Not All Detection Is Equal: How Todyl's Detection & Analysis Engine Compares to Other MDR Approaches

Over the past three posts, we have laid out why traditional detection is losing ground to modern attacks, introduced Todyl's Detection & Analysis Engine as a multi-tier, AI-powered alternative, and walked through how the engine addresses the specific attack types it was built to catch. This final post closes the loop by looking at how Todyl's approach compares to the other MDR offerings security teams and MSPs are evaluating today.

The short version: not all detection is equal, and the differences are architectural.

Why MDR Comparison Matters: Detection Architecture Is the Real Differentiator

Most MDR offerings on the market check the same boxes on a feature list:  

• A 24x7 SOC
• Some form of identity and endpoint coverage
• AI somewhere in the stack

The labels look similar enough on a comparison sheet that buyers often default to brand familiarity, price, or whichever vendor their MSP already sells.

The problem is that the labels obscure what actually matters. The differences between MDR offerings show up in the architecture: where data is collected, how much context is preserved, how investigations are conducted, and how much of the work depends on human analysts versus automated, multi-tier analysis. Those architectural choices determine whether an MDR catches advanced attacks like BECs, SSL-VPN compromises, and ransomware, or misses them.

Two of the most common MDR alternatives Todyl is evaluated against, Huntress and Blackpoint Cyber, illustrate the gap clearly.

How Todyl’s Detection & Analysis Engine Compares to Huntress

Huntress markets Threat Response backed by a 24x7 AI-Assisted SOC, along with Managed ITDR for investigating identity threats. The capability labels are familiar. The architectural reality is where the gap shows up.

Where Huntress falls short

• Drops log data at the point of collection: Huntress drops log data during collection, with no customer control, for performance reasons. This leads to incomplete forensic data, missed detections, and limits the ability to fulfill compliance-grade log retention requirements. When telemetry is dropped before evaluation, the supporting events that would have provided context for an investigation are gone before anyone knew they needed them. ‍
• SOC access is reactive, not proactive, and lacks a named resource: Huntress requires partners to request help via email in response to specific incidents. There is no proactive outreach, and partners do not receive a named detection and response resource until reaching significant scale. That reactive model creates delays exactly when speed matters most.

How Todyl is different

The Detection & Analysis Engine is built around full context aggregation across identity, endpoint, network, and cloud, not aggressive source-side filtering. AI-powered triage happens automatically before a human analyst is involved, so confirmed threats reach the 24x7 MXDR team faster and with the supporting context already attached. Human expertise is the escalation point, not the first line of correlation. And every Todyl MXDR customer receives a named Detection and Response Account Manager, with the team available 24x7 via preferred communication channels and proactive outreach during a potential incident.

How Todyl’s Detection & Analysis Engine Compares to Blackpoint Cyber

Blackpoint Cyber positions itself around contextual intelligence, patented detection logic, and AI-enhanced alerts feeding a 24x7 SOC. Strong on the surface. Weaker once you look at the architecture underneath.

Where Blackpoint falls short

• SIEM is positioned as a compliance and auditing tool, not as something for threat detection or threat hunting: Blackpoint's log management product is built for compliance with limited support for log sources and no advanced anomaly detection capabilities. Blackpoint actively opposes the role of SIEM in a modern security stack. In their own words, "SIEM is a bad word at Blackpoint." That limits the depth of advanced detection and analysis available within the platform by design.  ‍
• Detection and response is heavily dependent on human analysts, which limits how quickly the platform can surface and respond to advanced threats: Todyl combines human expertise with ML-driven SOC analysis to reduce mean time to detect and respond.  ‍
• Heavy focus on endpoint-driven detections, with integrations for third-party EDR solutions and a telemetry-focused agent: That leaves visibility gaps across network, cloud, and identity, which is exactly where the attacks Todyl's engine is built to catch tend to live. Blackpoint does not have a native network security option, with no SASE or ZTNA, and no LAN Zero Trust for protection against lateral movement and internal threats.

How Todyl is different

The Detection & Analysis Engine treats identity, endpoint, network, cloud, and SaaS as first-class signal sources in a single coordinated pipeline. The SIEM is not a separate compliance tool sitting next to the detection stack. It is part of the detection stack, delivering the same view our MXDR team uses to investigate and respond. Because the architecture is cross-surface by design, the visibility gaps that endpoint-centric MDR creates are closed at the foundation rather than patched through integrations. And unlike Blackpoint's largely automated and hands-off approach outside of critical issues, Todyl MXDR is always available via Slack, Teams, email, and phone -- with a named DRAM acting as an extension of your business with ongoing recommendations and threat hunts.

What This Means When You're Evaluating MDR

A few questions worth asking when comparing MDR offerings, whether or not Todyl is on the shortlist:

• Where in the pipeline is data filtered? Filtering after evaluation is good engineering. Dropping data at the point of collection, before detection logic has a chance to evaluate it, creates forensic gaps and missed detections. ‍
• How do you reach your SOC, and do they reach out to you? Reactive, email-only SOC access creates delays in the moments that matter most. Proactive outreach and named resources change the dynamic entirely. ‍
• Is the SIEM part of the detection architecture, or sitting next to it? If the SIEM exists primarily for compliance and reporting, advanced detection and threat hunting will be limited by design. ‍
• Where does AI actually live in the stack? AI-powered detection and investigation applied across every tier of the pipeline is different from AI applied only at the end of a traditional workflow. ‍
• What surfaces does the architecture treat as first-class? Endpoint-only architectures leave gaps that modern attacks deliberately exploit.

The bottom line

Modern attacks are designed to evade single-surface detection, exploit gaps between tools, and outpace human-driven investigation. The MDR offerings that struggle most against those attacks tend to share architectural traits: data dropped at collection, reactive SOC workflows, SIEMs positioned as compliance tools, and endpoint-centric visibility.

Todyl's Detection & Analysis Engine was built around the opposite set of choices. Multi-tier, AI-powered detection that surfaces, enriches, classifies, triages, and escalates automatically. Full context aggregation across identity, endpoint, network, and cloud. AI-assisted investigation that follows the attacker's actual path. Continuous signal detection that catches the low-level indicators others suppress. And 24x7 MXDR escalation built into the architecture, with named experts available via preferred communication channels and proactive outreach, so confirmed threats move from detection to containment without delay.

The labels on a comparison sheet may look similar. The architecture underneath is where the real difference lives.

The series, in one place

If you are catching up:

• Post 1: Why Modern Attacks Are Breaking Traditional Detection
• Post 2: Introducing Todyl's Detection & Analysis Engine
• Post 3: How Todyl's Detection & Analysis Engine Catches What Traditional Detection Misses
• Post 4: Not All Detection Is Equal: How Todyl's Detection & Analysis Engine Compares to Other MDR Approaches