The Impact of AI on Global Security Policies: A Paradigm Shift?

AI has become a subject of national-security policy. The harder question is whether a policy changes what institutions actually do: how they procure a system, investigate its failures, restrict its use and explain their decisions. An announcement is evidence of an intention. It is not yet evidence of an outcome.

This distinction matters because security-policy debate often moves quickly from a technical demonstration to a claim about national advantage. We need to see the intervening decisions. Which use is proposed? Who bears the risk? What comparison establishes value? What happens if the system performs differently from its evaluation?

Date the policy before interpreting it

The US national-security memorandum on AI discussed in the original article was issued on October 24, 2024, not in October 2023. Its archived text is a useful historical example of an administration connecting AI development with national-security objectives.¹

It should not be presented as a statement of every policy currently in force. Administrations and legal requirements can change. A historical document can show the direction of a debate without establishing the present obligations of every organisation.

The analytical question is what a policy makes possible and what it requires. Does it give a reviewing body authority? Does it specify an evaluation or reporting process? Are the responsible organisations identified? Those questions help distinguish an operational change from a broad aspiration.

Detection statistics are not national-security outcomes

Precise percentages for improved detection and mitigation are difficult to interpret without a sample, method and operational context. They should not imply that AI has produced a comparable improvement across national systems. A result can be valid within a particular experiment and still fail to support that inference.

For any proposed adoption, I recommend asking for the baseline, the type of incident, the cost of missed threats and unnecessary interventions, and the conditions outside the test. Security decisions require attention to consequences as well as a percentage correct.

The same applies to adversary capability. The availability of a tool is different from its successful use in a consequential operation. A policy should distinguish demonstrated incidents, plausible assistance and future scenarios. Each may warrant attention, but they support different conclusions.

The institution is part of the system

A model can be technically capable while an organisation remains unable to use it responsibly. Staff may lack the time to review outputs, authority may be unclear, or procurement may make replacement difficult. Those conditions deserve as much scrutiny as a model score.

I would require a deployment record identifying the intended function, permissions, responsible owner, unresolved limitations and conditions for suspension. The record should also identify dependencies on vendors and external services. Responsibility should remain legible when a system crosses organisational boundaries.

NIST’s Cybersecurity Framework 2.0 offers an outcomes vocabulary that includes governance and recovery alongside other security functions. It can structure these discussions, without proving that the institution has achieved the outcomes.²

International cooperation needs specific subjects

Calls for cooperation are more useful when they name a problem: incident communication, evaluation terminology, misunderstanding in a crisis or the protection of sensitive information during testing. Agreement about every use of AI is unlikely to be a prerequisite for progress on every narrower subject.

UNIDIR’s confidence-building analysis provides a research basis for examining such questions. Its proposals are not themselves a binding agreement between states.³

My recommendation is to begin with defined activities whose participation and results can be described. Specify what information is shared, which institutions take part and how a concern reaches someone able to act. A forum’s existence should not be counted as proof that strategic distrust has been resolved.

What would count as a paradigm shift?

A credible shift would be visible in changed decisions: deployment being paused after a failed assessment, procurement responding to documented limitations, or incident findings leading to a corrected workflow. Those are proposed signs to look for, not accomplishments asserted about a named government.

The strongest policy will combine ambition with a willingness to revise. It should explain which benefits have been demonstrated, which risks remain uncertain and who can change course. Without that account, a paradigm shift may amount to a new vocabulary attached to familiar institutional weaknesses.

Sources and notes

1. US President, Memorandum on Advancing the United States’ Leadership in Artificial Intelligence … (October 24, 2024), archived Biden White House. A historical policy document; no assertion is made that every provision remains in force. ↩
2. NIST, The NIST Cybersecurity Framework 2.0 (February 2024). An outcomes framework, not evidence that a particular AI product improves security. ↩
3. Ioana Puscas, AI and International Security: Understanding the Risks and Paving the Path for Confidence-Building Measures, UNIDIR (October 2023). Research analysis and proposals, not a binding international agreement. ↩