About Author
Mark Duke
Mark Duke is CTO and co-founder of enhanced.io. He designed the company's SOC architecture and oversees all technical delivery.
enhanced.io, the channel-only Open XDR SOCaaS for MSPs
TL;DR
Legacy SIEM breaks down under the cost and complexity of scaling across many tenants.
Open XDR normalizes, enriches, and correlates data across EDR, firewalls, identity, and cloud sources in one pipeline.
Automated triage and human analyst judgment operate at different points in the workflow, not interchangeably.
Automation guardrails define what the system can act on and what requires a human decision.
The technical difference translates directly into a cost and speed difference at MSP scale.
enhanced.io is a channel-only Open XDR SOCaaS built exclusively for MSPs, with 400+ integrations across endpoint, network, cloud, identity and IoT/OT. That correlation pipeline is the technical foundation behind every driver in this post.
Where legacy SIEM breaks down for MSPs
Legacy SIEM licensing and infrastructure scale per tenant, which becomes expensive fast across a multi-tenant MSP book. Query performance also degrades as log volume grows across many client environments in one instance.
The pattern you will see is MSPs either under-provisioning to control cost, which reduces detection coverage, or over-provisioning, which erodes margin. Neither is sustainable at scale.
Log normalization, enrichment, and correlation
Stage | What happens |
|---|---|
Normalization | EDR, firewall, identity and cloud logs converted to a common schema |
Enrichment | Context added: threat intelligence, asset ownership, user role |
Correlation | Patterns identified across sources rather than within a single log type |
Triage | Automated filtering flags genuine anomalies for analyst review |
The pipeline starts with normalization, converting logs from EDR, firewalls, identity providers, and cloud platforms into a common schema. Enrichment adds context, threat intelligence, asset ownership, user role, before correlation looks for patterns across sources rather than within a single log type.
This is the same pipeline referenced in the multi-cloud monitoring approach, extended across on-premises and cloud sources simultaneously.
Where AI acts and where a human analyst acts
Automated correlation and initial triage handle volume: filtering noise, flagging genuine anomalies, and enriching alerts with context before a human ever sees them. A human analyst makes the judgment calls that carry business risk: containment decisions, client communication, and anything with ambiguous intent.
The line is not about capability. It is about accountability. Automated systems handle scale. Analysts handle consequence.
Automation guardrails
Guardrails define what an automated system can act on without human sign-off, typically low-risk, high-confidence actions like isolating a single confirmed-malicious endpoint. Anything with broader blast radius routes to a human analyst first.
The question to ask any provider is not "how much do you automate," but "where exactly is the guardrail, and who approved it."
Closing
This same correlation architecture is what makes zero trust achievable without a stack rebuild. It is worth understanding both together before evaluating a provider.
If you want to see the pipeline against your own log sources specifically, that is a technical scoping conversation, not a sales pitch. See the full platform approach at enhanced.io.
About enhanced.io
enhanced.io is a channel-only Open XDR SOCaaS built exclusively for MSPs, with 400+ integrations across endpoint, network, cloud, identity and IoT/OT. It sells only through MSP partners, never direct to end clients, and integrates with the EDR or MDR an MSP already runs rather than replacing it.
FAQ
How does Open XDR handle multi-tenant data separation?
Each tenant's data is logically separated within the correlation pipeline, so cross-tenant visibility never occurs while still allowing consistent tooling across the whole MSP book.
What happens to existing SIEM investments during a transition?
Does more automation mean less human oversight?
How is data normalized across different vendors' formats?
What is the typical latency between an event and an analyst seeing it?
Can this scale to hundreds of client tenants?