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The increasing integration of artificial intelligence (AI) into cybersecurity has reshaped national security
strategies worldwide. This study investigates AI readiness in national cybersecurity strategies through a
comparative analysis of three distinct governance models: the United States, China, and the European Union
(represented by France). Using a systemic document review methodology, the research provides a comparative,
cross-country analysis of AI readiness within national cybersecurity strategies. The results reveal three divergent
strategic approaches to AI readiness: a public-private, market-driven ecosystem in the United States that fosters
rapid innovation but can lead to fragmented national strategy; a top-down, state-led approach in China that
enables rapid resource mobilisation and large-scale data collection at the potential cost of individual liberties;
and a regulation-first framework in the European Union that prioritises ethical integrity and public trust. The
analysis further found that a nation’s economic maturity and human capital are foundational to its capacity for
AI readiness, regardless of its governance model. The discussion highlights a fundamental trade-off between the
speed of innovation and ethical oversight, arguing that an effective national strategy must be holistic and
adaptive, combining policy guidance with targeted investment in technology, talent, and international
collaboration.
AI readiness, cybersecurity, national strategies, United States, China, European Union, Comparative
analysis, governance models.
Cybersecurity is a collection of tools, procedures, and methods used to prevent attacks, damage, and illegal
access to networks, devices, software, and data (Perwej et al., 2021). The notion of “AI readiness” in national
cybersecurity strategies refers to a state where strategic documents not only acknowledge the risks and benefits
of AI but also systematically embed institutional, technical, regulatory, and capacity mechanisms to anticipate,
prevent, detect, respond to, and recover from AI-enabled cyber threats. AI readiness encompasses dimensions
such as governance frameworks specific to AI security, institutional coordination, human capital and skills,
research and development, data infrastructure, threat intelligence sharing, and regulatory oversight (Ajani et al.,
2025; Al Dajeh, 2024; Isagah and Dhaou, 2024).
The increasing integration of artificial intelligence (AI) into various sectors of society has created a new
paradigm for national security, particularly in the domain of cybersecurity (Kaur et al., 2024). AI’s capacity for
rapid data analysis, pattern recognition, and autonomous decision-making presents both a powerful tool for
defence and a potent weapon for malicious actors (Adewale, 2025). Nations are grappling with a dual challenge:
leveraging AI to enhance their cybersecurity postures while simultaneously defending against AI-driven threats
(Khan et al., 2025). There have been attempts to use AI technology to anticipate and identify different types of
cyberattacks. For over ten years, the security sector has been using AI to develop a system that analyses,
disseminates, and defends attack information to withstand changes in attackers (LG CNS, 2016). But ten years
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ago, harmful intrusions were less varied than they are now. As a result, efforts to use AI technology for
cybersecurity have gotten little attention.
Furthermore, the intrusion detection and attack analysis system was able to completely protect against these
attacks thanks to the pattern matching technique. However, the modern cyber risks brought on by cybersecurity
problems are significant in both number and breadth compared to the scenario ten years ago (Yoo, 2017). There
will probably be an even greater rise in these assaults. Because new information and communications technology
(ICT) businesses are constantly emerging, cyberattacks have gotten more sophisticated, well-organised, and
varied, making them more successful than in the past. To properly respond to this development in cyber-attacks,
a variety of technological and administrative solutions and response systems have become increasingly required.
People have also asked how cyberattacks react and whether it’s feasible to predict how they will change every
day. Or can unexpected Black Swans—which, once they happen, seriously harm systems—be identified and
addressed beforehand? The necessity of using AI technology, which is expected to offer solutions to these
queries, has just come to light (LG CNS, 2016).
Machine learning (ML) is one of the most often used terms to characterise AI applications in cybersecurity and
is a key component of the upcoming cybersecurity defensive frontier (Perlman, 2019). Security control, threat
detection, and prevention are just a few of the cybersecurity domains where ML technology is being used in
research and applications. Additionally, it offers a prompt, potent, and proactive real-time reaction to cyberthreats
(Kumar, 2019). Cybersecurity is made easier, more proactive, and more efficient by these security services
(Perlman, 2019). Three broad categories may be used to group these safe machine learning problems and
solutions. The primary applications of this technique are in problem solving, including decision trees, regression,
diagnostics, classification, and recognition. The technique is applied in the realm of cybersecurity for virus
detection, spam filtering, and network traffic analysis. Second, unsupervised learning is a method of learning
just by posing problems. It is most effective when it comes to finding features and is mostly utilised for clustering
for network anomaly detection, malware identification, and user behaviour analytics. Third, learning by
evaluating results is known as reinforcement learning. By using this technique, ML agents can acquire behaviour
through encounters in environments that resemble games (Bommelaer et al., 2017).
