Case Study - Building a Managed Detection & Response Practice on a Proprietary DRP Platform

The Gap Between Platform and Service

Having a detection platform and running a managed service are different problems. The client had built capable internal tooling for digital risk protection — detecting brand abuse, impersonation, phishing infrastructure, and fraudulent content across the internet. The technology worked. What they lacked was the operational layer that turned platform output into something a security team could rely on and pay for.

Selling MDR is selling a promise: our analysts, acting as an extension of yours, will handle this end-to-end. That promise requires a defined onboarding process, per-client platform calibration, analyst workflows that scale, and automation that removes manual overhead before it buries the team.

The client had the platform. They needed the practice.

What We Built

Customer Onboarding

Onboarding a new client determines the quality of everything that follows. We defined the process from scratch: asset inventory (domains, trademarks, executive profiles, products), threat profile assessment, monitoring scope agreement, and the rules of engagement that govern what the MDR team can act on unilaterally versus what requires client escalation.

The output was a client-specific configuration baseline and a signed rules-of-engagement document. No analyst touched a client's threat queue without both in place.

We built the workflow to be repeatable — templates, checklists, handover criteria — so time from contract signature to live monitoring coverage dropped from weeks to days.

Solution Fine-Tuning

A detection platform configured identically for every client is a noise machine. Brand protection signals vary by industry, geography, and threat actor behaviour. What's a false positive for one client is a confirmed attack vector for another.

We built the fine-tuning methodology: an initial calibration period with close analyst involvement, signal classification, and iterative threshold adjustment. Clients with high brand visibility in high-risk regions got tighter coverage and lower confirmation thresholds. Narrower threat surfaces got focused scope to prevent alert fatigue on both sides.

Fine-tuning wasn't a one-time activity. We embedded it into the quarterly review cycle — coverage drift, new asset additions, emerging threat patterns.

Analyst Workflows & Automation

Triage at scale requires automation to be viable. Analysts should be making judgement calls — qualification, escalation decisions, remediation strategy. They should not be copying incident data between systems, looking up registrar contacts, or formatting takedown notices by hand.

We mapped the full analyst workflow, identified every manual step, and automated the repeatable ones: threat qualification pipelines that pre-enrich detected incidents with domain registration data, hosting information, SSL certificate lineage, and prior incident history; takedown request generation for common abuse types; escalation routing based on severity and rules-of-engagement criteria; client notification templates.

The analysts who remained in the loop were making better decisions faster because the context was already assembled when the ticket landed.

Managed Service Delivery

The MDR team operated as an extension of each client's security function. Analysts handled takedowns, coordinated with registrars and hosting providers, escalated to client security teams with clear recommended actions, and closed the loop on every incident with documented outcomes.

We defined the service tiers: continuous monitoring, triage and qualification SLAs, managed takedown with client approval gates where rules of engagement required them, and escalation on incidents needing internal action — executive impersonation, credential exposure, active fraud campaigns.

The rules-of-engagement framework was the operational spine. It made the MDR team's authority explicit, gave clients confidence about what would happen without their involvement, and removed the ambiguity that slows incident response.

Results

The MDR practice launched with a delivery model the client could replicate across their customer base without rebuilding from scratch each time. Onboarding became a process. Fine-tuning became a discipline. The analyst team had the automation and clarity to deliver at scale.

What We Used