# On Large Language Models in National Security Applications

William N. Caballero  Phillip R. Jenkins  Department of Operational Sciences,
Air Force Institute of Technology, WPAFB, OH 45433

###### Abstract

The overwhelming success of GPT-4 in early 2023 highlighted the transformative
potential of large language models (LLMs) across various sectors, including
national security. This article explores the implications of LLM integration
within national security contexts, analyzing their potential to revolutionize
information processing, decision-making, and operational efficiency. Whereas
LLMs offer substantial benefits, such as automating tasks and enhancing data
analysis, they also pose significant risks, including hallucinations, data
privacy concerns, and vulnerability to adversarial attacks. Through their
coupling with decision-theoretic principles and Bayesian reasoning, LLMs can
significantly improve decision-making processes within national security
organizations. Namely, LLMs can facilitate the transition from data to
actionable decisions, enabling decision-makers to quickly receive and distill
available information with less manpower. Current applications within the US
Department of Defense and beyond are explored, e.g., the USAF’s use of LLMs
for wargaming and automatic summarization, that illustrate their potential to
streamline operations and support decision-making. However, these applications
necessitate rigorous safeguards to ensure accuracy and reliability. The
broader implications of LLM integration extend to strategic planning,
international relations, and the broader geopolitical landscape, with
adversarial nations leveraging LLMs for disinformation and cyber operations,
emphasizing the need for robust countermeasures. Despite exhibiting “sparks”
of artificial general intelligence, LLMs are best suited for supporting roles
rather than leading strategic decisions. Their use in training and wargaming
can provide valuable insights and personalized learning experiences for
military personnel, thereby improving operational readiness.

###### keywords:

Large language models , applied statistics , artificial intelligence ,
national security , strategic planning

L1.4

##  1 Introduction

The profound success of GPT-4 in early 2023 [56] demonstrated the
revolutionary nature of large language models (LLMs) to a global audience.
Their capabilities were immediately recognized as a potentially disruptive
force in numerous industries, e.g., healthcare [30], education [38], and
finance [37]. However, as with other artificial intelligence (AI) models, LLMs
are not limited in scope to civilian applications; their capabilities may also
transform national security operations. Indeed, as Richard Moore of MI6
highlighted, AI, including LLMs, is already a critical factor in current
threat environments, acting as a force multiplier for many tools and practices
[62].

The nature and degree of this transformation have yet to be determined, but
speculation abounds. For example, using AI in defense applications is a
disquieting topic for some observers, conjuring pop-culture images of Skynet
[39]. However, numerous applications lacking autonomous weapon employment can
be drastically reshaped through AI integration. Such applications overlap with
civilian activities (e.g., natural language processing tasks, computer-vision
implementation, and decision-analytic problems), domains where LLMs are being
rapidly commercialized. Thus, opinions on using LLMs in defense applications
run the gamut; proponents contend that they may be used to create a virtual
Clausewitz, whereas dissidents believe their use in any defense setting would
result in dangerous hallucinations. Similar contentions arise when considering
using LLMs outside of military activity and within the use of other national
security instruments, e.g., diplomatic, information, and economic measures
[75].

From the perspective of a statistician or data scientist, applying LLMs to
national security problems will feel quite familiar despite the domain’s
derived idiosyncrasies. Nearly every civilian activity has a national security
analog, implying that LLMs developed for a particular civilian task may be
extended to a national security setting after domain-specific modification.
Military physicians may receive decision support from LLMs, provided these
systems have been tuned to the realities of combat. Likewise, LLMs may support
the decision-making of military commanders via automatic summarization,
sentiment analysis, and topic modeling, provided they have been trained on
domain-specific vocabulary, acronyms, and jargon. However, the national
security setting modifies the relative importance of select LLM design
challenges. For example, even if an LLM is only used for automatic
summarization during armed conflict, the effect of hallucinations may be
catastrophic if these summaries inform senior-level decision-making. On
another extreme, if an LLM is used to provide strategy or planning
recommendations, then model interpretability and output transparency are
paramount to ensuring effective human oversight. The same applies to
understanding emergent LLM abilities in national security applications as well
[83, 59]. Moreover, when an LLM has access to classified information, the
importance of data privacy and the relevance of prompt-injection attacks
[e.g., see 33] increases dramatically; if the underlying information is not
appropriately safeguarded, security incidents (e.g., data spillage) that
endanger national security personnel and the civilian population pose a
significant risk. The national security setting is high stakes and leaves
little margin for error in LLM applications.

