Part of the DoW SBIR/STTR FY27 Release 2 topic list. Browse all 83 topics by industry or service.
Topic DTR27TZ02-NV001 from the DoW STTR 2027 BAA, Release 2. Pre-release now; proposals due January 6, 2027, 12:00 PM ET.
Quick answer
The Defense Threat Reduction Agency (DTRA) wants the Biolab Mirror, an AI/ML digital twin that cross-checks a biological facility's digital logs against physical and biological signatures to detect clandestine production, containment spoofing, and drift away from declared research. This is an STTR topic that starts with a Phase I architecture and signature-mapping effort. Proposals are due January 6, 2027, 12:00 PM ET.
Solicitation at a glance
- Topic number: DTR27TZ02-NV001
- Official title: Biolab Mirror: Artificial Intelligence / Machine Learning (AI/ML) Digital Twin for Intent Characterization and Bio-Containment Integrity
- Agency: Defense Threat Reduction Agency
- Program: STTR (DoW STTR 2027 BAA, Release 2)
- Award type: Phase I (see topic text for Phase II)
- Award amount: Up to $250,000 (7 months, plus up to $6,500 TABA). Source: DTRA Release 2 instructions in DSIP.
- CMMC level: Level 1
- ITAR: No
- Technology areas: Bio Medical, Information Systems, Sensors
- Pre-release (direct TPOC contact allowed): November 4 to December 2, 2026, 8:00 AM ET
- Opens in DSIP: December 2, 2026, 8:00 AM ET
- Proposals due: January 6, 2027, 12:00 PM ET
- Official source: DSIP Topics app (search DTR27TZ02-NV001)
BW&CO's take
What the Defense Threat Reduction Agency wants
Adversaries can spoof building management systems and alter LIMS records, so DTRA wants a digital twin that does not trust digital logs. The Biolab Mirror fuses continuous physical and biological signatures (metabolic output, thermodynamics, material logistics) with discrete telemetry (LIMS, SCADA/BMS) and uses conservation of mass, thermodynamics, and metabolic rates to flag Coherence Failures, such as water inflow not matching effluent outflow. Phase I maps the signatures of a mid-size BSL-3/BSL-4 facility, defines the coherence models, and lays out an AI/ML intent roadmap and tiered sensor strategy. Phase II builds a software prototype that finds injected anomalies in a performer-generated synthetic facility dataset.
Who is a good fit
- Digital twin or physics-informed anomaly detection work on operational technology (SCADA, BMS) data
- Knowledge of BSL-3/BSL-4 facility operations, including effluent decontamination, autoclaves, and vivarium systems
- Ability to generate realistic synthetic facility datasets from biological and physical engineering standards
- Experience normalizing and categorizing multi-source procurement data
Watch-outs
- STTR program rules require the small business to team with a research institution; the topic text does not name one, so line up that partner early.
- The performer generates its own Phase II training and validation datasets, so the proposal needs a credible synthetic data plan.
- Phase II must detect specific injected scenarios (population ghosting and containment spoofing) and demonstrate ingesting, normalizing, and categorizing multi-source procurement registries.
- The topic lists CMMC Level 1. Direct contact with the topic authors is allowed only during pre-release, which ends when the topic opens December 2, 2026, 8:00 AM ET.
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Official topic text (verbatim from DSIP)
The text in this section is copied word for word from the official DSIP topic as published on November 4, 2026. DSIP is the official source and topics can be amended. Always confirm the current version at dodsbirsttr.mil before you submit. Our commentary above is BW&CO's interpretation, not Government guidance.
Title
Biolab Mirror: Artificial Intelligence / Machine Learning (AI/ML) Digital Twin for Intent Characterization and Bio-Containment Integrity
Objective
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.
