AI Helping to Refine Intelligence Analysis
America’s national security organizations have begun applying AI to more quickly and effectively produce intelligence assessments.
Speaking at the GovernmentCIO Media & Research AI: National Security virtual event, Director of the National Security Agency (NSA) Research Directorate Mark Segal discussed how these new capacities are assisting intelligence analysts in better processing and sorting large quantities of often complex and disparate information.
In outlining the NSA’s research priorities, Segal noted that both AI and machine-learning capacities already showed promise for better organizing the large pools of variable data their analysts sort through in producing regular assessments.
“One of the challenges that we have found AI to be particularly useful for is looking through the sheer amount of data that’s created every day on this planet. Our analysts are looking at some of this data trying to understand it, and understand what its implications are for national security. The amount of data that we have to sort is going up pretty dramatically, but the number of people that we have who are actually looking at this data is pretty constant. So we’re constantly looking for tools and technologies to help our analysts more effectively go through huge piles of data,” Segal said.
This application of AI to analysis has the potential to expedite the delivery of actionable intelligence to policymakers as well, who are able to more quickly and conclusively come to decisions based on a more effective sorting of available information.
“We analyze information and then provide that analysis to policymakers. For example, let’s say we’re looking at a large pile of documents and trying to understand what the intentions of another country are by looking through that data quickly. We want to zoom in immediately on the most important parts of that data, and have our skilled analysts say, ‘We think this entity is doing a specific thing,’ and then leave that to the policymakers to determine how we might respond,” Segal said.
Segal cautioned that agency technologists need to start with a realistic understanding of AI and machine learning to make most effective use of these new capacities, and to see them in terms of how they can concretely refine internal processes and advance their organization’s key aims.
“One of the biggest risks about AI right now is that there’s this huge amount of hype surrounding it … AI is a tool just like any other tool. And the way that you use a tool is to figure out where it would be effective, and where it would actually help solve a problem in our research organization.
