Securing AI infrastructure is critical – here's how to do it (www.techradar.com)

A recent analysis emphasizes the urgent need to secure AI infrastructure as organizations become increasingly reliant on AI systems for operational functions. With predictions that by 2026, AI-generated cyber threats will pose real-time adaptive risks, the significance of fortifying defenses around AI technology has never been more critical. Modern AI infrastructure encompasses various components, including models, training frameworks, data pipelines, and deployment environments, each representing potential attack surfaces. As adversarial attacks and supply chain compromises rise, organizations must transition from traditional security measures to more comprehensive, proactive defense-in-depth strategies. Key threats identified include data poisoning, where attackers can manipulate training data to introduce vulnerabilities; model supply chain compromises that expose organizations to backdoored AI models; and adversarial attacks causing misclassification in decision-making settings. The implications of these risks are severe, potentially impacting critical infrastructure and eroding trust through misinformation. To combat these challenges, experts recommend measures such as model provenance tracking, data lineage validation, and employing zero-trust architectures for AI workloads. As AI technology continues to evolve, securing its infrastructure is essential for organizations to harness its benefits while safeguarding against catastrophic risks.