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02/08/2026 11:59 am
TL;DR: Security data fragmentation lets one attacker appear as five unrelated events across cloud, SaaS, and collaboration tools, while enterprises process an average of 24,000 unique log sources and retain less than 15% of available telemetry, according to DataBahn and MITRE ATT&CK. Unified identity resolution and pre-ingestion routing now matter more than adding another AI layer.
NHIMG editorial — based on content published by DataBahn: Why are Legacy SIEMs a problem? The AI isn't broken. The data feeding it is
By the numbers:
- Those same SIEMs have detection coverage for just 21% of MITRE ATT&CK techniques.
Questions worth separating out
Q: How should security teams connect identities across cloud, SaaS, and endpoint data?
A: Security teams should resolve identities at ingestion, using a governed mapping between usernames, email addresses, account IDs, device IDs, and service handles.
Q: Why do fragmented logs make AI security tools less reliable?
A: AI systems depend on structured, correlated data.
Q: What breaks when identity resolution is left to the SIEM?
A: When identity resolution is left to the SIEM, schema drift, custom parsers, and inconsistent source fields create silent failures.
Practitioner guidance
- Implement verified identity resolution at ingestion Map usernames, email addresses, account IDs, device IDs, and SaaS handles to a single subject before data reaches detection systems.
- Route high-value events by enriched identity context Keep cloud access, admin actions, and cross-system identity transitions in the detection tier, while shifting low-value compliance noise to cheaper storage.
- Measure correlation quality, not just ingestion volume Track the percentage of alerts that can be tied back to one verified actor across cloud, SaaS, endpoint, and collaboration telemetry.
What's in the full article
DataBahn's full article covers the operational detail this post intentionally leaves for the source:
- How the unification layer handles cloud logs, SaaS activity, endpoint telemetry, and identity events without custom integration work.
- The practical routing logic for separating high-fidelity detection events from compliance logs before SIEM ingestion.
- Examples of how entity resolution maps jsmith, email addresses, and SaaS handles back to one verified identity.
- The article’s implementation questions on OCSF normalization, source onboarding, and routing flexibility for cost optimisation.
👉 Read DataBahn's analysis of identity stitching in AI-enabled SOC data →
Identity stitching in SIEMs: what it means for AI security tools?
Explore further
02/08/2026 1:06 pm
Identity stitching is now a control requirement, not a reporting convenience. The article shows that a SOC can ingest enormous volumes of telemetry and still fail to see one attacker’s path if identity data is inconsistent. That shifts the problem from analytics to governance, because correlation quality determines whether AI can support investigations at all. For practitioners, verified identity resolution belongs in the security architecture, not as an afterthought in the query layer.
A question worth separating out:
Q: How can SOC teams tell whether identity stitching is working?
A: SOC teams should test whether a single user or workload can be traced across cloud, SaaS, and collaboration systems within one investigation workflow. If that path still requires manual reconciliation, identity stitching is not working well enough. A reliable system produces continuity, not just more searchable data.
👉 Read our full editorial: Identity stitching is the real problem behind AI-powered SOC data
