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20/08/2026 5:34 pm
TL;DR: Shadow data is unmanaged information that sits outside approved systems, and Strac argues that this creates hidden security and compliance exposure across SaaS, cloud, Gen AI, and MCP workflows. The governance problem is not discovery alone but controlling where sensitive copies live, who can reach them, and how quickly they can be remediated.
NHIMG editorial — based on content published by Strac: Understanding Shadow Data: Risks and Solutions
By the numbers:
- 72% of organisations have experienced or suspect they have experienced a breach of non-human identities, with 46% confirmed and 26% suspected.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems, inappropriately sharing sensitive data, and revealing access credentials.
Questions worth separating out
Q: What breaks when employees use shadow SaaS for business data?
A: Shadow SaaS breaks governance because IT cannot reliably see the account, classify the data, or enforce permission limits.
Q: Why does shadow data create a compliance problem as well as a security problem?
A: Compliance depends on knowing where regulated data lives, who can access it, and when it should be deleted.
Q: How do security teams know whether DSPM is actually reducing shadow data risk?
A: Look for measurable closure, not just more findings.
Practitioner guidance
- Inventory unmanaged data copies across every major storage path Build a shadow data register covering SaaS attachments, personal storage, cloud backups, legacy exports, and AI workflow outputs.
- Bind data remediation to identity and access reviews Review which service accounts, API keys, and automation paths can still reach each sensitive copy.
- Use DSPM findings to drive enforcement, not just reporting Convert every high-risk discovery into a tracked remediation ticket with a named owner, due date, and closure criterion.
What's in the full article
Strac's full article covers the operational detail this post intentionally leaves for the source:
- How Strac frames discovery, redaction, masking, blocking, and deletion as distinct remediation actions for shadow data
- The article’s product-led walkthrough of continuous compliance across SaaS, cloud, Gen AI, and MCP environments
- Examples of how Strac positions DSPM alongside DLP and integration workflows for live monitoring
- The vendor’s own explanation of policy management and incident response planning for data exposure events
👉 Read Strac’s analysis of shadow data risks and remediation →
Shadow data across SaaS and AI workflows: what teams miss?
Explore further
20/08/2026 6:20 pm
Shadow data is a governance failure, not just a storage problem. The key issue is that sensitive copies often outlive the business process that created them, so ownership, retention, and access decisions drift apart. That creates a control gap between data classification and access governance. Practitioners should treat shadow data as a lifecycle problem that spans discovery, entitlement review, and deletion.
A question worth separating out:
Q: How should teams stop machine access from keeping shadow data exposed?
A: Teams should review every token, service account, and automation path that can still reach unmanaged copies, then revoke access before the data is left in place. This is especially important when AI workflows or integrations can continue using inherited permissions after the original use case ends. The objective is to close the access path, not only to find the file.
👉 Read our full editorial: Shadow data governance is failing across SaaS, cloud and AI
