Security Service
Managed Detection & Response (MDR)
MDR combines continuous monitoring with analysis, prioritization, and response support. The goal is to detect meaningful incidents early and handle them in a structured way, based on high-quality signals from EDR, SIEM, Cloud, and Identity.
24/7 triage
Assessment and prioritization instead of raw alerts.
Clear escalation
Playbooks, contact chain, roles, and response model.
Measurable metrics
MTTD/MTTR, false positives, volume, and coverage.
MDR does not replace incident response for severe events. Confirmed compromises require forensics, containment, and recovery as a separate effort.
Quick overview
- Continuous operations, not a one-off project.
- Coverage depends on signal quality and log coverage.
- Response via playbooks with clear escalation.
- triaged events instead of alert floods
- clear escalation paths (who decides what?)
- continuous tuning to reduce false positives
- regular metrics and a detection improvement backlog
A good fit if …
- you do not have a 24/7 SOC.
- alert volume is high and prioritization is missing.
- compliance requires continuous monitoring.
- cloud and SaaS environments lack visibility.
- you need measurable improvements in MTTD/MTTR.
Not a fit if …
- logs/EDR are missing or low quality.
- there are no clear escalation paths or decision owners.
- MDR is expected to prevent all incidents.
MDR vs. SOC vs. Incident Response
external operations with 24/7 triage and escalation.
internal team with platform operations and higher fixed cost.
acute engagement for severe events, forensics-focused.
MDR for ongoing operations, IR for exceptions.
Typical use cases
- Multiple teams/sites without 24/7 coverage.
- Tool mix of EDR, SIEM, and cloud without unified prioritization.
- Audit or customer requirements for monitoring evidence.
- Cloud and SaaS workloads without end-to-end logging.
- Need for measurable improvements in MTTD/MTTR.
Process & methodology
Scope, data sources, roles, response model.
Triage, correlation, context, playbooks.
Reports, metrics, improvement backlog.
Scope & preparation
- Define protection scope (assets, identities, cloud accounts, regions).
- Select data sources and integrations (EDR, SIEM, M365, cloud logs).
- Clarify roles and escalation paths (RACI, contact chain, on-call).
- Agree on response authority (advisory only vs. active containment).
Execution
- Collect signals, normalize, and activate detection use cases.
- 24/7 triage, correlation, and enrichment.
- Confirmed cases are handled via playbooks and escalated.
- Continuous tuning to reduce false positives.
Deliverables
- Onboarding documentation with data sources and use-case coverage.
- Incident reports with timeline, indicators, and recommended actions.
- Regular metrics (alert volume, MTTD/MTTR, false positives).
- Backlog for detection improvements.
Provider selection criteria
- 24/7 coverage, SLAs, and escalation speed.
- Transparency: access to raw data, rules, and detection logic.
- Response model (advisory vs. active isolation/containment).
- Supported data sources and integration effort.
- Data residency, privacy, and log retention.
- Pricing model (per asset, per event, per GB) and scalability.
Next steps
- Inventory data sources and prioritize gaps.
- Decide on response model (advisory vs. active actions).
- Define pilot scope and 90-day success criteria.
- Assign internal owners for operations and escalation.