Source: SiliconANGLE
As organizations deploy AI factories—centralized platforms that continuously train, fine-tune, and serve models at scale—traditional perimeter-based security models fail because data flows in loops between training pipelines, vector databases, and inference endpoints rather than following linear input-output paths. The attack surface expands: prompt injection, model poisoning, and unauthorized fine-tuning on proprietary data now compete with classical infrastructure threats, forcing CISOs to architect security around data lineage and model provenance rather than network segmentation alone. OpenAI and Anthropic have already demonstrated the cost of getting this wrong through jailbreaks and data leaks; enterprises copying their architecture without building native security controls will face similar exposure at scale.