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Machine learning (ML) is a key component of artificial intelligence (AI) and is becoming more widely used in the cloud. Machine learning creates the ability for a solution to learn and improve without the use of additional programming. Many of the CSPs provide ML tools. There is some concern and regulatory movement when ML makes decisions about individuals without the involvement of a person in the process.

The availability of large amounts of inexpensive data storage coupled with vast amounts of computing power increases the effectiveness of ML. A data warehouse, or even a data lake, can hold amounts of data that could not be easily approached before. ML tools can mine this data for answers to questions that could not be asked before because of the computing power required. This capability has the potential to transform how we use data and the answers we can extract from our data.

The security concern has to do with both the data and the processing. If all of your data is available in one large data lake, access to the data must be tightly controlled. If your data store is breached, all of your data is at risk. Controls to protect the data at rest and access to this data are crucial to make this capability safe for use.

The other concern is with how the data is used. More specifically, how will it impact the privacy of the individuals whose data is in the data store? Will questions be asked where the answers can be used to discriminate against groups of people with costly characteristics? Might insurance companies refuse to cover individuals when the health history of their entire family tree suggests they are an even greater risk than would be traditionally believed?

Governmental bodies and Non-Governmental Organizations (NGOs) are addressing these concerns to some degree. For example, Article 22 of the EU GDPR has a prohibition on automated decision-making, which often involves ML, when that decision is made without human intervention if the decision has a significant impact on the individual. For example, a decision on a mortgage loan could involve ML. The final loan decision cannot be made by the ML solution. A human must review the information and make the final decision.