Success Starts With Secure Data: A Practical Guide for Enterprises
Thales Blog, Sunday, July 19th, 2026
Enterprises need unified, proactive data security frameworks to protect sensitive information across AI model training and inference.
AI initiatives carry significant risk, with 44% of organizations planning to spend $2 million or more on AI in 2026, yet many falter because inadequate data security exposes PII, intellectual property, and financial records.
Enterprises lack visibility into data distributed across cloud platforms and applications, and unstructured data makes up 90% of enterprise information, complicating governance as AI generates new volumes rapidly.
Fragmented tooling compounds the problem, with organizations averaging seven data protection tools and five for key management, leaving gaps AI systems can exploit.