Why do traditional DSPM tools miss the real exposure path?

Why This Matters for Security Teams

Traditional DSPM can tell security teams where sensitive data sits, but it often misses the identity chain that decides who can reach it. That gap matters because exposure rarely comes from one obvious permission. It emerges through nested groups, inherited sharing, delegated tokens, service accounts, and stale third-party grants that outlive the business need. NHI Mgmt Group notes that only Ultimate Guide to NHIs — Why NHI Security Matters Now states only 5.7% of organisations have full visibility into their service accounts.

That visibility gap is why data-first findings can look reassuring while the real exposure path remains open. The practical issue is not just where a file or bucket exists, but which non-human identities can chain access through IAM relationships, automation, and application logic. When teams rely on object-centric reports alone, they miss the entitlement graph that actually drives breach paths. In practice, many security teams encounter the real exposure only after a token, vendor integration, or service account has already been abused, rather than through intentional discovery.

How It Works in Practice

The exposure path is usually an identity graph problem. A dataset may be protected at rest, yet still reachable because an NHI inherits permissions through a group, assumes a role in another account, or presents a long-lived token that never got revoked. Current guidance suggests that the useful control plane is not just data classification, but runtime visibility into who or what can act on the data. That means correlating DSPM findings with IAM, PAM, and workload identity telemetry.

Practitioners increasingly pair object-centric DSPM with identity-centric analysis using policy evaluation at request time. That can include:

• Mapping service accounts, API keys, and delegated tokens to the resources they can reach.
• Checking whether access is direct, inherited, or transitive through group nesting and role chaining.
• Flagging stale vendor access and shared links that remain valid after the business need ends.
• Rebuilding the path from identity to object so remediation targets the grant, not just the asset.

This approach aligns with the direction described in The 52 NHI Breaches Report, where identity abuse repeatedly appears before data exfiltration, and with the broader attack-chain framing in Anthropic’s first AI-orchestrated cyber espionage campaign report, which shows how autonomous tooling can accelerate discovery and abuse of weak access paths. These controls tend to break down in environments with fragmented IAM ownership, where SaaS sharing, cloud roles, and CI/CD secrets are governed by different teams and no single inventory reflects the real path.

Common Variations and Edge Cases

Tighter identity-path analysis often increases operational overhead, requiring organisations to balance clearer exposure visibility against platform complexity and access-review fatigue. That tradeoff becomes important when data is spread across SaaS, cloud, and internal systems, because the same object can be reachable through different identity models. Best practice is evolving here, and there is no universal standard for a single “real exposure path” calculation yet.

Some environments also create false confidence for DSPM because the data layer looks well controlled while the identity layer is highly permissive. Shared links, break-glass accounts, machine-to-machine delegation, and vendor-managed integrations can all bypass the assumptions behind object-centric scanning. The right question is not only “Where is the data?” but “Which identities can reach it, by what path, and under what conditions?” When those answers depend on manual spreadsheets or periodic reviews, the path usually becomes stale before the next scan runs. One useful check is whether the organisation can explain every high-risk access path from NHI to data object without relying on tribal knowledge.

Related resources from NHI Mgmt Group

• Why do traditional data classification tools fail on structured records?
• Why do traditional data classification tools fail at scale?
• Why do collaboration tools create such a large secrets risk?
• When does secret exposure become a broader identity risk?