Therefore, within this manuscript, we explore the ramifications of LLMs on
national security applications viewed in terms of the strategic competition
continuum set forth by the US Joint Chiefs of Staff [73]. This framework does
not view international conflict in binary terms (e.g., war and its absence)
but interprets interactions between rivals continuously varying from peaceful
cooperation to armed conflict across multiple domains. We synthesize varying
perspectives on the use of LLMs for national security applications, examine
the current usage of LLMs in defense settings, explore their efficacious use
in defense applications, and forecast their more general effect on national
security writ large. Additionally, whereas LLMs are the focus of this
manuscript, emphasis is also placed on the interface of LLMs with alternative
probabilistic, statistical, and machine learning (ML) methods to determine how
this interplay may affect national security operations.

The remainder of this manuscript is structured as follows. Section 2 inspects
the contemporary application of LLMs by the national security establishment
and reviews its relation to scholarly research on the topic. In this manner,
we tangibly explore the reception of LLMs among a subset of national security
organizations and investigate how they relate to the views of other national
security scholars. Based upon this information, Section 3 provides our views
on how LLMs can be best integrated into defense applications and discusses
their broader repercussions across the other instruments of national power.
Section 4 provides concluding remarks and posits avenues of future inquiry.

##  2 A Rapidly Evolving and Uncertain Landscape

Whereas the impressiveness of LLMs is self-apparent, at the time of this
writing, their utility in application is less certain. This is typified by
Google’s difficulties incorporating Gemini, a multi-modal LLM, into its search
engine [12] and recent surveys on generative AI utilization [88].
Nevertheless, the LLM landscape is rapidly evolving, inducing numerous
stakeholders to experiment with associated transformations to national
security operations. This section reviews such experimentation and their
relation to the scholarly literature.

###  2.1 Recent LLM Initiatives and Applications by National Security
Organizations

From the perspective of the US Department of Defense (DoD), the emergence of
high-functioning LLMs was perfectly timed for innovation. After nearly two
decades focused on counterterrorism operations, the 2018 National Defense
Strategy (NDS) refocused the DoD on inter-state strategic competition. This
strategy recognized that the military advantage enjoyed by the US after the
Cold War had eroded [40] and, as one means to rectify this realty, stated that
the DoD would “invest broadly in military application of autonomy, artificial
intelligence [AI], and machine learning [ML], including rapid application of
commercial breakthroughs.” Six years after its publication, the acquisition
practices exposed by the 2018 NDS, which are also expressed in the 2022 NDS,
have taken root, and their effects are apparent in the DoD’s approach to LLMs.

In August 2023, Task Force (TF) Lima was created under the DoD Chief Data and
AI Office (CDAO) with the express purpose of identifying low-risk applications
for generative AI and LLMs [78]. Such a focus on low-risk areas coincides with
recent strategic guidance from Biden [5] and the US Department of Defense [70]
on responsible AI use; in the view of Deputy Secretary of Defense Kathleen
Hicks, “most commercially available systems enabled by [LLMs] aren’t yet
technically mature enough to comply with our DoD ethical AI principle.” At the
time of this writing, TF Lima’s work continues; however, it has highlighted
the DoD’s use of LLMs to streamline staff operations within its internal
bureaucracy via, e.g., the automatic summarization of doctrine, instructions,
and policy manuals. TF Lima is also endeavoring to identify how LLMs affect
classification guidance, especially given the effects of classification by
combination, i.e., a national security particularity whereby specified
unclassified information becomes classified upon combination [81]. In line
with such concerns, TF Lima is also constructing a “virtual sandbox” through
which military personnel can experiment with generative AI tools [80];
suitably, TF Lima has its own ChatGPT instance that can be used to discuss its
activities [67].