Description
The proliferation of dual-use biological technologies and automated, high-containment laboratories globally presents a profound challenge to arms control verification and biosurveillance [3]. A state-sponsored facility operating under a declared, peaceful mandate (e.g., vaccine development, agricultural research) possesses all the necessary infrastructure, reagents, and personnel to rapidly pivot to unauthorized, non-compliant, or undeclared high-risk biological material production [2]. Current verification methods rely heavily on periodic physical inspections, voluntary declarations, and audits of digital LIMS. However, advanced adversaries can digitally spoof BMS, manipulate LIMS records, and conceal clandestine pathogen scaling through “population ghosting” [4][5]. The Department of War (DoW) requires a “Physics-to-Mesh Zero Trust” architecture [6]. This solicitation seeks proposals for a “Biolab Mirror”—a comprehensive Digital Twin that does not inherently trust digital control logs. Instead, the Mirror must continuously cross-validate digital telemetry against the unalterable laws of physics, chemistry, and biology (Conservation of Mass, Thermodynamics, Metabolic Rates). By establishing a rigorous physical baseline (e.g., correlating total water inflow with Effluent Decontamination System (EDS) out-flow, or matching documented animal feed procurement to autoclave waste mass and vivarium Carbon Dioxide (CO2) exhaust), the AI/ML engine must automatically flag the subtle “Coherence Failures” that betray clandestine pathogen scaling, undeclared animal models, or spoofed containment boundaries.
SUPPLEMENTAL INFORMATION
- Programmatic Metrics and Data Sourcing
- Data Sourcing Strategy Synthetic Baseline Generation (Performer Sourced): The performer is responsible for generating the Phase II training and validation datasets using established biological and physical engineering standards.
- Phase I Exit Metrics Measure of Performance (MOP) 1 (Architecture Delivery): Deliver a Digital Twin architecture mapping cross-domain signatures including specialized cross-domain digital and physical validation points.
- Strategic Offshoots & Cyber-Physical Emulation
- Executive Summary: The primary objective of the Biolab Mirror SBIR/STTR is defensive: to establish a high-fidelity “Physics-to-Mesh Zero Trust” baseline to detect clandestine activity and intent. Successful completion yields a secondary strategic asset: a perfect, un-spoofed “Cyber-Terrain” map that can be ported into isolated cyber-ranges to simulate, analyze, and train against multi-domain attacks targeting critical high-containment infrastructure [10].
- The Capability Gap in Current Cyber-Ranges: Current cyber-ranges are optimized for IT network emulation and struggle to accurately emulate OT and the physical realities of a BSL-3/BSL-4 environment.
- The Biolab Mirror as the Emulation Foundation: The Biolab Mirror solves this gap. By mapping the continuous physical signatures directly to the discrete digital telemetry, the performer constructs a fully functional Cyber-Physical Sandbox.
The Attack Simulation: Authorized cybersecurity evaluation teams and system auditors can execute multi-stage simulated attacks against this virtual target (e.g., spear-phishing an academic researcher -> pivoting to the BMS network -> manipulating the Effluent Decontamination System). The Red Team must navigate the actual laws of biology and thermodynamics to succeed.
- Strategic ROI & Follow-On Potential: Authorizing the Biolab Mirror effort provides a “two-for-one” return on investment (ROI), yielding a defensive characterization engine and a foundational architecture for dedicated cybersecurity testing and critical infrastructure protection.
Phase I
Architecture & Signature Mapping: Develop the conceptual architecture for the Biolab Mirror. The performer must comprehensively map all critical signatures of a mid-size Biosafety Level 3 (BSL-3) / Biosafety Level 4 (BSL-4) facility, categorizing them into Continuous (Physical/Biological) and Discrete (Digital/Telemetry) domains [7]. • Physics-to-Mesh Framework: Define the “Coherence Fuses” and mathematical models required to cross-validate specific digital assertions (e.g., BMS reporting “Sterilization Complete”) with physical ground truth (e.g., calculated facility equipment power utilization metrics or chemical reagent depletion). • Intent Characterization Roadmap: Develop an AI/ML roadmap demonstrating how the Mirror will analyze deviations from the established facility baseline to detect the transition from legitimate research to unauthorized, non-compliant, or undeclared high-risk biological material production. This roadmap must include not only mass-balance analysis (detecting how much is being produced) but also Procurement Vector Analysis (detecting what is being produced). • Resource Prioritization Strategy: Design a tiered sensor deployment strategy that maximizes the use of existing, zero-cost facility infrastructure while identifying the critical “Choke Points of Transduction” requiring specialized physical validation. • Final Deliverable: A comprehensive Phase I report detailing the Digital Twin conceptual architecture, the mathematical coherence models, and a preliminary design for Phase II software implementation.
