Security is integrating with data science

Data gives companies a competitive edge. Data scientists leverage AI algorithms, made available on open source, to cut and paste AI models together. 

But AI models rely on quality data, scalable computing and reliable algorithms. The cloud has lifted computing constraints, but has allowed companies to modernize rapidly, sometimes leaving behind ethical considerations.

AI implementation in outpacing "clear regulatory and ethical consensus," according to Gartner, threatening privacy's current high stakes. 

"Algorithms and the handling of personal data will become more perceptive," Lenley Hensarling, chief strategy officer of Aerospike, told CIO Dive.  "At the same time, the handling of data will become more careful." 

Data processing, rather than data collection, is riskier for companies, according to Gartner. Deanonymization, an increase in data lakes, and various definitions of privacy all contribute to a more complex landscape in need of protection. 

"Regulators, like much of the public in general are becoming savvier about data, both personal and otherwise, and about its use," said Hensarling. "We are well into multiple generations of digital natives as full participants in the marketplace."