Despite the ongoing development of a unified experimentation platform, the
pace of LLM development has necessitated that other DoD agencies work in
parallel with TF Lima. The United States Air Force (USAF) has been
particularly active, and LLMs featured chief among the topics discussed at the
USAF’s 2024 Data, Analytics, and AI Forum. Multiple USAF organizations have
developed proof of concepts whereby LLMs are leveraged to expedite myriad
coding and administrative tasks. For example, Air Mobility Command has
leveraged LLMs to generate campaign simulations based on user-defined, text-
based narratives. Moreover, US Air Forces Central, among other USAF
organizations, use LLMs to expedite routine maintenance of their endemic
software tools. More recently, at the Bravo 11 Hackathon, a US Pacific Air
Forces team spearheaded an ambitious use case whereby LLMs automatically
summarize incoming mission reports to rapidly distill insights for commanders
[32, 57]. In so doing, the team illustrated how an LLM can readily handle a
task that, when performed manually, requires multiple operators and weeks of
man-hours. Similar time savings were shown at the Air Force Test Center’s Data
Hackathon when an LLM pipeline was created to automatically generate flight
test documents, e.g., plans and reports [11]; the prototypes were based on the
relatively small MPT-7b, MPT-30b, Falcon-7b, and Falcon-40b models augmented
with a customized retrieval augmented generation (RAG) architecture.

Additionally, Air University’s innovation center (AUiX) has been developing a
GPT framework for wargaming called the Comprehensive Heuristic Utility for
Combat Knowledge (CHUCK), which is currently in a beta stage but shows
exciting potential for the future. This initiative is part of a broader
collaboration with Stanford University’s Hoover Institution and the MIT
Artificial Intelligence Accelerator (MIT/AIA), aimed at advancing wargaming
techniques and exploring LLM’s impact on crisis decision-making. These efforts
align with the Air Force Futures office’s AI initiatives, exploring how AI can
enhance wargaming by running thousands of iterations to optimize strategies
and decision-making [21]. Such initiatives aim to transform traditional
wargaming by incorporating AI capabilities to improve strategic analysis and
operational planning. In conjunction with these wargaming efforts, the Air
Force Research Laboratory also developed NIPRGPT, an LLM recently cleared and
deployed by the USAF for installation on designated unclassified systems [49].

Alternatively, the USAF’s most innovative use cases derive from its academic
institutions. Cadets in the Data Science program at the USAF Academy
constructed an LLM-based prototype to modernize the user interface in the
USAF’s Envision platform [77]. The prototype is a “no-code” solution allowing
users to submit statistical queries via text-based narratives and receive
outputs with corresponding explanations. For example, a user may request a
graphical summary of pilot training graduation rates by commissioning source,
and the tool may output a box plot with an associated narrative on how to
interpret the figure. The USAF Academy has also used LLMs for assessments
within its statistical courses. In the Spring 2024 semester, LLMs were used in
its applied statistical modeling class (i.e., MATH 378) to conduct “oral
boards” instead of examinations; that is, students were asked to interface
with an LLM on a specified set of topics, and the associated transcripts were
used to assess student knowledge. LLMs are being further integrated into USAF
Academy instruction via the development of a virtual teaching assistant.
Notably, seniors in the Data Science program created the QuantaIQ prototype
that uses LLMs to generate assignments, conduct assessments, and interface
with students via question-and-answer sessions.

Other branches of the United States military are also exploring LLMs, though
their approaches and applications vary. The United States Army is
experimenting with generative AI in military video games to improve battle
planning by using LLMs, including OpenAI’s GPT-4 Turbo and GPT-4 Vision
models, to provide information on battlefield terrain and details on friendly
and enemy forces [61]. Additionally, the Army is developing new policy
guidance to guide the department’s use of generative AI to streamline
operations while addressing security concerns [22]. In collaboration with
Scale AI, the Marine Corps created an LLM named Hermes to assist in military
planning by synthesizing data, generating hypotheses, and refining courses of
action [29]. At Marine Corps University, experts are incorporating LLMs into
simulations and wargames to test whether they improve analytical products and
ease of use for military students [79]. These experiments demonstrated that
LLMs can enhance the planning process and provide valuable insights but also
require human oversight to manage current limitations, such as hallucinations
and structural biases, to ensure accuracy.

In contrast, the United States Navy has adopted a more cautious approach to
using LLMs. According to a recent memo published by the Navy’s acting Chief
Information Officer, “the use of proprietary or sensitive information poses a
unique security risk and has the potential to lead to data compromise when
employed by commercial generative AI models” [53]. The Navy’s policy advises
against using commercial LLMs for operational purposes until security
requirements are fully investigated and approved [14]. This cautious stance is
reflected in efforts to secure access to LLM technology through Jupiter, the
Navy’s enterprise data and analytics platform, to ensure safe employment [50].
Similarly, the United States Space Force implemented a temporary ban on using
generative AI and LLM tools for official purposes in October 2023 due to
concerns about safeguarding sensitive data [23]. As of the time of this
writing, the ban remains in place as the service continues to evaluate the
best path forward for securely integrating generative AI capabilities into its
mission [24].

