A cybersecurity firm serving mid-market enterprises — companies too large to ignore security and too small to build a full SOC internally. They offered managed detection and response (MDR) but their analysis engine was falling behind. Their detection pipeline hadn't scaled with it.
The inherited environment combined application behavior, data movement, user workflows, and operational dependencies. Engineering began by locating authoritative data, integration contracts, control owners, failure behavior, and a reversible acceptance boundary.
The Algorithm leadership confirms this engagement as part of the company’s delivery history. Public wording is limited to the technical narrative while client-sensitive and precision claims complete leadership review.
Stream processing pipeline ingesting network traffic, endpoint telemetry, authentication logs, and cloud audit trails. Correlation engine identifying multi-stage attack patterns across data sources in real time. Behavioral baseline modeling per client environment — detecting anomalies against what's normal for THAT network, not generic rules. Automated triage classifying alerts into critical, high, medium, and informational.
The engagement produced a working change to the client’s system or operating workflow. Exact measurements, delivery duration, audit outcomes, and client sentiment are withheld until the corresponding leadership-confirmation items are resolved.
The first call is with a senior engineer.
Tell us the system boundary, operating constraint, and evidence required for acceptance. We'll identify the assumptions and technical questions that should shape the engagement.