# Optimizing National Security Strategies through LLM-Driven Artificial Intelligence Integration

arXiv: https://arxiv.org/abs/2305.13927 · HTML: https://mlsanity.com/paper/2305.13927/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 22 references that are in the map.
- First posted on arXiv 7 May 2023.

## Built on by (most cited first, 6 of 6)

- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [Agentic AI for autonomous anomaly management in complex systems](https://mlsanity.com/paper/2507.15676/) (2025)
- [On the Performance of an Explainable Language Model on PubMedQA](https://mlsanity.com/paper/2504.05074/) (2025)
- [Semantic Scheduling for LLM Inference](https://mlsanity.com/paper/2506.12204/) (2025)
- [Cross-Lingual Stability and Bias in Instruction-Tuned Language Models for Humanitarian NLP](https://mlsanity.com/paper/2510.22823/) (2025)
- [Reducing Large Language Model Bias with Emphasis on 'Restricted Industries': Automated Dataset Augmentation and Prejudice Quantification](https://mlsanity.com/paper/2403.13925/) (2024)

## Abstract (arXiv)

As artificial intelligence and machine learning continue to advance, we must understand their strategic importance in national security. This paper focuses on unique AI applications in the military, emphasizes strategic imperatives for success, and aims to rekindle excitement about AI's role in national security. We will examine the United States progress in AI and ML from a military standpoint, discuss the importance of securing these technologies from adversaries, and explore the challenges and risks associated with their integration. Finally, we will highlight the strategic significance of AI to national security and a set of strategic imperatives for military leaders and policymakers