Beyond the DoD, other US national security agencies are also exploring LLM
applications. The Central Intelligence Agency (CIA) began exploring generative
AI and LLM applications more than three years before the widespread popularity
of ChatGPT. For example, generative AI was leveraged in a 2019 CIA operation
called Sable Spear to help identify entities involved in illicit Chinese
fentanyl trafficking [2]. The CIA has since used generative AI to summarize
evidence for potential criminal cases, predict geopolitical events such as
Russia’s invasion of Ukraine, and track North Korean missile launches and
Chinese space operations [2]. In fact, Osiris, a generative AI tool developed
by the CIA, is currently employed by thousands of analysts across all eighteen
U.S. intelligence agencies. Osiris operates on open-source data to generate
annotated summaries and provide detailed responses to analyst queries [2]. The
CIA continues to explore LLM-incorporation in their mission sets and recently
adopted Microsoft’s generative AI model to analyze vast amounts of sensitive
data within an air-gapped, cloud-based environment to enhance data security
and accelerate the analysis process [64]. Other agencies, including the
Defense Advanced Research Projects Agency (DARPA), which uses LLMs to detect
and fix critical software vulnerabilities, and MITRE, which collaborates with
NATO to enhance AI security, are also exploring the effects of LLMs in their
application areas [76, 68].

Moreover, allied countries are actively investigating LLMs to enhance military
capabilities across various domains as well. For example, the 2024 TIDE
Hackathon, co-hosted by NATO Allied Command Transformation (ACT) and the Dutch
Ministry of Defence, included an LLM wargaming challenge [48]. This challenge
focused on using LLMs to improve military wargaming by creating dynamic
scenarios and providing real-time feedback to enhance interoperability,
decision-making, and strategy development among allied forces. LLM
capabilities have garnered attention from other NATO entities aside from ACT
as well. Namely, the NATO Communications and Information Agency (NCI Agency)
is developing an AI cognitive agent to automate routine IT-support tasks [20].
Similarly, Hadean, a British deep tech start-up company, secured a contract
with the Defence and Security Accelerator (DASA) to develop an LLM for the
British Army’s virtual training space [25]. This initiative seeks to create a
dynamic virtual environment with realistic human terrain and social media
simulation, providing real-time feedback, generating complex scenarios, and
assisting in after-action reports to enhance military training and decision-
making. Additionally, the United States and Australia are leveraging
generative AI for strategic advantage in the Indo-Pacific, focusing on
applications such as enhancing military decision-making, processing sonar
data, and augmenting operations across vast distances [3]. These efforts are
accelerating data processing, improving the identification and response to
threats, and helping maintain technological and operational superiority by
investing in AI talent and reskilling military personnel. These examples
demonstrate the transformative potential of LLMs on modern military strategy
and operations.

Besides the US DoD and its allies, the nation’s strategic competitors (e.g.,
China, Russia, North Korea, and Iran) are also exploring the national security
applications of LLMs. For example, China employs Baidu’s Erni Bot, an LLM
similar to ChatGPT, to predict human behavior on the battlefield to enhance
combat simulations and decision-making [41]. Some reports indicate Baidu’s
Ernie Bot surpasses ChatGPT in accuracy and real-time information processing
but struggles with political inquiries due to restrictions [66]. Additionally,
the CopyCat network, suspected to be aligned with the Russian government,
leverages LLMs to manipulate media content for disinformation [19, 44]. The
CopyCat network plagiarizes, translates, and edits content from legitimate
media outlets to spread biased political messages aligned with Russian
interests and has been linked to generating sophisticated narratives on the
Ukraine conflict and US politics that are particularly difficult for public
officials to counteract. Moreover, suspected state-backed hackers from China,
Iran, North Korea, and Russia are accused of experimenting with LLMs to assist
in cyber operations, e.g., by generating malicious code and content for
phishing campaigns [18]. For instance, North Korea’s Kimsuky group uses LLMs
to advance their military cyber capabilities by generating content for
phishing campaigns, targeting organizations focused on North Korean defense
and nuclear issues, thereby enhancing their ability to gather intelligence and
exploit vulnerabilities [54]. The widespread exploration of LLMs in such
capacities highlights the threat the technology poses, particularly in cyber
and information warfare, and necessitates the development of countermeasures
to safeguard national security.

