Biolab Mirror: Artificial Intelligence / Machine Learning (AI/ML) Digital Twin for Intent Characterization and Bio-Containment Integrity

During pre-release, until the topic opens on Dec 2, 2026, you may contact the topic author directly with technical questions. After that, questions go through the Topic Q&A on DSIP.

Develop a high-fidelity "Digital Twin" (The Biolab Mirror) and an advanced Artificial Intelligence / Machine Learning (AI/ML) methodology capable of continuous, non-invasive integrity verification and intent characterization of a mid-size, state-sponsored biological research facility. The solution must fuse continuous physical/biological signatures (e.g., metabolic output, environmental thermodynamics, material logistics) with discrete digital telemetry (e.g., Laboratory Information Management Systems (LIMS) logs, Supervisory Control and Data Acquisition (SCADA) / Building Management Systems (BMS) control data). The objective is to detect clandestine biological activities, containment spoofing, and the subtle operational drift indicative of a transition from peaceful, declared research to unauthorized, non-compliant, or undeclared high-risk biological material production [1][2]. This effort bridges a critical gap between standard Information Technology (IT) network defense and biological physical reality by establishing a “Zero Trust” posture that cross-validates digital telemetry against the immutable laws of physics. Unlike current commercial cyberbiosecurity offerings that focus solely on the IT perimeter or traditional biosurveillance platforms that are purely reactive, the Biolab Mirror is proactively designed to characterize nefarious intent before unauthorized, non-compliant, or undeclared high-risk biological material production occurs.

The full objective, description, Phase I, II and III requirements and evaluation criteria are in the official topic on DSIP.