Private AI: Securing Innovation for the Future of Enterprise

Private AI is transforming how enterprises leverage artificial intelligence while maintaining strict data security and compliance. As AI becomes embedded in everyday business applications, organizations are increasingly prioritizing solutions that protect confidential data. Private AI enables enterprises to harness the power of AI without exposing sensitive information to third-party providers.

Unlike most SaaS-based AI tools, private AI infrastructures offer enhanced security, greater control over AI models, and compliance with industry regulations. In this article, we’ll explore how businesses can implement a private AI strategy, the key challenges involved, and real-world use cases that showcase its impact.

What is private AI?

Private AI refers to artificial intelligence systems and models that operate within an organization’s secure infrastructure, ensuring that sensitive data remains private and compliant with industry regulations. Unlike public AI services, which process data on third-party cloud platforms, private AI enables enterprises to leverage AI capabilities while maintaining full control over their data and models.

By deploying AI on-premises or within a secure, private cloud environment, organizations can enhance data privacy, mitigate security risks, and comply with regulations. Leading technology companies are advancing private AI solutions to help enterprises integrate AI-driven automation, analytics and decision-making while prioritizing security and governance.

Public vs private AI

AI solutions generally fall into public AI and private AI, each with distinct benefits depending on security, compliance, and control needs.

What is public AI?
Public AI refers to cloud-based AI services available to multiple users. These solutions are scalable and cost-effective but come with data privacy risks since information is processed on external servers.

What is private AI?
Private AI operates on-premises or in a private cloud, ensuring full control over data and compliance. It offers better security and customization but requires higher investment and dedicated resources.

Key Differences

Why private artificial intelligence is the better choice for enterprise applications

Private AI empowers enterprises with full control over their AI infrastructure, ensuring data security, compliance and operational efficiency. Unlike SaaS-based AI solutions that rely on third-party platforms, private AI keeps sensitive data within an organization’s own environment, reducing exposure risks and strengthening compliance with industry regulations.

Regulatory frameworks such as the EU AI Act and GDPR impose strict requirements on data privacy, with substantial penalties for non-compliance. Regulations such as the EU AI Act propose fines of up to €35 million or 7% of a company’s global turnover for non-compliance. GDPR has expanded to include AI, with fines reaching €20 million or 4% of global revenue. Frameworks like NIST’s AI risk management guidelines and the U.S. Executive Order on AI emphasizes the importance of secure, accountable and compliant AI practices.

By implementing private AI, enterprises can mitigate these risks while maintaining sovereignty over their AI models and datasets. This approach is particularly valuable in industries like healthcare, finance and government, where safeguarding confidential information is critical.

Beyond security and compliance, private AI offers flexibility and customization. Organizations can tailor AI models, algorithms, and infrastructure to meet specific business needs, ensuring optimal performance and alignment with strategic goals. It also enhances operational efficiency and scalability, making AI adoption more seamless for businesses looking to integrate intelligent solutions into their workflows. As more enterprises explore using AI in business, private AI is emerging as the preferred approach for balancing innovation with security and compliance.

Real-world use cases of private AI in Action

Private AI is transforming industries by enabling secure, efficient, and compliant AI-driven operations. From healthcare to finance and retail, businesses are leveraging AI while maintaining full control over their sensitive data. Below are key industry use cases demonstrating the value of private AI.

Healthcare

The healthcare field is starting to leverage AI to analyze patient’s medical histories. This analysis can then be used to inform diagnostics and medical research as well as predict health risks. It’s critical to use secure AI platforms in this scenario to safeguard protected health information (PHI) and comply with HIPAA. 

Finance

Financial institutions may use AI for fraud detection, pinpointing anomalies and concerning patterns. This analysis is based on customer data, which must not be exposed to external platforms. Running AI workloads on a secure platform makes it possible to process and analyze this data within the institution’s own infrastructure. 

Retail

In the retail space, e-commerce companies use AI to drive their personalized product recommendations engine. These recommendations are based on a shopper’s past purchase history and browsing behavior. To protect buyer privacy, choosing to deploy AI platforms on premises ensures that the business has sovereignty and control over the data.

Manufacturing

Manufacturing companies increasingly rely on AI to optimize processes, enhance quality control and develop new materials. AI-powered simulations can assist in designing advanced alloys, improving durability and efficiency in industrial applications. However, using public AI models for such proprietary research poses risks—sensitive intellectual property could inadvertently be exposed to competitors.

By deploying private AI within a secure infrastructure, manufacturers ensure that their R&D data remains protected while still benefiting from AI-driven innovation. This approach aligns with using AI in business to drive competitive advantages without compromising data security.