###  2.2 Relation to Scholarly Perspectives

Strictly speaking, LLMs are trained to predict the next word in a phrase
conditioned upon previously observed words. However, the surveyed use of LLMs
for national security applications is not concerned with this task in and of
itself. Instead, national security organizations are often concerned with the
emergent abilities of LLMs. The existence of such emergent LLM abilities as
generally conceived is not universally accepted [e.g., see 35, 59].
Nevertheless, it is undeniable that most national security applications using
LLMs seek to extend the technology beyond the task for which the LLMs were
trained.

Natural language tasks are heavily featured in the current efforts of national
security organizations, and, in this regard, the capabilities of LLMs are
well-established. Namely, Min et al. [42] survey the state-of-the-art
performance of pre-trained language models on multiple natural language tasks.
This survey was recently updated by Minaee et al. [43] and, given the rapid
pace of development, another survey will likely be warranted in the near
future. Conspicuously relevant in the defense setting are the capabilities of
LLMs for automatic summarization. Zhang et al. [90] study LLM performance on
news article summarization, finding that a variety of LLMs performed on par
with their human counterparts. The authors also determined that prompting and
instruction tuning, not model size, dictated model performance in zero-shot
summarization. This result presages the general importance of prompt
engineering when using LLMs in defense settings. Moreover, given the immense
scale of national security operations, it is important to understand how well
LLMs summarize longer documents or corpora; these documents often exceed the
context window size of standard LLMs. Chang et al. [7] construct and validate
a metric that evaluates LLM performance on such documents, and initial results
suggest they perform similarly to humans on the task. Schmidt and Robert [60]
specifically study LLM summarization performance in the DoD acquisition
setting with favorable results.

Alternatively, the self-evident efficacy of LLMs for text generation also
implies their efficacy for information warfare. Goldstein et al. [17] and Low
[34] explore how LLMs allow for the automation of influence operations,
circumventing the need for labor-intensive troll farms, once a feature of the
space [84]. Coupled with the disinformation threat of other generative AI
models [85], the need for an effective detection mechanism for LLM-generated
propaganda is imminent. Wan et al. [82] propose a prototypical pipeline for
doing so. However, the work of Chen and Shu [8] suggest that such messages may
be harder to detect than their man-made counterparts, a task of considerable
difficulty in and of itself [89]. Uchendu et al. [69] also discuss the
identification of deepfake texts, concluding that this difficult task may be
facilitated via human collaboration.

Interestingly, the desire to leverage AI for strategy development predates the
development of LLMs. Bazin [4] contrived the concept of a virtual Clausewitz,
a cognitive computing system with access to the whole of human military
thought, that would advise senior military leaders. The development of LLMs
has since increased the relevance of such ideas. Notably, COA-GPT [15] is an
LLM-based tool designed to automate the COA development phase of the joint
planning process [74], and the authors test it against baseline reinforcement
learning methods in StarCraft II with favorable results. Goecks and Waytowich
[16] provide a similar planning tool focused specifically on disaster-relief
operations. However, it is worth noting that both Simmons-Edler et al. [63]
and Hunter and Bowen [28] warn against such systems, the former arguing
against LLM-recommender systems and the latter against AI more generally in
military command decisions. Simmons-Edler et al. [63] cites the potential for
LLMs to escalate and the intractability of their recommendations. Hunter and
Bowen [28] assert that reliance on AI for command decisions may lower the
standards and practice of strategy development. Recent empirical research in
tabletop war games corroborates some of these concerns. Rivera et al. [55]
perform wargaming experiments whereby each nation state is modeled with a
commercial LLM; GPT-4, GPT-3.5, Claude 2, Llama-2 (70B) Chat and GPT-4-Base
are tested. Therein, the authors find that the LLMs tend to develop arms-race
dynamics, leading to greater conflict and, in rare cases, the use of nuclear
weapons. Lamparth et al. [31] similarly compare how LLMs and humans conduct
themselves in a US-China wargame. The authors find that, while the groups
behaved similarly, the LLMs were more aggressive. Additionally, Lamparth et
al. [31] found that prompt verbosity affected LLM behavior. Such findings are
highly relevant to the future of automated wargaming [e.g., see the Snow Globe
system of 26].

