Security operations teams – especially small and mid-sized teams – are under pressure from every direction.
Limited budgets result in 24×7 coverage gaps, slow incident response, and tooling sprawl that internal teams can’t maintain. Alert fatigue and manual triage still consume a huge share of analyst time, which increases breach risk because investigations get delayed.
That’s why SOC discussions should be less about which tool collects the logs and more about how to operationalize detection and response: how it runs, how fast it works, and how much can be automated.
The two dominant operating models in that conversation are:
This guide breaks down:
A Security Operations Center (SOC) is an IT security function that organizations oversee and operate themselves, with some elements potentially supported by external partners. A SOC combines people, processes, and technology – typically detection tooling and a SIEM (Security Information & Event Management) to monitor, detect and respond to threats.
People
Process
Technology
Implications of operating your own SOC:
Managed Detection & Response (MDR) is an outsourced security service where key aspects of a SOC – such as alert monitoring, detection, alert triage, and initial incident response – are managed for you. MDR is a human + tooling service that you can think of as managed SOC-as-a-service. MDR services typically come with service level agreements (SLAs) around key performance indicators.
Implications of using an MDR:
|
Dimension |
SOC (Security Operations Center) |
MDR (Managed Detection & Response) |
|
Ownership |
Fully or mostly internal, operated by the organization |
Operated by external provider |
|
24×7 Coverage |
Expensive to build and hard to staff |
Built into the service |
|
Detection Platform Responsibility |
Customer selects, licenses, and operates SIEM, XDR and other detection tooling |
Provider manages ingestion / normalization, often integrating with your existing tools; may offer managed SIEM and/or detection tools |
|
Detection Platform Maintenance |
Customer engineers maintain SIEM/XDR content, integrations, and upgrades |
Provider maintains the MDR platform and detection content; customer still maintains protected assets and local tools |
|
Compliance Evidence |
Internal teams must collect and assemble evidence manually |
Best-in-class MDR platforms increasingly generate and package evidence automatically |
|
Best Fit Team Size* |
Typically 5+ dedicated SOC staff, sometimes 20-100+ in large enterprises |
Typically 0-5 dedicated security staff |
|
Cost Profile |
High upfront and ongoing cost (capital + headcount) |
Predictable subscription pricing; capital costs mostly absorbed by provider |
|
Response Time |
Varies based on internal expertise, staffing, and process maturity |
SLA-backed response, with time-to-respond and time-to-triage defined in the service agreement |
* Note on hybrid models:
Organizations with their own SOC often use MDR as a hybrid overlay. For example for after-hours coverage, endpoint-only MDR, or surge support – rather than as their primary operating model.
So far we’ve looked at team size and capabilities. Another useful lens is overall business size (employees), which often correlates with how many dedicated security staff you can realistically hire.
A self-run SOC tends to fit best when:
An MDR-first approach tends to fit when:
In between those extremes, hybrid models are common – for example, a lean internal function plus MDR for off-hours or specific domains like endpoint or cloud.
For MSSPs and security service providers, the MDR vs SOC conversation is not about “should we outsource?” but about “how do we deliver security services at scale?”
Many MSSPs run on a SOC-style stack that combines:
This approach works well when MSSPs have:
Many MSSP owners lean toward MDR-like platforms or services when:
In other words: MDR-style capabilities let MSSPs offer certain SOC outcomes without scaling engineering and analyst staff linearly.
AI doesn’t eliminate the need for human analysts, it changes what they spend their time on. AI improves efficiency for any SOC operating model – whether in-house SOC, MDR, or MSSP – but the economics are especially transformative for small teams and multi-tenant providers.
Modern AI, especially agentic AI systems, can:
“AI SOC” or “agentic alert processing” is already being used or trialed in many SOCs. These are scenarios where an AI agent does most of the repetitive triage, enrichment, and correlation work, while humans focus on investigations, decisions, and complex response.
Specifically, AI is being applied across key SOC functions:
AirMDR is an AI-native detection and response platform that you can consume in two ways:
Under the hood, it’s the same AI engine – designed for agentic alert processing and transparent evidence.
For enterprise SOC teams, AirMDR doesn’t replace your SOC – it multiplies it by delivering a platform that removes the manual work that causes alert fatigue.
For MSSPs, it provides a multi-tenant AI SOC backbone without requiring a large internal engineering team.
Successful security operations is not about choosing between operating your own SOC or outsourcing everything to MDR.
The real challenge lies in how fast alerts are investigated, orchestrated, and validated – for enterprise SOCs, small security teams, and rapidly scaling MSSPs. The impact of AI is especially dramatic where headcount is constrained or you’re serving many tenants.
With AI-driven systems in place:
The future of security operations belongs to agentic AI + expert human oversight, where: