Private AI for the Enterprise: Where Data Security Meets Innovation

Security continues to be a top concern for enterprises as artificial intelligence (AI) becomes an integral part of everyday business applications. To protect confidential data, enterprises seek private AI solutions, which allow enterprises to use AI without exposing sensitive information. 

Unlike most SaaS-based AI tools, private AI infrastructures and solutions provide enhanced data security and greater control over AI models. In this article, we’ll look at how enterprises can implement an infrastructure that embraces private AI, as well as common challenges and real-word use cases.

Understanding Private AI and Its Importance in Enterprise Applications

Private AI describes an AI infrastructure that is designed to provide control and sovereignty over personal and private data. Having the right infrastructure in place prevents exposure of sensitive data to third-party systems and public cloud platforms. This level of privacy is essential for highly-regulated industries like finance, healthcare and government where confidentiality is a key concern and sensitive information is constantly being processed. 

The need for private AI is amplified by regulatory requirements. 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 emphasize the importance of secure, accountable and compliant AI practices

SaaS-based AI solutions hosted on third-party platforms risk data exposure and bias from untrustworthy sources. Private AI solutions offer a secure, compliant alternative. Built and managed within an organization’s infrastructure, they ensure full control over data, reducing regulatory risks and exposure to fines. Private AI also allows for tailored customization of models, architecture and algorithms to meet specific compliance requirements.

5 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.

Real-World Use Cases of Private AI in Action

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. 

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. 

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.

Achieving a Privacy-First AI Approach

As AI becomes more deeply embedded in business functions, enterprises need AI with privacy and security built-in. With SUSE, your business can maximize the benefits of AI while ensuring data security, control and compliance. We will help your organization implement a privacy-first AI approach that drives innovation forward. 

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