It may be argued that these more ambitious efforts rely upon an LLM having
some form of innate knowledge or, at the least, the ability to reflect human
logical reasoning. Diverse perspectives exist in the literature on both of
these topics. Yildirim and Paul [87] discuss the nature of LLM knowledge,
juxtaposing LLM instrumental knowledge with the worldly knowledge of humans.
Saba [58] emphasizes that LLMs cannot be relied upon for factual information
because, as currently trained, factual and non-factual information is not
differentiated. In this light, if an LLM cannot distinguish fact from fiction,
the nature of its knowledge is in question. Moreover, Huang and Chang [27]
consider if and how LLMs execute logical reasoning, concluding that, for LLMs
on the scale of GPT-3 175B or higher, reasoning is an emergent behavior,
despite their difficulties with complex logical tasks. Alternatively, Ellis
[13] argues that efficient human learning is intrinsically linked to their
ability to reason inductively. He claims that out-of-the-box LLMs are
deficient in this task but that this shortcoming can be addressed by layering
a Bayesian model on top of a pre-trained LLM. Setting aside the actual nature
of LLM knowledge and reasoning, many researchers and practitioners seek to
leverage LLMs in various game-playing and strategic decision-making tasks. Xu
et al. [86] and Chen and Chu [10] provide comprehensive surveys, respectively,
that provide further context on LLM use for national security planning.

Finally, in view of the momentous effects associated with national security
operations, we would be remiss if we excluded interpretable, explainable, or
adversarial machine learning from this discussion. Each of these sub-
disciplines is consequential to using LLMs for strategic decision-making. Both
interpretable and explainable machine learning focus on tractability but
through distinct means. Interpretable ML seeks to build simple yet powerful
models, and explainable ML endeavors to identify simple yet faithful
approximations of a black-box model. To date, LLMs are best viewed as a black-
box model, implying that explainable techniques [e.g., see the Deep SHAP
values of 36] are most readily applicable; however, the development of
interpretable image classifiers [e.g., see 9] suggest that the development of
high-performing, interpretable language models may be feasible. Applying
either approach is useful if it can successfully identify undesirable
behavior, e.g., bias in automatic summarization [91]. Alternatively,
adversarial ML focuses on the corruption and defense of ML methods in the
presence of an opponent. In the context of LLMs, training-time attacks can be
used to modify output in accordance with the attacker’s objectives.
Bagdasaryan and Shmatikov [1] provide a concrete example whereby poisoning
attacks to the training data encourage LLMs to spin automatically generated
summaries according to the adversary’s point of view. Deployment-time attacks
against a pre-trained LLM focus on modifying input data to affect a desired
output. Raina et al. [52] explore such attacks against assessment LLMs whereby
a short universal phrase is appended to a phrase to disrupt model performance.
The threat of such attacks to national security is self-evident. If an
attacker can manipulate an LLM that informs command decision-making, traps may
be laid in advance so that their adversary acts per their desires. For
additional information on adversarial ML, we point the interested reader to
Oprea and Vassilev [51].

##  3 Discussion

National security professionals are generally proceeding cautiously with LLM-
based technology. Although some seek to use LLMs as a foundation upon which to
build a virtual Clausewitz, the primary focus of national security
organizations is to use LLMs for natural language tasks, a function in which
their capabilities are well-established. This calculated approach is enshrined
in the strategic documents and international agreements adopted by many
countries. For example, the US Department of Defense [70] set forth a
framework for AI implementation conditioned upon its responsible, equitable,
traceable, reliable, and governable use. Similar tenets are foundational to
the approach toward emerging and disruptive technologies set forth by NATO
[47]. The Political Declaration on Responsible Military Use of Artificial
Intelligence and Autonomy, endorsed by 54 countries, also echoes analogous
themes [72]. In our estimation, such deliberate action is appropriate given
the nascent nature of LLMs and our incipient understanding of their
performance.

