The stakes are increasing for companies developing AI in highly regulated industries. In industries such as healthcare and finance, compliance is not just a legal obligation, but a crucial aspect of building trust and integrity between organizations and their customers.
As machine learning models require increasingly diverse data – often from multiple sources across organizations – the need for a compatible solution increases. As developers rush to create the most advanced machine learning models, data managers are looking for a way to make their data available to these developers – and in doing so, realizing its value.
An emerging solution is computational governance, which describes the ability to control, oversee, and track all aspects of computation on data. For companies with terabytes of valuable data, computational governance is a way to make data available for ML while ensuring governance, security and privacy. Although it is emerging, it can be part of unlocking the true potential of data for data owners.
Co-founder and CEO of Apheris.
Define your controls
Computational governance allows data administrators – the organizations that own the data – to set the required level of privacy and define access controls at the computational level. This determines who can perform which calculations on which of their data assets, and for what purpose. Essentially, only authorized computations that meet the custodian’s requirements can be performed on the data, ensuring compliance with privacy and AI regulations.
The result is that companies can monitor and track who is doing what with their data, while users of the data also retain the ability to update their models, as long as they comply with asset policies.
This is essential for several reasons. First, it helps organizations comply with regulations such as GDPR and HIPAA, which require organizations to protect the privacy and security of personal data. Computational governance helps organizations meet these requirements by ensuring that only authorized individuals have computational access to data, that data is used only for approved purposes, and that raw data is never shared directly.
Moreover, computational governance plays a crucial role in the development of ethical and responsible AI models. In healthcare, for example, this means that AI models can be trained solely on data for regulatory compliant purposes while protecting privacy.
Making data available
Data is the bloodline of modern organizations, but it is only as valuable as the insights that can be extracted from it.
Whenever data is moved, it is exposed to threats such as data theft and data interference. If it moves outside the environment by sharing it with another organization, the owner loses control over how their data is used. As a result, the data loses much of its value to the owner.
Federated learning is the way to train AI models without ever moving data from its secure location. This allows data custodians to make their data available to developers in a secure environment.
Protecting proprietary data as a valuable asset is critical for organizations of all sizes. This allows data managers to extract even more value (by commercializing or producing it). By not moving data, the custodian maintains full control, ensures compliance with data residency and sovereignty requirements, and preserves business value.
The ability to leave data where it is also supports compliance with regulations such as the GDPR, which sets out laws around the whereabouts of data, and the EU AI Act, which has strict privacy requirements.
Why aren’t companies doing this already?
It is likely that many companies do not use computational governance methods simply because they are not aware of the possibility of maintaining control over the data while algorithms are fed to the data. Consequently, their way of addressing regulatory issues is not to make data available and they choose to remain in silos. If change is to happen, a change in mentality is needed.
Compliant methods for leveraging customer data sometimes dilute the inherent value of their data, hindering its potential to drive AI advancements. As a result, many organizations do not meet compliance requirements, especially in Europe.
Centralization of data or new data sharing agreements may have enabled data collaboration to some extent, but these are often lengthy and costly and are unlikely to remain functional in the future given the pace of change in regulations and technological progress.
Companies are at a crossroads: do they prioritize compliance or innovation?
Taking the next step to tackle the biggest social problems
In a changing regulatory environment, being agile yet compliant is not just an aspiration, but a critical business imperative. Computational governance can serve as a catalyst for organizations to securely leverage their data assets to enable innovative, compliant, and trustworthy AI.
If companies can securely make their data available to ML and AI, they can truly differentiate themselves, helping them stay competitive and provide the data to develop products that can benefit society. By improving the quality of data available to developers, we’re moving from ChatGPT to a world where AI really makes a difference.
After months of hype around AI, a solution like computational governance could support data managers by making their data available to help advance practical solutions to problems that arise today, such as in medical research.
By producing your customers’ data in a compliant manner, you can stay at the forefront of innovation and responsibly push the boundaries of AI.
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