Right now, enterprise infrastructure is running a live experiment. AI is being deployed faster than it can be fully understood, with teams testing, tuning, and scaling models across hybrid environments in real time. Few are following a standard playbook, and most are building the plane as it ascends from the runway towards the wide-open sky.
This rapid acceleration in AI adoption increases the pressure to act. Business leaders see transformative innovation in applying AI to streamline business process and operations and unlock new forms of customer value. As a result, AI is moving quickly into production, embedded in workflows, integrated with cloud services, and shaping decisions across departments. The sense of urgency is driving rapid progress, but it is also creating new blind spots in hybrid cloud infrastructure that many organisations are simply not prepared or equipped to manage.
The systems supporting AI are now creating greater volumes of traffic, more complexity, and handling a wider range of behaviours than ever before. At the same time, oversight has become substantially more difficult. Shadow AI is expanding behind the scenes, model behaviour is often unclear, and data is moving in ways that traditional tools can’t fully capture. What was once a controlled environment is now a dynamic, fragmented one, where visibility is patchy and risk is harder to define and harder to contain.
Hidden risks of rapid AI adoption
The security challenges tied to AI adoption are growing more defined with each deployment. One of the most urgent is Shadow AI – the unsanctioned use of AI tools, developer-led model deployments, and employee-procured applications that operate outside the view of IT and security teams. These instances are rarely malicious, but they often bypass the controls that ensure security and compliance. Without a view into these models, it becomes nearly impossible to understand what data is being accessed, where it’s going, or how it’s being used.
At the same time, AI is putting pressure on the network itself. Generative models and automated workflows produce a significant increase in internal traffic, much of it encrypted and lateral in nature. According to the 2025 Hybrid Cloud Security Survey, 1 in 3 organisations have seen traffic volumes double over the past two years due to AI workloads. This growth creates blind spots that traditional monitoring tools struggle to keep pace, especially in complex hybrid cloud environments where systems are constantly in motion.
Without clear guardrails, these systems can introduce risks, undermining data integrity and eroding customer trust. And without complete visibility across the hybrid cloud infrastructure they rely on, the most powerful AI capabilities can become a liability
An expanding threat landscape
In parallel, AI is also redefining the tactics and tools used by threat actors. Attackers are increasingly turning to AI to automate reconnaissance, personalise phishing campaigns, and amplify the scope and scale of ransomware attacks. And this shift is already being felt across industries. Research shows that more than half of organisations experienced a breach in the past year, and 58 percent reported a rise in AI-powered ransomware activity.
Security teams are also seeing a rise in targeted attempts against their own AI and large language models. These systems present new surfaces for exploitation, particularly when used without proper isolation or access controls. Threat actors are taking advantage of limited visibility in hybrid environments, hiding their activity in encrypted and lateral traffic where detection is weakest.
Traditional security tools were not designed for this level of speed or complexity. Many rely on siloed log data and struggle to adapt to evolving AI workloads. Without comprehensive insight into all data in motion across the environment, attackers can operate undetected for prolonged periods, increasing the potential for damage.
Using visibility to deploy with confidence
As AI reshapes enterprise infrastructure, the need for complete visibility is becoming a foundational and business-critical requirement. Security teams must be able to see how workloads interact, how all data flows across environments, and where new risks may emerge. This is especially important as AI systems introduce more automation, generate new traffic patterns, and rely on a growing web of inter-connected services.
Without that complete visibility, even well-governed deployments can carry hidden risks. Misconfigurations may go unnoticed, sensitive data may move through unexpected channels, and threat activity can blend into normal operations. A fragmented view of the environment makes it difficult to respond quickly or with confidence, especially when incidents involve AI models that span multiple platforms.
To address this, many organisations are turning to deep observability – an approach that extends visibility by integrated network-derived telemetry in the form of packets, flows, and metadata with existing data sources like metrics, events, logs, and traces. With a comprehensive view into all data in motion, it brings greater depth to monitoring and helps organizations to detect threats that traditional tools might otherwise miss. In the 2025 Hybrid Cloud Security Survey, 88 percent of security leaders identified deep observability as critical to securing AI deployments.
By adopting more integrated visibility strategies, organisations are gaining a clearer, realtime view across their hybrid cloud environments. Correlating infrastructure, application, and network data allows teams to detect issues earlier, respond faster, and build trust in how AI systems are managed. It also enables security to support innovation without introducing friction, ensuring AI can scale safely within the guardrails of operational control.
AI is moving fast, but risk moves even faster when left unchecked. Having a strong level of visibility gives organisations the clarity they need to protect what matters, while keeping pace with everything yet to come.
