Artificial intelligence (AI) may be opening up new opportunities and markets for businesses of all sizes, but for a disparate group of hackers, this has provided the opportunity to deceive machine learning (ML) systems through a process called data poisoning. And these attacks are being carried out unnoticed every day, say experts, and this is not only losing potential income for businesses, but it is also infecting machine learning systems that go on to reinfect those ML models that rely on user input for ongoing training. McKinsey puts a US$10 trillion–US$15 trillion value on the potential global impact of AI-ML technologies and says early leaders in the field are already seeing 250% increase in five-year total shareholder returns. But when McKinsey asked more than 1,000 executives about their digital transformation work, 72% of organisations surveyed said they have not successfully scaled. Even hackers just starting out on their dark arts find data poisoning attacks relatively easy to perform because creating "polluted" data can often be done without any great knowledge of the system to be influenced.

Government agencies collect a lot of data, and have access to even more of it in their archives. The trick has always been trying to tap into that store of information to improve decision-making, which is a major focus in government these days. The President's Management Agenda, for example, emphasizes the importance of data-driven decision-making to improve federal services. The volume of data that most agencies are working with is such that humans can't easily tap into it for help with that decision-making. And even if they can perform searches into that data, the process is slow.