How secure are your AI and machine learning projects?

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When enterprises adopt new technology, security is often on the back burner. It can seem more important to get new products or services to customers and internal users as quickly as possible and at the lowest cost. Good security can be slow and expensive.

Artificial intelligence (AI) and machine learning (ML) offer all the same opportunities for vulnerabilities and misconfigurations as earlier technological advances, but they also have unique risks. As enterprises embark on major AI-powered digital transformations, those risks may become greater. “It’s not a good area to rush in,” says Edward Raff, chief scientist at Booz Allen Hamilton.

AI and ML require more data, and more complex data, than other technologies. The algorithms developed by mathematicians and data scientists come out of research projects. “We’re only recently as a scientific community coming to understand that there are security issues with AI,” says Raff.

The volume and processing requirements mean that cloud platforms often handle the workloads, adding another level of complexity and vulnerability. It’s no surprise that cybersecurity is the most worrisome risk for AI adopters. According to a Deloitte survey released in July 2020, 62% of adopters see cybersecurity risks as a major or extreme concern, but only 39% said they are prepared to address those risks.

Compounding the problem is that cybersecurity is one of the top functions for which AI is being used. The more experienced organizations are with AI, the more concerned they are about cybersecurity risks, says Jeff Loucks, executive director of Deloitte’s Center for Technology, Media and Telecommunications.

In addition, enterprises, even the more experienced ones, are not following basic security practices, such as keeping a full inventory of all AI and ML projects or conducting audits and testing. “Companies aren’t doing a great job right now of implementing these,” says Loucks.

AI and ML data needs create risk

AI and ML systems require three sets of data:

  • Training data to build a predictive model
  • Testing data to assess how well the model works
  • Live transactional or operational data when the model is put to work

While live transactional or operational data is clearly a valuable corporate asset, it can be easy to overlook the pools of training and testing data that also contains sensitive information.

Many of the principles used to protect data in other systems can be applied to AI and ML projects, including anonymization, tokenization and encryption. The first step is to ask if the data is needed. It’s tempting, when preparing for AI and ML projects, to collect all the data possible and then see what can be done with it.

Focusing on business outcomes can help enterprises limit the data they collect to just what’s needed. “Data science teams can be very data hungry,” says John Abbatico, CTO at Othot, a company that analyzes student data for educational institutions. “We make it clear in dealing with student data that highly sensitive PII [personally identifiable information] is not required and should never be included in the data that is provided to our team.”


Credit: Google News

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