Is US Defense Acquisition Ready to Acquire AI-Enabled Capabilities? Assessing the DoD Software Acquisition Pathway Through a Scenario-Based Policy Analysis

• URL: http://arxiv.org/abs/2606.07393v1
• Date: Fri, 05 Jun 2026 15:30:48 GMT
• Title: Is US Defense Acquisition Ready to Acquire AI-Enabled Capabilities? Assessing the DoD Software Acquisition Pathway Through a Scenario-Based Policy Analysis
• Abstract summary: The U.S. Department of Defense has modernized its acquisition processes through the Adaptive Acquisition Framework.<n>This paper evaluates whether SWP is sufficient to address the unique demands of AI acquisition.
• Score: 6.962685892199527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As AI systems transition from experimental prototypes to mission-critical tools, their dependence on dynamic data, evolving models, and governance raises questions about whether existing acquisition pathways can keep pace. The U.S. Department of Defense has modernized its acquisition processes through the Adaptive Acquisition Framework, with the Software Acquisition Pathway (SWP) serving as the primary mechanism for acquiring software-intensive capabilities. This paper evaluates whether SWP is sufficient to address the unique demands of AI acquisition. In this work, we perform a scenario-based evaluation that traces a notional AI-enabled program through key SWP planning activities to assess how policy translates into program artifacts and decisions. We use Policy Scenario Analysis to examine whether the SWP-centered governance stack provides sufficient actionable support for AI acquisition. The governance stack provides a viable foundation for iterative delivery and AI testing. However, we identify a recurring actionability problem in the core guidance. AI-specific controls for data provenance, lifecycle management, and human oversight remain distributed across supplemental documents rather than embedded in the program-facing mechanisms through which SWP is executed. This disconnect leaves program offices reliant on inconsistent local interpretation. We conclude by recommending an AI-supporting sub-path and targeted artifact refinements to better bridge this policy-to-artifact gap.

