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harmful and erroneous outputs.\u003C/span>\u003C/li>\u003C/ul>\u003C/li>\u003Cli>\u003Cspan style=\"white-space:pre-wrap;\">Data governance – AI systems should have proper data protocols or governance in place throughout the AI life cycle.\u003C/span>\u003Cul>\u003Cli>\u003Cspan style=\"white-space:pre-wrap;\">Protocols to ensure that only qualified and competent employees can have access to data should be implemented. \u003C/span>\u003C/li>\u003Cli>\u003Cspan style=\"white-space:pre-wrap;\">AI systems should have adequate and regular privacy and/or protection impact assessments. \u003C/span>\u003C/li>\u003Cli>\u003Cspan style=\"white-space:pre-wrap;\">Implement data management protocols to ensure security when using classified data. \u003C/span>\u003C/li>\u003Cli>\u003Cspan style=\"white-space:pre-wrap;\">Policies should be made regarding how long data is retained and how it will be securely disposed of when it is no longer 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