{"id":7881,"date":"2025-04-10T11:05:21","date_gmt":"2025-04-10T18:05:21","guid":{"rendered":"https:\/\/oosto.com\/?p=7881"},"modified":"2025-04-10T13:05:49","modified_gmt":"2025-04-10T20:05:49","slug":"podcast-beyond-myths","status":"publish","type":"post","link":"https:\/\/oosto.com\/podcast-beyond-myths\/","title":{"rendered":"Beyond the Myths: The Truth About Vision AI Security, Ethics, and Privacy"},"content":{"rendered":"

She didn\u2019t see him\u2014AI could have.<\/h2>\n

On a late hospital shift, an exhausted nurse looked up and locked eyes with someone she never expected to see\u2014a former patient with a history of aggression. He wasn\u2019t supposed to be there.
\nA restraining order was in place. But security had no idea.<\/p>\n

This isn\u2019t about surveillance\u2014it\u2019s about prevention. AI for good means identifying real threats before harm happens. “When you think about security, AI, biometrics, and behavior detection, a lot of red flags may go up,<\/em>” said Ben Thomas, host of Pro AV Today. A longtime technology analyst, Ben has seen how misinformation distorts public perception. “And a lot of times, those red flags go up because there’s a lot of miseducation out there.<\/em>”<\/p>\n

In our latest episode of Innovation Obsessed<\/a>, Ben Thomas and Shawn Fontenot, Head of Global Marketing at Oosto, tackle the biggest misconceptions about AI security. They explore how
\nmisinformation stalls progress and why AI, when done right, is a force for good\u2014protecting people before it\u2019s too late.<\/p>\n

Responsible AI Considerations for Security<\/h2>\n

Responsible AI narrows the focus to specific known threats.<\/p>\n

“We\u2019re not looking for everyone,” said Shawn. “In fact, when we start with a customer, we start with an empty database. We don\u2019t scrape data from the internet. We don\u2019t track people\u2019s movements. We don\u2019t even know who you are unless you\u2019re on a security watchlist\u2014like a known bad actor, a banned individual, or someone with a restraining order.<\/em>”<\/p>\n

With Oosto, customers populate and control watchlists and create rules to uncover safety and security threats. The system completely ignores individuals not on those watchlists.<\/p>\n

Real-World Example: AI in Highly Regulated Industries<\/h2>\n

Casinos, some of the most heavily regulated security environments in the world, rely on facial recognition for targeted threat detection and for identifying self-excluders: people who want help in breaking their addiction to gambling.<\/p>\n

“Casinos aren\u2019t just sending out a squad of people when an alert pops up,<\/em>” explained Shawn. “They go through multiple layers of verification before taking action. It\u2019s about identifying repeat offenders, fraudsters, and those banned from the premises\u2014not tracking guests.<\/em>”<\/p>\n

This model is used in airports, hospitals, and critical infrastructure where security matters most. Instead of relying on human guards to remember hundreds or thousands of faces\u2014a process prone to error\u2014AI helps ensure only pre-identified threats are flagged.<\/p>\n

Addressing Bias and Accuracy<\/h3>\n

AI is only as good as the data that built it, and bias arises when algorithms are trained on limited or unrepresentative datasets.<\/p>\n

“Some applications of facial recognition have given the industry a bad name,<\/em>” said Shawn. “But the truth is, most people we\u2019re looking for aren\u2019t standing still, looking at a <\/em>camera. They\u2019re in motion, in crowds, in bad lighting. Our AI is trained for those conditions\u2014not just perfect, static images.<\/em>”<\/p>\n

In this episode, Ben and Shawn discuss what makes ethical AI security different:<\/p>\n