Related papers

• The Evaluation Context Protocol (ECP): A Portable Contract for AI Agent Evaluation [0.0]
  This paper explores the critical methodologies for evaluating AI agents and the essential role of advanced observability infrastructure.<n>We propose the Evaluation Context Protocol (ECP), an early-stage, vendor-neutral framework intended to act as a contract layer for agentic systems.<n>We describe an open-source reference implementation that includes adapters for LangChain, LlamaIndex, CrewAI, and PydanticAI.
  arXiv  Detail & Related papers  (2026-08-18T07:14:36Z)
• Traccia: An OpenTelemetry-Based Governance Platform for AI Systems [0.7919969809015938]
  The rapid development of Large Language Models (LLMs) and Artificial Intelligent (AI) powered autonomous agents has fundamentally changed the existing forms of software governance.<n>Current solutions fail to protect unbound state space agentic architecture from serious threats such as alignment, security concerns, and unauthorized deployment of shadow AI systems.<n>A solution is proposed for overcoming the discussed challenges in form of a coherent multi-level AI governance stack Traccia built on the top of OpenTelemetry infrastructure platform.
  arXiv  Detail & Related papers  (2026-07-15T19:14:21Z)
• When AI Meets Wall Street: A Survey on Trustworthy AI in Fintech [32.022055126183865]
  We propose a unified, lifecycle-centric and mechanism-driven framework for financial AI.<n>We partition financial AI into three lifecycle stages: training and updating, deployment and inference, and operation, monitoring, and feedback.<n>For each subtype, we analyse algorithmic strategy, feasibility constraints, stealth and persistence, and downstream financial consequences.
  arXiv  Detail & Related papers  (2026-05-28T23:10:04Z)
• Uncovering Security Threats and Architecting Defenses in Autonomous Agents: A Case Study of OpenClaw [42.9467373594646]
  This report presents a comprehensive security analysis of the OpenClaw ecosystem.<n>We highlight critical vulnerabilities such as prompt injection-driven Remote Code Execution (RCE), sequential tool attack chains, context amnesia, and supply chain contamination.<n>To address these systemic architectural flaws, we introduce the Full-Lifecycle Agent Security Architecture (FASA)<n>This theoretical defense blueprint advocates for zero-trust agentic execution, dynamic intent verification, and cross-layer reasoning-action correlation.
  arXiv  Detail & Related papers  (2026-03-13T04:33:05Z)
• Toward Third-Party Assurance of AI Systems: Design Requirements, Prototype, and Early Testing [16.53658640529767]
  We introduce a third-party AI assurance framework that addresses gaps in AI evaluation.<n>We focus on third-party assurance to prevent conflict of interest and ensure credibility and accountability of the process.<n>Our findings show early evidence that our AI assurance framework is sound and comprehensive, usable across different organizational contexts.
  arXiv  Detail & Related papers  (2026-01-30T00:37:12Z)
• Advances and Frontiers of LLM-based Issue Resolution in Software Engineering: A Comprehensive Survey [59.3507264893654]
  Issue resolution is a complex Software Engineering task integral to real-world development.<n> benchmarks like SWE-bench revealed this task as profoundly difficult for large language models.<n>This paper presents a systematic survey of this emerging domain.
  arXiv  Detail & Related papers  (2026-01-15T18:55:03Z)
• Let the Barbarians In: How AI Can Accelerate Systems Performance Research [80.43506848683633]
  We term this iterative cycle of generation, evaluation, and refinement AI-Driven Research for Systems.<n>We demonstrate that ADRS-generated solutions can match or even outperform human state-of-the-art designs.
  arXiv  Detail & Related papers  (2025-12-16T18:51:23Z)
• QOC DAO - Stepwise Development Towards an AI Driven Decentralized Autonomous Organization [0.0]
  This paper introduces a structured approach to improving decision making in Decentralized Autonomous Organizations (DAO)<n>We outline a stepwise governance framework that evolves from human led evaluations to fully autonomous, AI-driven processes.
  arXiv  Detail & Related papers  (2025-11-10T12:55:23Z)
• Barbarians at the Gate: How AI is Upending Systems Research [58.95406995634148]
  We argue that systems research, long focused on designing and evaluating new performance-oriented algorithms, is particularly well-suited for AI-driven solution discovery.<n>We term this approach as AI-Driven Research for Systems ( ADRS), which iteratively generates, evaluates, and refines solutions.<n>Our results highlight both the disruptive potential and the urgent need to adapt systems research practices in the age of AI.
  arXiv  Detail & Related papers  (2025-10-07T17:49:24Z)
• Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned [45.44933002008943]
  This white paper presents the T"UV AUSTRIA Trusted AI framework.<n>It is an end-to-end audit catalog and methodology for assessing and certifying machine learning systems.<n>Building on three pillars - Secure Software Development, Functional Requirements, and Ethics & Data Privacy - it translates the high-level obligations of the EU AI Act into specific, testable criteria.
  arXiv  Detail & Related papers  (2025-09-08T17:52:08Z)
• Towards Log Analysis with AI Agents: Cowrie Case Study [0.23332469289621785]
  This study explores the use of AI agents for automated log analysis.<n>We present a lightweight and automated approach to process Cowrie honeypot logs.<n>Preliminary results demonstrate the pipeline's effectiveness in reducing manual effort and identifying attack patterns.
  arXiv  Detail & Related papers  (2025-08-22T16:50:59Z)
• The AI Imperative: Scaling High-Quality Peer Review in Machine Learning [49.87236114682497]
  We argue that AI-assisted peer review must become an urgent research and infrastructure priority.<n>We propose specific roles for AI in enhancing factual verification, guiding reviewer performance, assisting authors in quality improvement, and supporting ACs in decision-making.
  arXiv  Detail & Related papers  (2025-06-09T18:37:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.