Hybrid cloud infrastructure is under mounting pressure as AI-driven cyber threats continue to evolve, leaving security teams grappling with fragmented environments, outdated tools, and limited threat visibility.

That’s according to the latest research from deep observability platform Gigamon, which has found that more than nine in ten (91%) of security and IT leaders are being forced to make compromises in securing and managing their hybrid cloud infrastructure in the AI era.

Gigamon’s 2025 Hybrid Cloud Security Survey, based on a global poll of more than 1,000 security and IT leaders, reveals that these compromises can be traced back to a lack of clean, high-quality data to support secure AI workload deployment (46%), and limited visibility across cloud environments, particularly when it comes to tracking East-West lateral movement (47%).

Public cloud risks are also prompting security recalibration, with 70% of security leaders now viewing the public cloud as a greater risk than any other environment.

Once considered an acceptable risk in the rush to scale post-COVID operations, many organisations are now rethinking their public cloud strategies in the face of their growing exposure.

As a result, 70% of organisations are actively considering repatriating data from public to private cloud due to security concerns, while more than half (54%) are reluctant to use AI in public cloud environments, citing fears around intellectual property protection.

Meanwhile, AI’s role in escalating network complexity and accelerating risk is becoming increasingly evident, with Gigamon’s study finding near half (46%) of team leaders agree that managing AI-generated threats is now their top security priority. 

That pressure may be due in part to expanding data demands, with one in three organisations reporting their network data volumes have more than doubled in the past two years because of AI workloads, while 47% report a rise in attacks targeting their LLM deployments.

Added to that, more than half (58%) of security leaders say they’ve seen a surge in AI-powered ransomware, up from 41% last year, while breach rates have surged to 55%, a 17% year-on-year increase, driven largely by AI-enabled attackers.

With AI driving unprecedented traffic volumes, risk, and complexity, 89% of IT leaders said that deep observability is becoming fundamental to securing and managing their hybrid cloud infrastructure. 

Gigamon found that firms are shifting their priorities in response, looking to gain complete visibility into their environments, with 64% saying their number one focus for the next year will be achieving real-time threat monitoring delivered through having complete visibility into all data in motion.

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“Security teams are struggling to keep pace with the speed of AI adoption and the growing complexity and vulnerability of public cloud environments,” said Mark Jow, a technical evangelist at Gigamon.

“Deep observability addresses this challenge by combining MELT data with network-derived telemetry such as packets, flows, and metadata, delivering increased visibility and a more informed view of risk. 

“When we can clearly see what’s happening across AI systems and data flows, we can cut through the noise and manage risk more effectively. Deep observability helps us spot vulnerabilities early and put the right protections in place before issues arise.”