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	Comments on: Large Language Models Will Not Enhance National Security Decision Making	</title>
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		By: gsharpe		</title>
		<link>https://globalsecurityreview.com/large-language-models-will-not-enhance-national-security-decision-making/#comment-7167</link>

		<dc:creator><![CDATA[gsharpe]]></dc:creator>
		<pubDate>Wed, 23 Sep 2026 10:22:02 +0000</pubDate>
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					<description><![CDATA[Alexis, I applaud your work in this critical area at a time of consequence in our nation.  This is an excellent article that poses a useful warning against treating LLMs as autonomous decision-makers, particularly in high-consequence national-security contexts. I believe the message should be that we distinguish more carefully between LLMs making decisions and LLMs supporting human decision-makers through bounded tasks such as document triage, multilingual translation, structured extraction, drafting, red-teaming, scenario generation, and retrieval from authoritative sources. The paper treats “national security decision-making” as if it were a single task. In practice, it ranges from routine administrative judgment to intelligence analysis, force management, operational planning, crisis management, and potentially lethal employment decisions.

Hallucination and inconsistency are serious limitations, especially in national security, but they do not by themselves demonstrate that every LLM-enabled decision-support use case lacks value. I would say that LLMs should not be granted unverified or irreversible decision authority—and should be deployed only with task-specific evaluation, provenance, access controls, auditability, human accountability, and escalation thresholds calibrated to the mission risk. The National Institute of Standards and Technology has conducted extensive research on LLMs and AI in particular. Their researchers have often framed the Generative AI problem as one of use-case-specific risk management rather than categorical infeasibility. This debate should continue to keep the main topic in focus for defense and industry leaders alike.]]></description>
			<content:encoded><![CDATA[<p>Alexis, I applaud your work in this critical area at a time of consequence in our nation.  This is an excellent article that poses a useful warning against treating LLMs as autonomous decision-makers, particularly in high-consequence national-security contexts. I believe the message should be that we distinguish more carefully between LLMs making decisions and LLMs supporting human decision-makers through bounded tasks such as document triage, multilingual translation, structured extraction, drafting, red-teaming, scenario generation, and retrieval from authoritative sources. The paper treats “national security decision-making” as if it were a single task. In practice, it ranges from routine administrative judgment to intelligence analysis, force management, operational planning, crisis management, and potentially lethal employment decisions.</p>
<p>Hallucination and inconsistency are serious limitations, especially in national security, but they do not by themselves demonstrate that every LLM-enabled decision-support use case lacks value. I would say that LLMs should not be granted unverified or irreversible decision authority—and should be deployed only with task-specific evaluation, provenance, access controls, auditability, human accountability, and escalation thresholds calibrated to the mission risk. The National Institute of Standards and Technology has conducted extensive research on LLMs and AI in particular. Their researchers have often framed the Generative AI problem as one of use-case-specific risk management rather than categorical infeasibility. This debate should continue to keep the main topic in focus for defense and industry leaders alike.</p>
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