Defense Department began testing AI surveillance system to safeguard critical military assets
A commercial AI surveillance solution which can be tailored to specific DoD needs- Security startup Scylla offers "proactive," AI-based security systems to safeguard perimeters around facilities and depots.
- Its Scylla AI systems are apparently good enough to protect US nuclear sites, as the Department of Defense (DoD) began testing them eight months ago at the Blue Grass Army Depot (BGAD) in Richmond, Kentucky.
- The systems help personnel find and identify intruders, weapons, or "abnormal behavior" in real-time.
- Scylla systems work with existing surveillance cameras and drones to monitor facilities, providing significant efficiency improvements in threat response by human personnel.
In BGAD tests performed by the Physical Security Enterprise and Analysis Group (PSEAG), Scylla has shown it can detect threats with accuracy rates beyond 96%.
- Depot Electronic Security Systems Manager Chris Willoughby said the system significantly lowers false alarms caused by "environmental" phenomena.
- Humans are still required to decide whether to respond to a threat.
The AI showed remarkable surveillance capabilities by identifying an armed individual climbing a water tower a mile away. Another example of the system's reliability was an alert sent to security personnel "within seconds" after the algorithm detected two potential armed intruders breaching a fence. The intruders were part of the BGAD staff, and Scylla immediately identified both via facial recognition.
While PSEAG is heavily involved in testing, evaluating, and even training Scylla's deep learning algorithms for BGAD, the Army has provided no specific details regarding how different the trained system is from the commercial software for obvious reasons. Deputy Assistant Secretary Walter is a fan of the AI, as it could be "transformative" to PSEAG's core mission: safeguarding the US strategic nuclear arsenal.
‘The Simpsons’ Did Multiple Crossovers With ‘Far Side’ Comics