The Transportation Security Administration (TSA) is looking at how it can integrate artificial intelligence (AI) into its Geographic Information Infrastructure (GII), which is a shared sensitive but unclassified platform that provides critical geospatial capabilities across the Homeland Security Enterprise.
The Department of Homeland Security (DHS) GII serves as a centralized geospatial data ecosystem for Federal, state, local, tribal, and territorial partners. By leveraging the platform, TSA can share mission-critical intelligence across the Homeland Security Enterprise and inform operational decisions at key transportation nodes.
Speaking at the Google GEO for Gov conference in Washington, D.C., on June 17, Matt Gilkeson, TSA’s chief technology officer and chief data officer, outlined how TSA is exploring working AI into its geospatial platform to improve real-time threat response and coordination with commercial stakeholders.
“Embedding AI into the geospatial platform is where we see the opportunity to fully realize the value of those resources,” he said.
Gilkeson explained that DHS GII “is robust and has provided the ability to use and implement tools rapidly to establish a better, commonly operating picture across [DHS] commercial stakeholders.”
“Our approach is delivering the geospatial intelligence to understand the real-time, low-latency data to monitor transportation networks and respond to security threats in real time,” said Gilkeson. “We use that geospatial data to inform the airports and partners responsible for physical security, helping them mitigate risks … These events require direct coordination and interoperability.”
Gilkeson explained that TSA is actively exploring how AI can strengthen geospatial intelligence to support infrastructure protection and transportation preparedness. He also highlighted the agency’s efforts to test hybrid computing models that move cloud capabilities closer to the operational edge, enabling faster data processing and more responsive decision-making in the field.
“We’re looking to straddle [AI] and cloud at the edge,” he explained. “That way we can most effectively leverage the benefits of [GEOINT] data transit across the nation, while also executing our mission at the local level.”