Securing a stadium is no easy task. Ensuring the thousands of occupants are safe is crucial work, but traditional approaches are slow when thorough. Self-learning artificial intelligence (AI) could provide an answer.
Today’s stadiums are an ideal environment for AI. Machine learning tools need data: something stadiums have in excess. SoFi Stadium handled 53 petabytes of network traffic during the 2022 Super Bowl. With all this information, AI could provide the insight and speed these facilities need to stay safe.
Addressing Physical Risks
Protecting against physical dangers like riots and terrorism is the first and most important part of stadium security. Here are three ways self-learning AI can upgrade traditional approaches.
Improving Weapons Detection
One of the most helpful ways to use AI in stadium security is in weapons detection. Instead of funneling crowds through slow, potentially inaccurate metal detectors, stadiums can use AI to scan them as they walk in.
AI can analyze light signals similar to radar to detect guns or other weapons without people having to empty their pockets. Since AI can scan people faster than traditional machines, crowds also don’t need to stop when entering the stadium. People can walk in at their normal pace, and if there’s an issue, the AI will spot it and alert security staff.
AI weapons detection may also be more accurate. Metal detectors aren’t reliable since not every metal is magnetic, and some weapons aren’t metal. Since AI sensors rely on other criteria, they overcome this limitation.
Modeling Crowd Behavior
Machine learning algorithms can also help address potentially unruly crowds. Stadiums like Camp Nou, the home of FC Barcelona, have turned to AI to create and analyze digital twins of the facilities and their occupants.
These algorithms can use data from past events to model various potential scenarios, highlighting which are most likely in different conditions. The results help stadiums understand when a crowd might become unruly or dangerous. Security staff can then look for these early warning signs to address the situation before any real damage occurs.
As these algorithms gather more crowd data, they’ll refine their decision-making process. They can then offer more accurate predictions and guidance for when security staff needs to step in.
Enabling Ongoing Improvements
Similarly, self-learning AI can help stadiums embrace a spirit of ongoing security improvements. Over time, these facilities will have more data and real-world context about how various scenarios play out. AI can analyze these to recognize shortcomings and potential fixes.
AI models could recognize repeatedly slowed foot traffic in some areas and suggest changes to make emergency exits more efficient. Alternatively, they could analyze past security incidents to reveal what worked and what didn’t. As stadium staff get more of these insights, it’ll be easier for them to refine their processes.
Stadiums could even share these insights with each other. As more facilities embrace AI, the industry as a whole could become a safer place.
Improving Stadium Cybersecurity
As stadiums add more online services and incorporate more technology, their security must also address digital threats. Self-learning AI can help in this area, too. Here’s how.
Detecting Suspicious Activity
Machine learning network monitoring solutions help by learning what normal network behavior looks like. These tools analyze network activity across various scenarios to develop a baseline for how information normally travels across it. They can then recognize unusual patterns that could suggest an attempted cyberattack.
Many NFL stadiums already use network analytics to ensure connections run smoothly. With this data, they could easily create reliable monitoring solutions to recognize and isolate suspicious activity.
What constitutes suspicious activity in one network may not in another. Consequently, self-learning AI provides a crucial upgrade over a one-size-fits-all solution as it adapts to its specific situation. Stadiums can then ensure they tailor criteria according to their unique network setup.
Enabling Faster Responses
Self-learning AI also brings the critical advantage of speed. Machine learning models can spot and isolate suspicious activity far faster than a human, helping stop attacks before they cause any significant damage. This is particularly helpful as human security workers become harder and harder to find.
Recent reports reveal that 73% of IT leaders are facing difficulty hiring enough qualified staff. That’s a problem in an environment where networks must analyze activity from tens of thousands of devices. Since self-learning AI can automate much of this process and speed it up, those workforce constraints aren’t as impactful.
With AI monitoring, stadiums can meet higher cybersecurity needs with fewer people. The staff they do have can focus on meeting other needs faster from the time they save.
Self-Learning AI Has Promising Security Potential
Machine learning could revolutionize stadium safety from both physical and digital risks. As stadiums get bigger and these threats grow more concerning, these upgrades will become increasingly important.
As technology advances, AI’s possibilities will only grow. Some stadiums have already started to implement AI-based security systems, and as it develops, more will follow. Attending a game or concert will become much safer in the future.
