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Cybersecurity could help U.S. AI companies earn trust and win international customers, but it is a potential advantage—not a proven secret weapon. In a January 30, 2026, CyberScoop opinion article, David E. Wade and Courtney Manning argue that secure, AI-powered cloud services could distinguish U.S. offerings in competition with China. The case is plausible, but the article does not provide a like-for-like assessment showing that U.S. firms are more secure or that cybersecurity will decide the AI race.

Why cybersecurity could matter in the AI race

AI competition is not only about who builds the most capable models. Organizations and governments also have to decide whether they can safely use those models and the cloud systems around them. Security failures can expose data, disrupt operations, or undermine confidence in AI deployments. If U.S. providers can demonstrate dependable protection, that may make their services more attractive to customers weighing where to place sensitive workloads.

Wade and Manning frame that possibility as a national competitiveness strategy: advanced models are necessary, but trust and security could help differentiate American AI offerings. Their argument centers particularly on AI-powered cloud security and its potential as an export. It is a policy thesis, not a comparative evaluation of U.S. and Chinese systems or evidence that security already gives the United States a lead.

What “secure AI” means in practice

Security is broader than protecting a model from a single attack. NIST says AI security overlaps with established software and hardware concerns, including the confidentiality, integrity, and availability of systems and their data, as well as risks from adversarial machine learning. In practical terms, a provider has to consider the infrastructure, data, model, and surrounding applications—not just the model’s output.

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NIST identifies security and resilience as characteristics relevant to trustworthy AI. Its AI Risk Management Framework is a voluntary resource for managing risks across the design, development, use, and evaluation of AI systems. NIST also says the framework is being revised and describes work toward a trustworthiness profile for AI in critical infrastructure. These resources offer ways to organize risk management; they do not certify that a system is secure or establish that one country’s providers are better than another’s.

Why AI agents add a new security challenge

AI agents make the question more immediate because they can take actions as part of a workflow, rather than only generate responses. NIST’s May 18, 2026, analysis of responses to a request for information on AI-agent security reports broad agreement that agents bring novel threats and that existing cybersecurity principles may need adaptation. Respondents pointed to implementation guidance, information-sharing, and standards as possible areas for government action.

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That analysis supports a practical point: security has to keep pace with how AI is deployed. A trustworthy brand or national origin cannot substitute for controls that address the particular system, data, and actions involved. NIST’s findings describe concerns raised by respondents; they are not proof that any single security approach has solved them.

Does the U.S. outspend China on cybersecurity?

Wade and Manning report that the United States accounts for roughly 40% of global cybersecurity spending, compared with closer to 3% for China. Those figures should be treated as the authors’ reported comparison: their article does not set out the underlying dataset, year, or definitions, and the available linked market source does not independently verify the country-level spending figures.

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Fortune Business Insights reports that North America represented 43.0% of the global cybersecurity market in 2025. That is a regional market-share estimate, not a U.S.-only spending figure and not a comparison with China. The two measures cannot be substituted for one another. A sound U.S.-China comparison would need to align the year, geographic boundaries, and definitions—such as spending versus market revenue and public versus private investment.

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What the authors propose—and what remains unproven

Wade and Manning recommend a set of policy measures intended to expand U.S. secure-cloud and cybersecurity exports. These are proposals in an opinion article, not evidence of enacted policy or demonstrated results.

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  • Targeted tax credits: encourage investment in secure cloud infrastructure.
  • Faster GPU sales for defensive cyber use: ease access to computing hardware for cybersecurity applications.
  • Export financing: help U.S. companies sell AI and cloud cybersecurity offerings abroad.
  • Technology diplomacy: make security and trust part of international engagement.
  • Streamlined agreements: improve arrangements covering data transfers, cloud services, and security.

NIST’s AI standards work also describes international coordination as part of developing standards. Participation can help establish common expectations and interoperability, but standards leadership alone does not guarantee market dominance or customer adoption.

How to judge whether cybersecurity becomes a real advantage

The central claim is testable, but the evidence cited by the op-ed does not settle it. A fair comparison of U.S. and Chinese ecosystems would use matched measures, including:

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  • Cybersecurity spending or market revenue, with the same year and definition for both countries.
  • Defensive product and service capabilities, evaluated on comparable terms.
  • Real-world deployments and independent security evaluations.
  • Transparency around incidents and vulnerability disclosure.
  • Adoption by international customers, rather than spending figures alone.

Until such evidence is available, the careful conclusion is that cybersecurity could be one component of U.S. AI competitiveness. It is not established as America’s decisive edge over China.

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