OCtober 6, 2025
by Praveen Cherukuri, chief technologist, AI-driven digital transformations, Paul Hewitt, senior director and global practice lead, Data & AI and Ryan Nowak, Private AI offering lead
Private AI represents a pivotal shift for organizations seeking to harness artificial intelligence without sacrificing data sovereignty or security. As enterprise leaders navigate this complex landscape, understanding specific use cases and their infrastructure requirements becomes essential for making informed deployment decisions.
The choice between on-premises, cloud or hybrid private AI deployments involves weighing critical factors: performance demands, regulatory compliance, scalability needs, integration complexity and total cost of ownership. Each use case presents unique challenges that require tailored infrastructure approaches to maximize value while minimizing risk.
Let's examine five compelling Private AI scenarios and explore how different infrastructure strategies address their specific requirements.
#1. Global enterprise: GDPR compliance and data residency
Multinational corporations with European customers face complex regulatory landscapes requiring sophisticated data governance approaches. GDPR compliance extends beyond basic privacy protections to encompass data transfer restrictions and regional processing requirements.
Requirement: Adherence to compliance frameworks
Data residency assurance becomes paramount, requiring regional tenancy solutions that guarantee European customer data remains within EU boundaries. This involves not just storage location, but also processing location (AI inference and model training must occur within approved jurisdictions).
Federated learning emerges as a powerful approach, enabling global model improvement while maintaining local compliance. This technique allows organizations to train AI models across distributed datasets without centralizing sensitive information.
Infrastructure strategy: Sovereign and hybrid ecosystems
Sovereign cloud AI solutions excel in this scenario, offering regional deployment capabilities that automatically handle data residency requirements. Major cloud providers offer EU-specific regions with GDPR compliance certifications, enabling organizations to deploy private AI services while maintaining regulatory adherence.
Hybrid architectures also prove valuable, allowing sensitive European data processing to occur in EU-based infrastructure while leveraging global cloud capabilities for non-regulated operations. This approach strikes a balance between compliance requirements and operational flexibility.
#2. Manufacturing: Real-time quality control with computer vision
Manufacturing environments require millisecond-precision AI responses, particularly for production line quality control systems that utilize computer vision cameras. These systems detect defects, monitor assembly processes and ensure product consistency at industrial speeds.
Requirement: Specialized video processing
Frame rate requirements typically range from 30 to 60 FPS for standard applications, with high-speed lines requiring higher frame rates. This translates to substantial processing demands; a single production line might generate several terabytes of visual data daily. Object detection models require different computational resources than anomaly detection systems, often demanding sophisticated neural networks.
Network bandwidth becomes critical when multiple camera feeds operate simultaneously. A typical setup might involve 20-50 cameras per production line, each streaming high-resolution data to central processing units.
Infrastructure strategy: On-prem and on the edge
On-premises deployment often proves optimal here. A few high-performance GPUs or specialized edge accelerators can effectively handle localized workloads. For example, NVIDIA's industrial computing platforms can process multiple camera streams while maintaining the low latency required for real-time decision-making.
The manufacturing environment also benefits from edge computing architectures that process data locally, reducing latency and network dependency. This approach ensures continuous operation even during network disruptions, a critical consideration for 24/7 manufacturing facilities.
#3. Healthcare: HIPAA compliance and patient data protection
Healthcare providers must navigate stringent regulatory requirements, including HIPAA, HITRUST and ISO 27001 standards, while implementing AI solutions to enhance patient care, diagnostic support and operational efficiency.
Requirement: Security and compliance
Healthcare AI deployments require comprehensive security frameworks that encompasses encryption at rest and in transit, detailed audit logging and granular access controls. These systems must demonstrate compliance through regular assessments and certifications.
Data minimization becomes crucial. AI systems should process only the minimum patient data necessary for specific clinical purposes. This principle affects both model design and infrastructure architecture.
Infrastructure strategy: On-prem, sovereign cloud and hybrid
Both on-premises and sovereign cloud solutions can meet healthcare requirements, but configuration complexity varies significantly. On-premises deployments offer maximum control over security posture but require substantial internal expertise to maintain compliance.
Sovereign cloud implementations can accelerate deployment while maintaining security standards, provided organizations properly configure encryption, audit logging and access controls. The key insight: Compliance isn't automatic. It requires deliberate architectural choices and ongoing management.
