Wiz has launched Shadow Data Detection, a new capability within its Data Security Posture Management (DSPM) platform designed to identify redundant, stale, and inefficiently managed data across cloud environments.
The feature extends Wiz’s existing data security tooling by analyzing storage inventory data from major cloud providers, enabling organizations to reduce both storage costs and their effective attack surface without deploying additional infrastructure.
The release is positioned squarely within the cost and exposure optimization layer of cloud security. While DSPM has traditionally focused on identifying sensitive data and access risk, Shadow Data Detection addresses a parallel operational issue: the unchecked growth of non-sensitive but unnecessary data. This includes duplicated objects, excessive versioning, and over-retained files – datasets that increase storage spend and expand the surface area for potential compromise.
Wiz reports that early deployments across customer environments have identified over one exabyte of cloud storage data, with a significant proportion classified as redundant or stale. In its own environment, the platform surfaced terabytes of unnecessary data across storage buckets. These findings translate directly into quantifiable cost savings while simultaneously reducing exposure pathways tied to misconfigured or forgotten data assets.
The capability operates by ingesting inventory reports generated natively by cloud providers, avoiding performance overhead or intrusive scanning. It applies both built-in detection logic and custom policy rules to flag inefficient data patterns, surfacing them as prioritized findings within the Wiz platform. These insights are consolidated into a dedicated dashboard that quantifies storage impact, highlights cost-saving opportunities, and maps inefficiencies to broader security context, including access permissions and configuration risk.
The operational significance lies in cross-functional alignment. Shadow data typically spans security, platform, and data engineering teams, with no single owner. By providing a unified view of inefficiency tied to both cost and risk, Wiz enables shared prioritization across these functions. Security teams gain reduced exposure, platform teams improve resource efficiency, and data teams strengthen lifecycle governance—without introducing new tooling silos.
As AI and cloud-native workloads accelerate data generation, the challenge is no longer limited to identifying sensitive information but extends to controlling total data volume and relevance. Shadow Data Detection underscores a broader enterprise requirement: reducing the amount of data stored is increasingly equivalent to reducing the amount of data that must be secured.