For years, cloud and security have been equally important but separate line items in the corporate budget, managed by different teams, often with different strategies and priorities.
By Richard Vester, chief executive: cloud at iOCO
The addition of AI has followed this trend, with most companies just adding it as another portfolio for the tech team to manage. As cloud and AI have become more deeply intertwined, this fragmented approach is doing more harm than good.
Companies that are keeping cloud, AI, and security as independent domains are ignoring the fact that they are interconnected components of a single operating environment. The cloud provides the infrastructure. It centralises data, scales resources, connects systems, and hosts the applications and processes that drive daily operations.
AI delivers the intelligence. It interprets the data stored and processed in the cloud, identifies patterns, predicts outcomes, and automates decision-making. Security safeguards the integrity of both. It ensures that data, models, and workflows remain accurate, compliant, and protected from manipulation or breach.
When cloud, AI, and security are treated independently, the result is an ecosystem that looks connected on the surface but lacks true integration. While many companies are used to bridging these types of gaps, this situation leads to a number of additional challenges across all three domains: Cloud implementations don’t deliver the desired results, AI projects produce compromised outputs, and security teams are constantly putting out fires.
Unlocking intelligence
AI models require vast amounts of computing power, storage, and data accessibility. Local systems often can’t provide these, so the cloud has become AI’s natural environment. However, if cloud and security ecosystems aren’t designed to work with AI in mind, performance bottlenecks, outages, or attacks can disrupt critical business processes.
Since AI models are only as reliable as the systems they run on, companies are quickly finding out that separately implemented AI, cloud, and security layers cause more problems than they solve.
Over time, security gaps, outdated models, and cloud misconfigurations become more obvious – and have a greater negative impact – leaving companies with solutions that can no longer perform the way they need to, and in some cases, become completely unusable.
Disconnected strategies, compromised outcomes
Traditional security focuses on firewalls, identity management, and encryption. While these are all important, they are insufficient in an AI-driven cloud ecosystem.
What’s needed is security that moves inside the workflows themselves. That means securing the data pipelines that AI depends on, both at rest and in transit, and protecting the decision layer so outputs and recommendations can’t be manipulated.
It also means that all environments should be designed for multi-cloud resilience, ensuring that critical workloads and data remain secure and available no matter where they run.
When organisations approach security in isolation, they introduce dangerous gaps. Even if monitoring and threat detection cover traditional endpoints, they can easily miss critical AI-driven processes in the cloud.
In addition, if compliance frameworks don’t extend consistently across infrastructure, intelligence, and security layers, you’re creating points of failure where errors, vulnerabilities, or malicious activity can spread quickly and silently.
The digital trinity
The cloud has made it possible to centralise and scale operations. AI is making it possible to automate insight and decision-making. Together, they’re becoming the digital mind of the business. If security isn’t integrated into this ecosystem from the start, a lot more could go wrong than a data breach.
The era of treating cloud, AI, and security as separate investments is over. They must be viewed as one integrated capability. Cloud provides the infrastructure, AI delivers the intelligence, and security ensures both remain trustworthy and resilient.
In other words, safeguarding your systems isn’t enough anymore. What matters is protecting – and enabling – the decisions, insights, and outcomes that those systems produce.