From the course: Applied AI for IT Operations (AIOps)
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Security and privacy best practices - Python Tutorial
From the course: Applied AI for IT Operations (AIOps)
Security and privacy best practices
- [Instructor] Security and privacy has become one of the biggest considerations for AI. There are significant concerns from employees and customers in using their private data for training models. There are new legal requirements for data privacy, including GDPR in Europe. There are industry standards like PCI and HIPAA that require data to be protected. There is also the risk of data theft. These requirements have led to strict security procedures that deny access to this information for data scientists. Data labelers need to look at this information to create accurate labels. How do we enable AI in this environment? Here are some recommendations. First, create a secure machine learning environment with restricted access. Deny access by default and only allow required personal to access data. Use sensitive data for machine learning only when it's absolutely required. Anonymize and obfuscate sensitive data when possible. This can be done by mapping actual names to pseudo names while…
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