Phase II
Prototype Development: Develop, demonstrate, and validate a working software prototype of the Biolab Mirror AI/ML engine. • Synthetic Facility Ingestion: The prototype must successfully ingest and process a complex, simulated baseline dataset representing normal operations of a mid-size biological facility. • Anomaly Detection: Utilize the prototype to successfully identify simulated “Coherence Failures” introduced into the dataset. Scenarios must include: • Population Ghosting: Detecting undocumented pathogen scaling or animal models via mass balance and metabolic discrepancies [9]. • Containment Spoofing: Detecting unauthorized EDS discharges masked by manipulated operational technology telemetry [10]. • Procurement Vector Validation: Demonstrate the prototype’s capability to ingest, normalize, and categorize multi-source procurement registries. • Transition Strategy: Provide a clear transition plan detailing how the Biolab Mirror architecture can be integrated into standard government biosurveillance architectures for remote facility assessment and biological risk verification [11].
Phase III Dual Use Applications
Government Application: Integration into standard government biosurveillance architectures for remote facility assessment and biological risk verification to quickly ascertain historical facility operations. • Commercial Application: Transition to commercial pharmaceutical Quality Assurance / Quality Control (QA/QC), BSL-3/BSL-4 containment safety assurance, and insider-threat protection for proprietary genomic data and high-value biotechnology Intellectual Property (IP).
References
Department of Defense Directive (DoDD) 2060.02, Combating Weapons of Mass Destruction (CWMD) Policy, (2017).
National Academies of Sciences, Engineering, and Medicine (NASEM), Biodefense in the Age of Synthetic Biology, (2018).
World Health Organization (WHO), Global guidance framework for the responsible use of the life sciences: mitigating biorisks and governing dual-use research, (2022).
National Institute of Standards and Technology (NIST), Platform Firmware Resiliency Guidelines, Special Publication (SP) 800-193 (2018).
Peccoud, J., et al., “Cyberbiosecurity: From Naive Trust to Risk Awareness,” Trends in Biotechnology, (2017).
Department of Defense Zero Trust Strategy, (November 2022).
Centers for Disease Control and Prevention (CDC) / National Institutes of Health (NIH), Biosafety in Microbiological and Biomedical Laboratories (BMBL), 6th Edition, (2020).
Environmental Protection Agency (EPA), Design Manual: Municipal Wastewater Stabilization Ponds, (1983).
American Association for Laboratory Animal Science (AALAS), Guide for the Care and Use of Laboratory Animals, 8th Edition, (2011).
Cybersecurity and Infrastructure Security Agency (CISA) Alert (AA20-205A), Recommend Immediate Actions to Reduce Exposure Across Operational Technology (OT) and Control Systems, (2020).
Defense Threat Reduction Agency (DTRA), Director’s Strategic Intent 2025, (Fort Belvoir, VA: Office of the Director, February 3, 2025).
Keywords
Digital Twin, Cyberbiosecurity, Intent Characterization, AI/ML, Bio-containment, Zero Trust, Biosurveillance
Frequently asked questions
What is DTR27TZ02-NV001?
It is a DTRA STTR topic in the DoW STTR 2027 BAA to develop the Biolab Mirror, a digital twin and AI/ML method for continuous, non-invasive integrity verification and intent characterization of a mid-size, state-sponsored biological research facility.
When is DTR27TZ02-NV001 due?
Proposals are due January 6, 2027, 12:00 PM ET. Pre-release began November 4, 2026, and direct contact with the topic authors is allowed only until the topic opens on December 2, 2026, 8:00 AM ET.
Is this a Phase I or Direct-to-Phase II topic?
It is an STTR topic with a Phase I effort focused on architecture and signature mapping, followed by a Phase II working software prototype. It does not describe a Direct-to-Phase II option.
What is a Coherence Failure?
It is a mismatch between what digital telemetry reports and what physics allows, for example total water inflow not matching effluent decontamination outflow, or animal feed purchases not matching autoclave waste mass and vivarium CO2 exhaust. The AI/ML engine must flag these automatically.