Data is the most valuable asset for enterprises. It drives decision-making, productivity, and customer experience, but this makes it a prime target for attackers. For a long time, encryption, tokenization, masking, and data loss prevention (DLP) have helped enterprises to protect data, while newer approaches, such as data security posture management (DSPM), enhance visibility across cloud and software-as-a-service (SaaS) workloads. Different types of data security solutions exist because risks emerge at multiple entry points, requiring specialized controls. These solutions are often deployed at different times, making it difficult to maintain a consistent context and policy enforcement. Defining data security platforms (DSPs) through the lens of the data lifecycle helps resolve this.

•    How will strong governance and contextual intelligence at the point of data creation enable accurate and precise subsequent actions, such as anomalous behavior detection and protection enforcement?
•    Which capabilities showcase growth potential and can impact centralized policy & intelligence engines to ensure consistency and shared context across all controls?
•    What are the top companies redefining the growth trajectory of data security platforms?