IDC Guest Post (Sponsored by Broadcom)
According to the 2026 IDC Cloud FutureScape, by 2028, 40% of all large enterprises will adopt private clouds for their AI workloads to meet stringent data privacy requirements and mitigate risks of sensitive information leakage to public large language models (LLMs).
This shift will enable enterprises to maintain complete control over data governance, ensuring proprietary information, customer data, and intellectual property remain within secured environments. Organizations will deploy dedicated AI infrastructure in their private datacenters or colocation facilities, running private or open-weight AI models. This approach addresses regulatory compliance, prevents competitive intelligence exposure, and provides full audit trails while maintaining sovereignty over AI training and inference processes.
Geopolitical uncertainties will accelerate the adoption of private cloud, as 60% of organizations with digital sovereignty requirements will be migrating sensitive workloads to new cloud environments to increase autonomy and reduce risk.
Impact on Information Technology
IT must embrace new levels of infrastructure complexity in building and managing private, dedicated AI infrastructure including GPU clusters, model registries, and data pipelines to support retrieval-augmented generation (RAG) and fine tuning. IT must expand security responsibilities as it secures the entire AI stack, from model weights to training data, implementing zero-trust architectures for AI workloads running in the private cloud. Holistically, this shift toward private AI will lead IT to begin moving from a "public cloud first" to a "control first" focus when planning for AI adoption.
Impact on Business
Due to increased confidence in AI adoption, business units will accelerate AI initiatives knowing sensitive customer data, trade secrets, and proprietary processes remain fully protected in the private cloud. While private AI infrastructure requires significant upfront investment, organizations gain predictable costs, avoid vendor lock-in, and eliminate growing transaction fees to deliver better ROI for the business.
Guidance
Establish a data classification framework where tiers for data sensitivity (public, internal, confidential, restricted) are defined. Map which AI use cases require low-latency intelligence via private infrastructure versus high-latency, public LLMs. Design and build a hybrid AI infrastructure that seamlessly routes workloads between private and public environments based on data sensitivity. Start with high-risk use cases that prioritize private cloud deployment for workloads involving PII, financial data, or IP-sensitive processes to demonstrate compliance value.
Conclusion
To avoid the exfiltration of corporate data to a public LLM running in another organization’s cloud, it’s important for IT to run AI workloads privately on the company's own infrastructure. Utilize pre-trained, open-weight, small and large language models from trusted sources in the organization's datacenter or colocation facility. Fine-tune those models with corporate data in the company's private cloud to become an invaluable resource for employees.