8 security risks overlooked in the rush to implement AI

Broader operational impacts

“These technical vulnerabilities, if left untested, do not exist in isolation,” Mindgard’s Garraghan says. “They manifest as broader organizational risks that span beyond the engineering domain. When viewed through the lens of operational impact, the consequences of insufficient AI security testing map directly to failures in safety, security, and business assurance.”

Sam Peters, chief product officer at compliance experts ISMS.online, sees widespread operational impacts from organziations’ tendency to overlook proper AI security vetting.

“When AI systems are rushed into production, we see recurring vulnerabilities across three key areas: model integrity (including poisoning and evasion attacks), data privacy (such as training data leakage or mishandled sensitive data), and governance gaps (from lack of transparency to poor access control),” he says.