Call centers

AI-driven chatbots and virtual assistants enhance customer support by providing instant responses and analyzing sentiment in real time. However, call centers handle vast amounts of personal data, including call transcripts and customer histories. Private AI ensures that customer interactions are processed securely within an organization’s infrastructure, maintaining compliance with data protection laws while improving service efficiency.

How to implement enterprise private AI

When setting up private AI, enterprises must consider infrastructure, data governance and cost-effectiveness while maintaining control over AI models and sensitive information. One of the key decisions organizations face is whether to build their own private AI infrastructure or adopt a third-party solution.

Should you build or buy?

When implementing private AI, enterprises must decide between building a custom AI solution in-house or purchasing a pre-built private AI platform. Each approach has its advantages and challenges:

• Building a private AI solution allows full control over data, AI models, and infrastructure. Organizations can tailor the system to their specific compliance, security, and operational needs. However, this requires significant investment in AI expertise, hardware, and ongoing maintenance.
• Buying a private AI platform from a trusted provider accelerates deployment and ensures reliability. These solutions come with built-in security, compliance, and scalability features, making them ideal for enterprises that lack the resources to build from scratch. However, customization options may be limited.

There is also the option to build your own AI applications on a secure, trusted AI platform like SUSE AI. This accelerates your delivery of AI innovation, since you can focus your internal talent on developing custom AI use cases for your business, instead of building AI architectures from the ground up.

Key factors influencing the build vs. buy decision include data security, compliance mandates, cost, scalability and internal AI capabilities. Enterprises must evaluate their AI adoption strategy based on business goals and regulatory requirements.

Essential strategies for implementing private AI

While the methods of implementing private AI in the enterprise vary based on organizational needs, infrastructure and resources, these best practices are building blocks to success. 

1. Compliance with data privacy regulations

Implement an extensible platform that provides both sanitized supported AI components and container-based zero trust security.  Ensure that your platform can fingerprint data and shut down the system should PII data leak.  Create templates that can automate compliance with regulatory requirements. Use observability to monitor resources – including LLM observability and fine tuning.

These assessments can also inform risk mitigation strategies and help privacy law compliance (e.g., California Consumer Privacy Act, HIPAA, GDPR).

2. Choice of deployment options

Providing a choice in deploying the platform for AI workloads  allows enterprises to better maintain data security. The business can decide whether the platform should be hosted in the data center, in a private cloud or even in an air-gapped environment. Deployment options provide the organization with the ability to make the right choices for their business.

3. Data security and encryption

It’s essential to rely on a platform that provides the highest data security – from certifications to  robust encryption techniques to protect data at rest, during processing, and in transit during AI operations. Lean on end-to-end encryption to safeguard data from the minute it enters the system until it reaches its final destination. 

Likewise, leverage secure multi-party computation so your private AI workloads can process data without exposing it to other parties to ensure privacy in collaborative environments.

4. Role-based access control (RBAC) and AI governance

Role-based access control (RBAC) policies allow businesses to restrict access to sensitive data and AI workloads based on set governance policies. These policies ensure that only authorized individuals can access or modify certain datasets. Audit logs are useful for further enhancing security. They provide a clear record of all activities, such as who accessed the system and what changes were made.

5. Utilizing private cloud or hybrid cloud solutions

Consider the cloud environment you’ll use. Private clouds allow full control over sensitive data with the flexibility of cloud-based services. Hybrid cloud environments integrate on-premise infrastructure with cloud resources, which can allow for greater scalability.

Prepare for the future of private AI with SUSE

As enterprises increasingly integrate AI into their operations, private AI emerges as the best solution for ensuring data security, compliance and control. Organizations must adopt a strategic approach to AI implementation—considering infrastructure, governance and scalability—to maximize its benefits while mitigating risks.

By embracing private AI, businesses can enhance security, maintain compliance with evolving regulations, and optimize AI workloads within their own environments. As AI continues to evolve, companies that prioritize privacy-first AI strategies will be better positioned for long-term success.

SUSE enables enterprises to build and deploy secure, scalable private AI solutions that align with their business needs. With SUSE AI, organizations can confidently implement AI while maintaining full control over their data and infrastructure.

Learn more in our on-demand webinar: Intro to SUSE AI.

Private AI FAQs

How secure is private AI?

Private AI keeps data within an organization’s infrastructure, ensuring greater security, compliance and control. Unlike public AI, it reduces exposure to third parties and allows for strong encryption and governance policies to protect sensitive information.

Is private AI right for enterprises?

For businesses handling confidential or regulated data, private AI is ideal. It ensures data privacy, compliance (GDPR, HIPAA), and model customization, making it a better fit for industries like finance, healthcare, and government.

How does private AI differ from public AI?

Private AI operates in a secure, controlled environment, ensuring data sovereignty and compliance. Public AI relies on third-party platforms, posing security and regulatory risks.

For enterprises prioritizing privacy and compliance, private AI is the better choice.