Zero Trust RFI Artificial Intelligence
ID: ZTAI0001Type: Sources Sought
Overview

Buyer

DEPT OF DEFENSEWASHINGTON HEADQUARTERS SERVICES (WHS)WASHINGTON HEADQUARTERS SERVICESWASHINGTON, DC, 203011000, USA
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    Description

    The Department of Defense, through the Washington Headquarters Services, is issuing a Request for Information (RFI) focused on enhancing Zero Trust assessments using Artificial Intelligence (AI) and Machine Learning (ML). The objective is to gather insights on Commercial Off-the-Shelf platforms and services that can automate and scale Zero Trust Purple Team evaluations across both UNCLASSIFIED and SECRET networks, aiming to achieve Target Level Zero Trust by FY27. This initiative is critical for validating compliance with established Zero Trust activities and identifying limitations in current practices. Interested parties are invited to submit their responses, limited to ten pages, by 12 PM ET on February 9, 2026, to Leanne Condren at leanne.m.condren.civ@mail.mil.

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    The Department of Defense (DoD) Chief Information Officer (CIO) is issuing a Request for Information (RFI) for market research purposes to explore how Artificial Intelligence (AI) and Machine Learning (ML) can enhance and automate Zero Trust (ZT) assessments across the DoD enterprise. The ZT Portfolio Management Office (ZT PfMO) seeks industry insights on Commercial Off-the-Shelf platforms and services that leverage these technologies to scale ZT Purple Team evaluations on UNCLASSIFIED and SECRET networks. The objective is to accelerate assessments, validate compliance with 91 Target Level Zero Trust Activities and ten Acceptance Criteria (detailed in Appendix A), identify limitations, and achieve Target Level ZT by FY27. The RFI poses 13 questions covering the use of automation and AI in ZT Purple Team activities, obstacles, suitable tasks, attack path identification, simulation of attack scenarios, analysis of detection effectiveness, report generation, valuable data sources, emerging trends, innovative technologies, barriers to entry, implementation friction points, and neural network training data. Responses are limited to ten pages, due by 12 PM ET on Feb. 9, 2026, and must be submitted electronically.
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