting their security posture.
Fragmentation’s hidden cost
Most enterprise networks weren’t built with AI or modern threats in mind. As companies grow through acquisitions, their IT and security environments accumulate disparate tools and processes from different parts of the business. Over time, this leads to disjointed workflows across the entire IT stack and a patchwork of systems that rarely communicate effectively, managed by teams that are both siloed and overextended.
Similarly, security tool expansion, while a natural response to evolving risks, can have the same effect. When new security capabilities are added through standalone solutions that don’t integrate into the broader ecosystem, they contribute to further fragmentation. This level of disconnect might have been manageable when threats were external and predictable. But AI has changed the game. Data poisoning attacks, for example, don’t loudly announce themselves with malware signatures or unusual traffic patterns. They silently corrupt models from within, forcing them to make bad decisions that appear legitimate, and if systems aren’t unified, it’s challenging to track down the source of the corruption.
In this environment, every new tool or application adds complexity, blind spots, and operational strain. As AI is adopted at unprecedented speed, defending against AI-driven threats requires the agility and cohesion of a unified security strategy instead of a growing cluster of disconnected solutions.
Shadow AI adds another layer of risk. The same research found that 37% of enterprises are seeing employees use generative AI tools without permission – and that’s likely just the tip of the iceberg. When already stretched IT teams can’t keep pace with business demands, people find their own solutions, often outside official processes. This accelerates fragmentation and leads to delayed alerts, inconsistent workflows, and difficulty correlating events across systems. It also introduces data-leakage and compliance risks, but more fundamentally, these unsanctioned tools are invisible to traditional security monitoring, leaving organisations blind to threats until it’s too late.
The solution is clear: fragmented tools cannot keep up with the speed and subtlety of AI-driven threats. Only a unified, integrated approach can close the gaps, maintain visibility and keep organisations secure and resilient.
Rethinking the network as a security foundation
The answer to security in the AI age isn’t more tools. It’s simplification. We need to stop thinking about networks as passive infrastructure and start treating them as a moving, intelligent foundation for security. The network can become a window into activity happening across the organisation and help unify all your security functions into a coherent defence.
To harness the power of the network, organisations must break down silos between network operations, security operations and AI governance. By integrating these solutions into a single platform, organisations can embed security directly into the foundation of their infrastructure rather than bolting it on afterwards. When the network acts as the connective tissue between systems, it delivers enterprise-wide visibility, reduces blind spots and gives AI the context it needs to detect and respond to threats effectively.
Critically, this approach lets you treat AI as an entity with specific access requirements, behaviour patterns and risk profiles, just like you would a new employee; you wouldn’t grant them unlimited access to every system. In the same way, you shouldn’t allow AI models to operate without granular controls and continuous oversight.
To make this possible, organisations must enforce strict identity and access controls for AI systems through Zero Trust principles, while maintaining full visibility across the environment. A unified network fabric can also help provide the foundation for consistent Zero Trust enforcement for both human and machine users alike, while supporting microsegmentation to keep sensitive areas isolated and protected from lateral movement.
What CISOs should prioritise now
To modernise your security environment for the AI era, start by conducting an honest audit of the business processes and data that need protection and assess how your existing tech stacksupports them. How many security solutions do you have? How well do they integrate? Where are the visibility gaps? Then prioritise establishing a foundational structure, starting with the network layer, all the way up to the data and users, that can unify these functions, not through dashboards, but through genuine architectural integration. Consolidation through a unified platform allows consistent policies and oversight to extend all the way to the enterprise edge, wherever data resides, ensuring security remains both comprehensive and manageable.
Next, implement AI-specific governance. This isn’t just about acceptable use policies, though those do matter. It’s about network-level controls that can detect when AI systems behave unexpectedly, when data flows into models from untrusted sources, or when shadow AI appears in your environment. Your network fabric should make these scenarios both observable and actionable.
Finally, think proactively rather than reactively. Data poisoning, deepfakes, AI-generated phishing – these threats will keep evolving. The only sustainable defence is a security architecture that adapts as quickly as the threats do. When security is embedded directly into the network, IT and security teams gain a single source of truth – making it easier and faster to identify, respond to, and eliminate threats, whether or not attackers are using AI.
From confidence to competence
That 86% confidence level from the IO research? It’s not entirely misplaced. CISOs understand the stakes. But confidence without the right architecture is just optimism. As AI becomes more deeply embedded in how we work, our security has to be woven just as deeply into the network itself.
The organisations that get this right won’t be the ones with the most tools. They’ll be the ones that made their entire network intelligent, adaptive, and unified. Because in the AI era, the network is one of your strongest security assets.