Despite the policy salience of AI in cybersecurity, comparative academic work remains sparse. Many existing
national cybersecurity strategy assessments mention AI among emerging technologies (Kharbanda et al., 2023)
but do not systematically evaluate how deeply AI is integrated into policy instruments. This study aims to provide
a comprehensive cross-country comparative analysis of AI readiness within national cybersecurity strategies. By
examining a diverse set of countries, this paper seeks to identify key commonalities and divergences in their
strategic approaches. The findings are relevant for cyber policymakers, international development agencies, and
scholars of AI governance and cybersecurity.
This study employs a systematic documentative review methodology to analyse and compare national strategic
approaches to the intersection of Artificial Intelligence (AI) and cybersecurity. This approach allows for a deep,
contextual understanding of the policies and frameworks adopted by different nations, moving beyond simple
data aggregation to a nuanced analysis of strategic intent, priorities, and implementation models. The study
selected a purposive sample of countries representing diverse geopolitical, economic, and technological
landscapes. The samples include 1) a major technological power with a market-driven approach (United States),
2) a state with a centralised, government-led strategy (China), and 3) a regional bloc with a strong regulatory
focus (European Union, using a representative member state as a case study). This selection allows for the
examination of distinct policy models and their impact on AI readiness.
The primary sources for this review were official government documents, including AI national strategies,
cybersecurity strategies, policy white papers, and legislative texts. To ensure a comprehensive search, the
following keywords and phrases were used in various combinations across official government websites, think
tank publications, and academic databases (e.g., Google Scholar, Scopus, Web of Science, IEEE Xplore, and
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PubMed): using keywords such as “AI readiness”, “national cybersecurity strategy”, “artificial intelligence AND
cybersecurity policy”, and “comparative cybersecurity strategies”. The search was restricted to English-language
documents but included countries from diverse regions to maximise comparability.
The documents were screened for inclusion based on the following criteria:
Documents are widely recognised reports from reputable international organisations
or government publications directly addressing the national strategy for AI and/or cybersecurity. The
documents are published between 2015 and 2025 to ensure relevance and currency. Also, documents had
to be available in English.
Documents were excluded if they were journalistic articles, opinion pieces, or
preliminary drafts not formally adopted as policy. Documents that focused solely on general technology
policy without explicit mention of AI and cybersecurity were also excluded.
The selection process followed a two-stage approach. In the first stage, the titles and abstracts of the retrieved
documents were screened for relevance against the eligibility criteria. This initial screening was conducted by
the researcher to quickly identify a broad set of potentially relevant sources. In the second stage, a full-text
review of the selected documents was performed to confirm their relevance and extract key data points related
to the study. Any uncertainties during the selection process were resolved through discussion.
The quality and reliability of each source were assessed to mitigate bias and ensure the validity of the findings.
The primary quality metric was the document’s official status, with official government white papers and
legislative acts being given the highest weight. Reports from independent and well-regarded think tanks were
also considered highly reliable. Documents were also evaluated based on the clarity of their methodology and
the presence of empirical evidence to support their claims. This ensured that the analysis was based on the most
authoritative and credible sources available.
The cross-country comparison revealed significant variations in AI readiness across the selected cases—the
United States, China, and the European Union (represented by France)—largely driven by differing strategic
priorities and governance models. These differences manifest in technological capability, threat perception, and
the ethical and legal frameworks governing AI development and deployment.
In the United States, AI readiness is primarily propelled by a dynamic public-private ecosystem, a model built
on decentralised innovation and market-driven incentives. The government’s role is often that of a catalyst and
facilitator, fostering innovation through substantial grants and research programmes (Dimitriadis, 2025). A prime
example is the Defence Advanced Research Projects Agency (DARPA), which has historically funded
foundational AI research that later finds its way into commercial applications, from autonomous systems to
natural language processing (Ledbetter, 2022; U.S. Department of War, 2025). This approach has led to a highly
innovative environment where technological capability is extremely high, with a global concentration of leading
AI and cybersecurity firms.
