The Fundamental Problem With Detection Engineering (And How 0Labs is Fixing It)

The Fundamental Problem With Detection Engineering (And How We're Fixing It)

Detection engineering suffers from an asymmetry. Attackers can test their techniques against defensive products in private labs and iterate until their variants successfully evade. Defenders cannot easily do the reverse because they rarely have access to a realistic set of novel attack variants. As a result, most security organizations cannot reliably answer whether their detections provide comprehensive coverage right now.

Coverage gaps are unknown to defenders. SOC teams map detections to MITRE ATT&CK and assume that a comprehensive mapping is adequate, but it isn't. Adversaries chain techniques in novel sequences, substitute different tools, and route around key parts of the detection stack. Most defending teams can't discover detection logic gaps before an incident painfully reveals them.

Detections lose efficacy over time. Log sources get misconfigured, schemas drift, endpoints are added without proper telemetry, and SIEM ingestion pipelines break. There is no CI/CD for detections in most organizations. How does a SOC team continually answer which of their hundreds of SIEM rules are actually working right now?

Purple teaming is too expensive, too infrequent, or simply never performed. A proper engagement requires skilled red team operators, weeks of scoping, and typically costs $100K–$200K. BAS platforms and APTAS companies replay deterministic atomic attack sequences that don't adequately reflect adversaries that adapt and evade. They do not test full attack chains well.

What We are Building

0Labs enables detection engineering teams and security vendors to continuously validate whether their detection capabilities work against realistic, adaptive attacker behavior. Upload or connect your detection rules. Select a threat profile (a specific threat actor campaign, a MITRE technique set, or a strategic objective like "reach domain admin"), then launch a validation run against a live cyber range modeled on your environment.

The platform executes realistic AI-driven attack chains, not single atomic attacks but sequenced, multi-step campaigns where each phase depends on the outcome of the previous one. When a detection alerts, the AI attacker mutates the technique using alternative tools and techniques, then tests again to determine whether the detection succeeds or fails. Our platform then produces a validation report containing exactly which techniques were executed, detected, and missed, then drafts detection rules to cover what originally went undetected. 

Our critical differentiator is that our system operates at the campaign level, not only the technique level. When our agent0 is blocked at one phase of the kill chain, it replans and stealthily attempts an alternative path, the same way a real adversary would. The output is not merely "your rule for SMB share enumeration missed an obfuscation technique." Rather, "your detection stack has zero coverage for an entire lateral movement path that bypasses credential theft and uses Kerberos delegation abuse instead." This is what separates us from existing BAS and APTAS tools. This is what makes our output actionable for defensive teams building real coverage.

Who Needs This

This is for security vendors and security teams who build detection logic but lack large-scale operational telemetry to validate against, we give them a QA pipeline where every rule is tested against hundreds of realistic attack variants before it ships. This is also for OT/ICS security vendors who protect environments they can almost never test against because standing up realistic OT environments is expensive, requires rare talent, and testing on production is too risky. Finally, mid-sized enterprises with a small security team and maybe a SIEM or EDR but no managed detection service and no budget for regular purple teaming.

The fundamental constraint in detection engineering has always been that defenders cannot test at the speed or scale of real adversaries. 0Labs makes continuous, adaptive detection validation available to every team that currently cannot afford or cannot staff it.

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