Healthcare organizations often benefit from hybrid approaches, keeping the most sensitive patient data on-premises while leveraging cloud capabilities for research, analytics and non-clinical operations.
#4. Retail: Seasonal scalability for customer personalization
Retail organizations face dramatic seasonal variations in computing demands, particularly during holiday periods when personalization engines must handle traffic spikes while delivering real-time product recommendations and automating customer service.
Requirement: Dynamic scalability
Peak shopping periods can generate 10 times normal traffic volume, requiring infrastructure that scales dynamically without performance degradation. Customer personalization systems must maintain response times under 100 milliseconds, even during peak loads.
Data governance becomes complex as retailers collect vast amounts of customer interaction data across multiple channels (web, mobile, in-store and social media). This data fuels personalization engines but requires careful management to respect customer privacy preferences.
Infrastructure strategy: Sovereign cloud and hybrid
Sovereign cloud-based private AI solutions shine in retail scenarios, offering elastic scalability that automatically adjusts to demand fluctuations. Pay-as-you-go pricing models align costs with actual usage, making seasonal spikes economically manageable.
Governance controls must include robust consent management systems, customer data minimization protocols and responsible AI practices that prevent discriminatory or biased recommendations. These requirements favor cloud platforms with built-in governance tools and compliance frameworks.
Hybrid architectures allow retailers to maintain core customer data on-premises while leveraging cloud elasticity for seasonal workloads, providing both security and flexibility.
#5. Government and defense: Regulated data and secure supply chains
Government and defense organizations require AI capabilities that maintain complete data sovereignty while meeting strict security supply chain requirements. These implementations must operate within national boundaries while delivering advanced AI capabilities.
Requirement: Strong defense
Air-gapped on-premises solutions offer maximum security isolation but they limit integration capabilities and scalability. In-country sovereign clouds offer a middle ground, providing cloud benefits while maintaining national data sovereignty.
Supply chain assurance becomes critical. Hardware and software components must meet strict procurement standards and undergo security validation. This requirement often necessitates collaboration with specialized vendors who are familiar with government security protocols.
Infrastructure strategy: On-prem and sovereign clouds
On-premises deployments typically dominate this sector, offering complete control over data location and processing. However, sovereign cloud solutions are emerging, providing cloud-like capabilities within national boundaries and under government oversight.
Accreditation by local authorities adds another layer of complexity, requiring infrastructure solutions that meet specific national security standards. This often involves lengthy certification processes and ongoing compliance monitoring.
Defense applications frequently require proprietary technologies and specialized vendor relationships, making standard cloud solutions less suitable. Custom infrastructure designed specifically for government requirements often proves most effective.
Strategic infrastructure planning for private AI success
Successful private AI implementation requires aligning infrastructure choices with specific business requirements and constraints. The key to right-sizing private AI infrastructure lies in understanding your organization's unique requirements across performance, compliance, scalability, integration and cost dimensions. Start with pilot projects that demonstrate value, then scale infrastructure investments based on proven business impact and operational demands.
As AI capabilities continue to evolve rapidly, infrastructure strategies must remain flexible enough to accommodate new technologies while being robust enough to meet current business requirements. The organizations that master this balance will unlock AI's transformative potential while maintaining the security, compliance and sovereignty their industries demand.
The choice between on-premises, sovereign cloud and hybrid private AI deployments involves several key trade-offs:
While private AI offers compelling advantages for sensitive applications, public AI models remain practical for specific scenarios:
Cost efficiency often drives these decisions, particularly for organizations exploring AI capabilities before committing to private infrastructure investments.
Praveen Cherukuri is a chief technologist at DXC, leading AI-driven digital transformations for global enterprises. With deep expertise in scaling systems, cloud optimization, and AI strategy, he helps organizations accelerate growth and enhance efficiency. Passionate about innovation, he creates competitive advantages for DXC clients across industries.
Ryan Nowak is the Private AI Offering Lead at DXC Technology, bringing over two decades of experience in product development, SaaS strategy and agile leadership. Previously holding senior roles at Centersquare, ONE Discovery, and Brainspace, he has led global teams in product innovation and strategy execution. Ryan specializes in private AI, product road mapping, and scalable enterprise solutions.