This decentralised model, however, also presents challenges. The lack of a single, overarching national strategy
can lead to a fragmented approach to AI adoption and governance (Themann, 2025). While the Department of
Defence (DoD) might have advanced AI initiatives, a civilian agency’s readiness may lag. This fragmentation
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can also create gaps in national cybersecurity posture, as different sectors—from finance to energy—develop
their own security protocols with varying degrees of maturity (Clapp, 2025). Threats are often in terms of great-
power competition and the protection of intellectual property and critical infrastructure from foreign adversaries,
as articulated in documents like the National AI Initiative Act of 2020. The emphasis on intellectual property
protection is a direct reflection of the private sector’s dominance in AI R&D, with government policy serving to
safeguard these investments (White & Case 2024).
Consequently, this public-private dynamic has yielded an ecosystem where innovation is rapid, but regulation is
often reactive. The development of AI-driven cybersecurity tools is largely in the hands of private companies,
with the government, while agile, sometimes prioritising commercial viability over robust, comprehensive
security standards, creating a tension between speed and safety.
China presents a stark contrast with its top-down, state-led strategy, as outlined in its “Next Generation Artificial
Intelligence Development Plan” 2017 (Wong et al., 2024). The government has designated AI as a national
priority, with extensive state funding and policy directives aimed at achieving global leadership in the field by
2030 (Roberts et al., 2019). This centralised control allows for the rapid mobilisation of resources and large-
scale data collection, which is crucial for training complex AI models. China’s ability to mandate data sharing
across sectors and build massive, centralised data lakes gives it a significant advantage in areas like computer
vision and natural language processing (Peredy et al., 2022). As a result, China shows a high degree of integration
of AI into its national security apparatus, particularly in areas of surveillance, defence and social governance.
The use of AI for public safety, urban management, and predictive policing is a key component of its national
strategy (Raji and Sholademi, 2024). This model, however, raises significant ethical and privacy concerns, as
the collection and use of citizen data are subject to state control rather than individual consent (Pina et al., 2024).
The threat perception is heavily focused on maintaining internal stability and countering external influence, with
AI technologies being viewed as critical tools for social control and national defence (Gilbert and Gilbert, 2024).
The state's top-down approach allows for rapid deployment of AI-driven cybersecurity systems at a national
scale, but it also means that the same systems can be used to monitor and control citizens (Mary, 2025).
Ultimately, it has been shown that this model prioritises collective national goals over individual rights, which
fundamentally shapes the nature of its AI readiness. While it achieves remarkable speed and scale, the lack of
independent oversight and public debate on ethical implications means that the development path is highly
controlled and less responsive to external criticism.
The European Union, represented here by a nation like France, exhibits a regulation-first approach to AI
readiness. The model is best exemplified by the EU AI Act, which focuses on managing the risks associated with
AI by categorising applications based on their level of risk and setting stringent requirements for high-risk
systems (Gstrein et al., 2024). For instance, AI systems used in critical infrastructure or law enforcement face
the most rigorous standards, including mandatory human oversight and data governance (Cate, 2025).
Additionally, technical development is strong, especially in countries like France and Germany with robust
research institutions and engineering talent; the pace of adoption can be slower due to compliance burdens (Skare
and Soriano, 2021). Public-private partnerships are encouraged, but within a highly structured and regulated
environment where data privacy (enforced by GDPR) and ethical guidelines are paramount (Pina et al., 2024).
The threat perception in the EU is often rooted in protecting democratic values and citizen data privacy, with a
focus on preventing AI from being used to manipulate public discourse or infringe upon fundamental rights
(Cupać and Sienknecht, 2024). Thus, this approach contrasts sharply with the US model’s market-driven focus
and China’s state-led control, placing the EU as a global leader in the ethical governance of AI. While this may
slow down commercialisation, it aims to build public trust and ensure AI is developed and used in a way that
aligns with democratic principles (Jørgensen and Ma, 2025). Hence, the focus is not just on technological
capability but on responsible technology capability.
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The findings from this cross-country comparison reveal that AI readiness is not a monolithic concept but a
multifaceted outcome shaped by district national governance models and strategic priorities. The results
underscore a fundamental trade-off between speed of innovation and safety and ethical oversight, with each
nation’s chosen model reflecting its core values and perceived national interest. While the United States'
decentralised, market-driven approach has fostered rapid technological advancement, it has also led to a
fragmented national strategy (Themann, 2025). Conversely, China’s state-led, top-down model enables
remarkable speed and scale at the potential cost of individual liberties (Peredy et al., 2022). The European
Union’s regulation-first framework, in turn, prioritises ethical integrity and public trust, which may slow down
adoption but aims to create a more sustainable and equitable AI ecosystem (Balcioğlu et al., 2025).