Utilizing LLMs for standard natural language tasks, e.g., automatic
summarization, is admittedly less intriguing than their use for generating
automated campaign plans. However, we contend that if these less alluring
functions were embedded across the national security ecosystem, the effects
would be transformational. Defense organizations are characterized by massive
bureaucracies. In these bureaucracies, many offices are tasked with
aggregating information and distilling insights to send to the next level in
the bureaucratic hierarchy. Others are responsible for aggregating
information, proposing courses of action for a commander’s decision, and
disseminating these decisions via written documentation. When conducted
exclusively by humans, these tasks are incredibly slow and cumbersome. Despite
the adoption of information-age technology, many defense organizations have
struggled to effectively manage data [71], forcing staffs to rely on manual
inquiry and collection. Moreover, many national security operations,
particularly those below armed conflict, inhibit the development and use of
structured databases. National objectives are often qualitatively defined
without a clear quantitative analog [e.g., see the Hamlet Evaluation System
discussed by 45], allocated resources may rapidly change due to shifting
national priorities, and personnel are transient (e.g., service-member
deployments).

Much of the data available to national security organizations is thus
unstructured, often in the form of text or images. Current practices require a
human to ingest, summarize, and distill insight from this information, thereby
limiting decision-making to the speed of human comprehension. Therefore, LLM-
based automatic summarization may be particularly well-suited for national
security applications. Such models can ingest and summarize a significant
amount of unstructured information much quicker than their human counterparts.
Alternatively, in use cases where structured databases exist, LLMs may be used
as a component within broader systems that translate text inquiries into code,
thereby allowing a layman to readily interact with data. For example, a non-
technical analyst may enter a text query that triggers the LLM to generate
structured query language (SQL) code for data set creation, R code for
visualization, and text suggestions for more follow-up statistical analyses.

Integrating LLMs with alternative probabilistic, statistical, and ML methods
can further streamline information processing and analysis by breaking down
queries into manageable steps and leveraging external tools for precise
calculations. For instance, combining LLMs with statistical forecasting
methods can enhance the accuracy of intelligence predictions, and using LLMs
alongside supervised ML techniques can improve data classification and
analysis. Moreover, through interaction with an LLM that has access to the
relevant information, an analyst can conversationally explore a compendium of
structured and unstructured data to derive novel insights. By facilitating
knowledge-based work, LLMs may allow commanders to reduce their bureaucratic
footprint and reallocate personnel from administrative to operational roles.
LLMs may also enable the deconstruction of information silos fashioned within
the bureaucracy due to limited human awareness and communication. Coupling
these potentialities with the speed of LLM processing, national security
organizations may be able to rapidly accelerate and improve their decision-
making cycles; Sundar [65] provides an example in USAF operations wherein an
LLM needed 10 minutes to complete a task that typically requires days of man-
hours.

Nevertheless, hallucinations remain a potential threat, and this risk must be
appropriately mitigated. Recent research by Nahar et al. [46] suggests that
simply warning users about hallucinations improves their ability to detect
them. Future academic studies should also compare LLM-induced hallucinations
to errors induced by transmission chaining in large bureaucracies; such
studies would provide a baseline to determine the degree to which LLM
hallucinations deviate from errancy in the status quo. Alternative comparative
studies could view the problem from a decision-theoretic perspective,
investigating how well each method summarizes data and how this affects
decision-making. LLMs may fall short of expectations, or their utilization in
national security staffs may evoke unforeseen consequences; however, they hold
great promise in expediting sluggish bureaucratic processes.

Conversely, the direct use of LLMs for real-world, strategic-level planning is
more fraught. Recently developed LLMs have shown “sparks” of artificial
general intelligence (AGI) [6], obtaining near human performance on tasks from
a variety of fields (e.g., medicine, law, and psychology); however, we contend
that national security planning is a function of a different character. No
textbook contains the right answers from which an LLM can learn. The work of
military historians, along with the treatises of Sun Tzu, Jomini, and
Clausewitz, contain valuable information that may allow an LLM to craft a
written strategic document comparable to the status quo. However, such
documents are aspirational and are useful insofar as the humans executing the
strategy achieve unified action across domains throughout the strategic,
operational, and tactical levels of command. Achievement of this goal has
eluded even the most skilled and experienced national security leaders. Thus,
we are skeptical that a recipe for its attainment is embedded even within even
the whole of written human text. That is not to say that AI cannot attain
superhuman abilities for the task or that LLMs are not the building blocks to
achieve it, but we believe that more than sparks of AGI would be required.
Alternatively, we are more optimistic about utilizing contemporary LLMs to
train national security strategists and planners. Their use in wargaming
allows for individual study and personalized instruction heretofore lacking in
professional military education. Automated, qualitative wargames [e.g., see
26] may be calibrated for specific scenarios and adversaries, transcripts may
be reviewed later with instructors to facilitate learning, and students may
replicate the same wargame autonomously to rectify their mistakes. We find
this last feature of replication particularly compelling. Military planners
often struggle to cope with the vast degree of uncertainty inherent in their
operations and do not understand the distinction between decision and outcome
quality. If properly executed during wargame training, LLMs may be able to
partially rectify these issues.

