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

🇺🇸 United States·U.S. Department of War — DTRA(Government)
Funding
Needs verification
Deadline
2027.01.06
D-61
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Overview

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STTR · Phase: BOTH · Topic DTR27TZ02-NV001 · Solicitation 27.TZ

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 1. 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. 2. 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.

Category
R&D
Industry
AI & Data
Technology
AI / ML, Digital Twin
Target stage
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SBIR/STTR 은 미국 중소기업만 지원할 수 있습니다(Small Business Act 법정 요건). • 계열사를 포함해 상시 종업원 500명 이하 • 미국 시민 또는 영주권자 1인 이상이 50%를 초과해 직접 소유·지배 • 미국 내 사업장을 두고 주로 미국 내에서 사업을 영위할 것 • 수행책임자(PI)의 주된 근무처가 신청 기업일 것 출처: https://www.sbir.gov/faq/eligibility-requirements

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🇺🇸 United States
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No
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미국 내 사업장을 두고 미국 시민·영주권자가 50%를 초과해 소유한 중소기업만 신청할 수 있습니다(법정 요건).

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Last fetched
2026.11.06
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  • · 4h ago — 최초 수집 (f62f0e3d)