The central tension identified in this result is the divergence in how nations approach the governance of
technological power. In line with research by Polyakov et al. (2024), the U.S. model, rooted in a tradition of
private sector innovation, views government primarily as an enabler and a customer. This system is highly
effective at fostering groundbreaking research and development and is unparalleled in its ability to
commercialise new technologies. However, the lack of a unified, comprehensive national strategy can create
critical gaps in cybersecurity posture, leaving various sectors vulnerable to attacks (Adegbite et al., 2023). Thus,
the threat perception is external and economic, focusing on intellectual property theft and great-power
competition.
In contrast, China’s model demonstrates the immense power of a centralised, state-led approach. The
government’s ability to direct resources and mobilise a massive workforce towards a single national objective—
global AI leadership—is a significant strategic advantage. In line with the works of Zou and Zhang (2025) and
Roberts et al. (2020), this centralised control facilitates the rapid and large-scale deployment of AI systems,
particularly for internal security and social governance. The primary threat perception is internal, centred on
maintaining stability and controlling information. This model, while efficient, presents a fundamental challenge
to global norms on privacy and human rights, leading to a significant ethical debate on the responsible use of AI.
The European Union’s approach offers a third, distinct pathway. By prioritising a human-centric, regulation-first
framework, the EU seeks to set a global standard for the responsible development of AI (Arora et al., 2025;
Yazici, 2025). The EU AI Act is a landmark effort to mitigate risks and ensure that AI systems are trustworthy
and accountable. This focus on regulatory integrity, however, introduces a different kind of trade-off (European
Parliament, 2025). While it builds public trust and may lead to more robust, ethical systems, the compliance
burdens can slow the pace of innovation and adoption compared to the more agile U.S. and Chinese models. On
the other hand, the EU’s threat perception is uniquely focused on protecting democratic values and citizen data
privacy from both state and corporate overreach.
Ultimately, the analysis reveals that a nation’s AI readiness is not simply a function of its governance model but
is deeply intertwined with its underlying economic and social foundations. The findings highlight that regardless
of whether a country is market-led, state-led, or regulation-first, a strong economic base foundational element—
significant GDP, R&D investment, and a skilled workforce—is the primary determinant of a nation’s capacity
to develop and implement sophisticated AI technologies. This suggests that the global AI landscape is likely to
see a divergence of these three distinct models, creating a competitive environment where each system’s
strengths and weaknesses will be tested in real time
Based on this comparative analysis, the following recommendations are proposed for nations seeking to enhance
their AI readiness in cybersecurity:
1. Nations should move beyond treating AI as a mere technological tool and instead integrate it as a central
pillar of their national cybersecurity strategy. This strategy should clearly define roles for government,
industry, and academia and outline a vision for leveraging AI for both defensive and offensive cyber
capabilities.
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2. A nation’s greatest asset in the AI era is its people. Hence, governments should invest in robust
educational programmes to create a pipeline of AI and cybersecurity professionals. This includes
fostering STEM education from an early age, supporting advanced university research, and implementing
reskilling initiatives for the current workforce.
3. Government cannot address the AI-cyber challenge alone. They must actively partner with the private
sector, which often holds the most advanced AI technology and talent. This can be achieved through an
environment that incentivises innovation while ensuring security.
4. While regulation is necessary, an overly restrictive approach can stifle innovation. Nations should
consider a risk-based framework like the EU’s, where high-risk AI applications are subject to strict
oversight, while low-risk applications are given more freedom to innovate.
5. Given the global nature of cyber threats, no country can be truly secure in isolation. Nations must actively
participate in international forums, share best practices, and work together to establish global norms and
standards for the ethical and responsible use of AI in cybersecurity. This includes collaboration on AI-
driven threat intelligence sharing and joint vulnerability research.
This cross-country comparative study demonstrates that AI readiness in national cybersecurity is a complex,
multi-dimensional issue. It is not simply a matter of technological prowess but is deeply influenced by a nation’s
governance model, strategic priorities, and human capital. While the United States, China, and the European
Union represent distinct approaches, their experiences offer valuable lessons for all nations. A comprehensive
and effective strategy for AI readiness in cybersecurity must be holistic, combining clear policy guidance with
targeted investments in technology, talent, and international collaboration. Moving forward, the race for AI
dominance in cybersecurity will not be won by the country with the most advanced technology alone, but by the
one with the most intelligent and adaptive strategy.
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