Based on the widespread efforts by national security organizations, we believe
LLMs will further the AI-induced change in the character of conflict. In
particular, LLMs will disrupt the status quo under the threshold of armed
conflict. This is already being seen through their use in the development of
nation-state-sponsored propaganda. Disinformation campaigns are becoming
cheaper at scale, and automated propaganda is harder to discern. This trend
will only continue as LLMs are paired with alternative generative AI methods
(e.g., those creating images and videos). Moreover, as LLMs are incorporated
into national security processes, they will further expand the relevance of
adversarial machine learning and instantiate a new cyber-warfare arena. The
black-box nature of existent LLMs increases this threat, making the
incorporation of interpretable and/or explainable models imperative to
enabling attack-and-vulnerability detection. Given the breadth of contemporary
LLM initiatives and applications, the aforementioned threats and mitigation
efforts are relevant across the competition continuum.

##  4 Conclusion

Integrating LLMs into national security operations presents unprecedented
opportunities and significant challenges. As evidenced by their varied
applications across the DoD and other world defense organizations, LLMs have
the potential to revolutionize the efficiency (and effectiveness) of national
security operations. The potential benefits are substantial. LLMs can automate
and accelerate information processing, enhance decision-making through
advanced data analysis, and reduce bureaucratic inefficiencies. Their
automatic summarization capabilities can streamline the creation of
operational documents and reports, while their integration with probabilistic,
statistical, and ML methods can improve accuracy and reliability, e.g.,
combining LLMs with Bayesian techniques can provide more robust threat
predictions. Conversely, deploying LLMs into national security organizations
does not come without risks. More specifically, the potential for
hallucinations, the ensuring of data privacy, and the safeguarding of LLMs
against adversarial attacks are significant concerns that must be addressed.
These risks are particularly concerning in high-stakes decision-making
environments where the accuracy and integrity of information are crucial
(e.g., armed conflict). Addressing these challenges is critical to protecting
sensitive information, preventing malicious exploitation, and avoiding
potential national security catastrophes.

The broader implications of LLM integration into national security
organizations are profound. Beyond immediate defense applications, LLMs have
the potential to influence strategic planning, international relations, and
the broader geopolitical landscape. The purported ability of nations to
leverage LLMs for disinformation campaigns and cyber operations emphasizes the
need to develop appropriate countermeasures and continuously scrutinize and
update AI security protocols. While LLMs have demonstrated “sparks” of AGI, we
contend that their current capabilities are best suited for supporting roles
rather than leading strategic decisions. Their use in training and wargaming
can provide valuable insights and personalized learning experiences for
military personnel, helping to bridge knowledge gaps and improve operational
readiness.

A cautious and calculated approach must be taken by national security
professionals with regard to integrating LLMs into their operations.
Deliberate integration, guided by frameworks for responsible AI use, is both
appropriate and necessary for harnessing the potential benefits of LLMs while
mitigating associated risks. Moving forward, continued research and
collaboration between defense, academic, and commercial entities is essential
to fully realize the benefits of LLMs. National security professionals must
ensure that, while they explore how these powerful tools can be leveraged to
enhance national security capabilities, they simultaneously focus on
safeguarding against potential threats. This balanced approach will enable
them to navigate the complexities of AI integration and establish strategic
advantage in an increasingly contested and technologically advanced world.

## Disclaimer

The views expressed in this article are those of the authors and do not
reflect the official policy or position of the United States Air Force, United
States Department of Defense, or United States Government. This work is
approved for public release (distribution unlimited) in accordance with PA#
USAFA-DF-2024-514.

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