Seven Industries, One Problem:
How Data & AI Security Is Being Rewritten Across the Enterprise

Across industries like financial services, healthcare, telecom, retail, energy, and manufacturing, organizations face the same issue. Traditional perimeter-based security can’t keep up with modern data sprawl and AI adoption. Data and AI risks are converging and legacy infrastructure security is no longer sufficient.

AI systems access vast, distributed datasets across multicloud, SaaS, and on-prem environments. Traditional tools protect networks and endpoints but lack visibility into where sensitive data lives, who can access it, and how it flows into AI models.

Enterprises struggle with limited data visibility i.e 81%, ungoverned Generative AI use 68%, shadow data, overprovisioned access, and regulatory gaps. These issues increase breach risk and undermine safe AI adoption.

A data-centric model focuses on protecting the data itself, discovering, classifying, and governing it wherever it resides. This enables context-aware access controls, proactive risk detection, and secure AI enablement across the enterprise.

Data Security Posture Management (DSPM) provides continuous discovery, contextual intelligence, risk correlation, access governance, and automated remediation, forming the foundation for secure AI adoption at scale.