The stakes are increasingly higher for companies developing AI in highly regulated industries. In sectors such as healthcare and finance, compliance is not only 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 different organizations), the need for a compatible solution increases. As developers rush to create the most sophisticated machine learning models, data custodians are looking for a means to make their data available to these developers and therefore realize its value.
An emerging solution is computational governance, which describes the ability to control, monitor and track all aspects of calculations on data. For companies with terabytes of valuable data, computational governance is a route to making data available for machine learning while ensuring governance, security, and privacy. Although it is incipient, it could be a component that unlocks the real potential of data for its owners.
Co-founder and CEO of Apheris.
Defining your controls
Computational governance allows data custodians (the organizations that own the data) to set the required level of privacy and define access controls at the computational level. This dictates who can run which calculations on which of your data assets and for what purpose. In essence, only authorized calculations that align with the custodian's requirements can be run on the data, ensuring compliance with privacy and artificial intelligence regulations.
The result is that companies can monitor and track who does what with their data, while maintaining the ability of data users 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 only used for approved purposes, and that raw data is never shared directly.
Additionally, computational governance plays a vital role in developing ethical and responsible AI models. For example, in the healthcare sector, it means that AI models can be trained solely on data for regulatory-compliant purposes while ensuring privacy protection.
Make data available
Data is the lifeblood of modern organizations, but it is only as valuable as the insights that can be extracted from it.
Every time data moves, it is exposed to threats such as data theft and data interference. If it is moved outside of its environment by being shared with another organization, the owner loses control of 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 the data ever moving from its secure location, allowing data custodians to make data available to developers in a secure environment.
Keeping proprietary data protected as a valuable asset is vitally important for organizations of all sizes. This allows data custodians to capture more value (either by marketing it or producing it). By not moving data, the custodian maintains full control, ensuring you meet data sovereignty and residency requirements, and preserving business value.
The ability to leave data where it resides also supports compliance with regulations such as the GDPR, which contains data residency laws, and the EU AI Law, which has strict privacy requirements.
Why don't companies do this already?
Many companies likely do not use computational governance methods simply because they are unaware of the option of retaining control of data while sending algorithms to the data. Consequently, their way of addressing regulatory concerns is to not make data available, thus choosing to remain in silos. For change to occur, a change in mentality is necessary.
Compliant methods for leveraging customer data sometimes dilute the inherent value of your data, hindering its potential to drive AI advancement. As a result, many organizations do not meet compliance requirements, especially in Europe.
Data centralization or new data sharing agreements may have enabled data collaboration to some extent, but these agreements are often lengthy and expensive and are unlikely to remain functional in the future, given the pace of regulatory changes and technological advances.
Companies are at a crossroads: do they prioritize compliance or innovation?
Take the next step to address society's biggest problems
In a changing regulatory environment, being agile while remaining compliant is not just an aspiration, but a fundamental business imperative. Computational governance can serve as a catalyst for organizations to securely leverage their data assets to enable innovative, compliant, and trusted AI.
If companies can make their data securely available to machine learning and artificial intelligence, they will truly be able to differentiate themselves, allowing them to remain competitive and provide the data to develop products that can benefit society. By improving the quality of data available to developers, we move from ChatGPT to a world where AI truly makes a difference.
After months of hype around AI, a solution like computational governance could help data custodians by making their data available to help advance real-world solutions to problems happening today, such as in medical research.
By producing your customer data in a compliant way, you can be at the forefront of innovation and push the boundaries of AI responsibly.
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