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Wiz Blog | RSS feed

Meet Wiz for M365: Bringing SaaS into the Security Graph Bringing Security Visibility to Vercel with Wiz Axios NPM Distribution Compromised in Supply Chain Attack Tracking TeamPCP: Investigating Post-Compromise Attacks Seen in the Wild The Wiz Blue Agent, now Generally Available Beyond the Badge: What Achieving Microsoft’s Certified Software Designation Means for Your Cloud Security Introducing the Green Agent: AI-Powered Remediation for the Cloud Three’s a Crowd: TeamPCP trojanizes LiteLLM in Continuation of Campaign KICS GitHub Action Compromised: TeamPCP Strikes Again in Supply Chain Attack Introducing the Wiz Red Agent- AI-Powered Attacker Introducing Wiz AI Application Protection Platform (AI-APP) Introducing Wiz Agents & Workflows: Security at the Speed of AI AI Runtime Threat Detection: From Input to Real-World Impact Trivy Compromised: Everything You Need to Know about the Latest Supply Chain Attack It’s Official: Wiz Joins Google Understanding and Reducing AI Risk in Modern Applications Introducing Wiz Tenant Manager: Multi-Tenant Management for Federated Organizations The Agile FedRAMP Playbook, Part 4: Reactive Risk Management through Enriched Incident Response Wiz Achieves CPSTIC Certification in Spain Seeing AI Clearly: Building Visibility Across Modern AI Applications The Agile FedRAMP Playbook, Part 3: Preventative Risk Management by building Secure by Design Wiz Leads the 2026 Latio Application Security Report with awards in 4 categories Building an Agentic Cloud Security Ecosystem: A Reference Architecture with Wiz MCP and Infosys Cyber Next The Agile FedRAMP Playbook, Part 2: Proactive Risk Management with Continuous Monitoring Cloud-native Security for your Windows environment: Announcing the Wiz Runtime Sensor for Windows Would You Click ‘Accept’? Automatically detecting malicious Azure OAuth applications using LLMs Wiz Named a Leader in The Forrester Wave™: Cloud Native Application Protection Solutions, Q1 2026 From Detection to Remediation: It’s Time to Rethink AppSec Around Exploitability and Root Cause Fixes The Agile FedRAMP Playbook, Part 1: Why Risk is Your Best Starting Point Introducing AI Cyber Model Arena: A Real-World Benchmark for AI Agents in Cybersecurity
Introducing Shadow Data Discovery in Wiz | Wiz Blog
https://www.wiz.io/authors/snegha · 2026-04-14 · via Wiz Blog | RSS feed

AI hasn’t just changed how we build — it’s changed how much data we keep.

Across cloud environments, data is constantly created, duplicated, versioned, and retained. Storage scales easily, but ownership doesn’t. Over time, what accumulates isn’t just sensitive data that needs protection — it’s data that no longer serves a purpose.

Stale objects, duplicated files, over-retained versions. Shadow data. And while it may not always be sensitive, it still matters — because every unnecessary object increases both cost and your exposure surface.

Today, we’re introducing Shadow Data Detection in Wiz — a new capability that helps organizations identify and eliminate inefficiently managed data across their cloud environments, reducing both storage spend and unnecessary risk.

Introducing Shadow Data Detection

Understanding sensitive data remains the foundation of any strong data security program. Organizations need to know what data matters, where it lives, and who can access it.

Shadow data builds on that foundation.

Even with strong visibility into sensitive data, a large portion of the data footprint is often inefficiently managed — duplicated, stale, or simply unnecessary. And whether or not that data is sensitive, it still contributes to both cloud cost and attack surface.

With Shadow Data Detection, Wiz extends DSPM beyond identifying what data is sensitive to also understanding what data is inefficient, unused, or over-retained — so teams can take action.

Across early environments, we’ve already identified over 1 exabyte of data in cloud storage buckets across customers, with a significant portion classified as redundant or stale — translating directly into meaningful cost savings and a reduced exposure footprint. In Wiz’s own environment, this surfaced TBs of redundant data across storage buckets, reinforcing the opportunity to reduce both spend and unnecessary exposure.

The result is clear visibility into what to keep, what to optimize, and what to remove — with measurable impact on both cost and risk.

What Shadow Data Detection Looks Like in Practice

Shadow Data Detection works as part of the Wiz platform, using the same connectivity already in place across your cloud environments.

Wiz analyzes inventory reports generated directly by cloud providers to evaluate how data is stored and managed, without impacting performance.

From there, it identifies patterns like duplicated objects, excessive versioning, and stale data, surfacing them as Data Findings with clear, actionable context — using both built-in detection logic and the ability for teams to define custom rules based on their own data management policies.

These insights are brought together in a dedicated Shadow Data dashboard, providing a centralized view of inefficient data across your environment — including storage impact, potential cost savings, and areas of unnecessary exposure.

Centralized Shadow Data dashboard showing total savings potential, top findings, and overall data breakdown across the environment

From a high-level view, teams can quickly identify where the biggest opportunities exist and which environments or resources are driving the most inefficiency.

Visualizing shadow data distribution across datastores to quickly identify high-impact areas

Findings are organized and prioritized based on impact, giving teams a clear view into the most critical inefficiencies across their environment.

Shadow Data findings and rules, including built-in and custom detections across data sources

Teams can then drill into specific findings to understand exactly what is driving inefficiency and what action should be taken.

Detailed view of a finding with evidence, affected objects, and estimated cost impact to guide remediation

This turns data sprawl into something teams can measure, prioritize, and fix — where reducing data directly reduces both cost and exposure.

Turning Insight Into Action Across Teams

Shadow data isn’t owned by a single team, which is exactly why it becomes a problem.

Security teams want to reduce unnecessary exposure. Platform teams want to optimize efficiency and cost. Data teams want stronger governance and lifecycle control.

Shadow Data Detection brings these together with a shared, consistent view, so teams can align on where inefficiencies exist, what to prioritize, and how to take action.

Because this is part of the Wiz platform, these insights connect directly to broader context — including access, configuration, and risk — so teams understand not just where data exists, but how it contributes to real exposure.

Built on the Wiz Data and AI Security Platform

Shadow Data Detection builds on Wiz’s DSPM foundation, combining inventory-based analysis with data classification and platform-wide context.

This allows organizations to understand sensitive data and unnecessary data side by side, connect findings to real risk across the environment, and take action without introducing new tools or workflows.

It’s not just about reducing what you store — it’s about reducing what you have to secure.

Looking Ahead

Data growth isn’t slowing down, especially as cloud and AI accelerate how data is created and used. The challenge isn’t just visibility. It’s knowing what should stay, what should go, and what actually matters — for both cost and security.

Shadow Data Detection helps teams take that next step, turning excess data into an opportunity to reduce spend, minimize exposure, and operate with greater clarity.

And as Wiz continues to expand its data security capabilities, this is just the beginning.