The hidden security gap in private AI: Lifecycle management

Most organizations still evaluate the security of an AI platform the way they would evaluate a brand-new car. They verify that it has the safety features they require (compliance certifications), compare its performance and power (benchmarks), and confirm its quality and capabilities through other users and industry professionals (proven deployments and analyst validation). Once the boxes are checked, they declare it secure. On the day it ships, it probably is. But attackers don’t operate on your deployment timeline. They operate in the months and years that follow, looking for vulnerabilit...