Data is the lifeblood of artificial intelligence. Those who produce, own, or control access to data are critical stakeholders in the present and future of AI. However, these data custodians face a paradox: They must protect their organization’s sensitive data, but in doing so, they act as a blocker to realizing the true value of that data in developing ML and AI models.
However, times are rapidly changing. As the first wave of AI hype begins to fade, organizations are awakening to the realization that real value lies in leveraging their proprietary data for use by developers in building new, innovative models. But the big question remains: How to capitalize on the value of the data without compromising on privacy, governance and security?
Challenges of the past
Traditionally, sharing data was the only means to harness its power for AI — with the attendant risks of privacy and compliance breaches. Organizations faced the dilemma of either centralizing data or providing direct access and relinquishing control, therefore opening themselves up to security breaches and diminishing the value of their data.
Today, however, there is a new way to leverage data without sharing it. By treating data as a product and governing what type of computations can be brought to it, data can be commercialized, and securely made available for use by others. Techniques such as federated learning and computational governance make this possible.
Data custodians can now retain control of proprietary data within a secure environment while making it available for machine learning applications. This not only enables growth and scalability for custodian organizations but also ensures compliance with the growing wave of AI and ML regulations, such as the EU AI Act’s stringent data privacy requirements.
This paradigm shift is ushering in a new era of innovation. Companies, once grappling with small, bespoke models trained on limited datasets, are now capitalizing on increasingly commoditized foundational models pre-trained on extensive publicly available datasets. This approach, with federated learning and computational governance, addresses the historical challenge of data scarcity, empowering companies to unlock the full potential of their proprietary datasets.
Applications across industries
By leveraging data for external AI use cases, enterprises secure a competitive edge in their markets. This not only contributes to individual business success but also propels AI towards tackling global challenges. Industries such as healthcare, financial services, retail, marketing and manufacturing are witnessing the impact of securely making data available for AI use cases such as tackling fraud, optimizing supply chains, reducing waste — and increasing productivity.
In the pharma and healthcare industry, for example, data custodians have an opportunity to unlock the value of sensitive data – contributing to enhanced drug discovery processes and more efficient clinical trials. Technologies like the Apheris Compute Gateway are facilitating collaboration among many of the top pharma companies and healthcare data providers, overcoming historical challenges in leveraging sensitive healthcare data.
However, industries dealing with sensitive data, such as healthcare, finance, or organizations in the public sector, face unique constraints. The extreme sensitivity of their data requires a nuanced approach – balancing the benefits of ML with the imperative to protect data integrity and privacy.
Unlocking value with confidence
As AI regulations tighten globally, data custodians in organizations need to ensure they remain in control of data — determining necessary privacy and security controls, and systematically defining who can use the data and for what purpose.
In this evolving landscape of AI and data collaboration, the relationship between data custodians and ML organizations emerges as a key element for unlocking the full potential of proprietary data. By maintaining control over proprietary data, custodians enable ML engineers to build and train models that not only comply with the tightening regulations — but also uphold the highest standards of governance and privacy.
Confidently navigating these challenges unlocks value for companies and enhances AI’s ability to address significant global challenges. Techniques such as computational governance allow data custodians to strike the delicate balance between enabling innovation and safeguarding sensitive information.
