It is 2028, and the United States Space Force’s early warning radar modernization is complete. Technical Sergeant Jack Nichols works at Buckley Space Force Base operating systems that detect and assess ballistic missile threats against the United States and Canada. Since arriving at the Colorado base, Nichols has experienced his share of false alarms. However, these are no ordinary false alarms; the system Nichols watches provides early warning that the United States is under ballistic missile attack. While these existential alerts would distress most, he maintains an “old school” validation protocol: He evaluates the warning against his sensor’s input settings and raw data output, resolving any concerns.
But today, the warning that flashed across his screen was different. Recent modernization efforts introduced next-generation sensors and machine learning–powered tools to manage the increased flow of information. These purported improvements made the raw data inaccessible to Tech Sgt. Nichols. The system had identified an incoming missile, but he couldn’t help but wonder: What if this was a mistake? What if the system had been hacked or had malfunctioned? And,…
