Building private AI: control, compliance and competitive edge
Using AI without losing control of sensitive data
by https://www.techradar.com/uk/author/martin-schirmer · TechRadarOpinion By Martin Schirmer published 6 April 2026
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AI has moved from experimentation to a business expectation. Boards want measurable returns. Teams want tools that save time. Customers expect smarter, faster experiences.
But as adoption accelerates, so do the risks. According to Stanford’s AI Index Report 2025, AI-related privacy and security incidents rose by 56.4% in a single year, with 233 reported cases in 2024 alone.
These ranged from data breaches to algorithmic failures that exposed sensitive information.
Article continues below Martin Schirmer
GVP NEMEA at Cloudera.
At the same time, data sovereignty is climbing the executive agenda, particularly across Europe.
Organizations are asking a more difficult question: how do we use AI to create value without losing control of our most sensitive data, our intellectual property, or our regulatory footing?
Enter private AI.
What Private AI Really Means
Private AI refers to the deployment of AI systems in a controlled environment where data privacy and security are maintained throughout the AI lifecycle. Unlike public AI models that process data in shared or external environments, private AI ensures all data remains within an organization's infrastructure, whether on-premises or in a private cloud.
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