AI security is broken at runtime: Most enterprises don’t realize it yet (www.techradar.com)

Recent insights from Fortanix highlight a critical vulnerability in AI security that many enterprises underestimate: runtime security. As AI integrations rapidly evolve across sectors such as customer support and fraud detection, traditional security measures—focused primarily on data at rest and in transit—fail to address the unique threats posed when AI models process data in real-time. At runtime, sensitive information, including model weights and user inputs, is exposed, particularly in environments that may not be fully secure or properly configured. This gap in security becomes increasingly significant as AI systems scale. The reliance on complex, shared infrastructures amplifies exposure risks during execution, while proprietary models and sensitive data drive the urgency to rethink trust in current security models. By moving security practices closer to where AI workloads run, including implementing methods like Confidential Computing, organizations can better safeguard their operations against potential vulnerabilities. Failing to adapt to these evolving threats may leave enterprises still relying on outdated trust assumptions, rendering their advanced AI systems vulnerable to exploitation.