⚡ Quick Summary
Published by Convergence Analysis, this report presents the AI Readiness Objectives (AROs): seven broad, aspirational goals intended to represent the objectives of all or most AI-security interventions. It defines AI readiness as a sufficient combination of interventions to prevent or mitigate intolerable risks to national security, the economy and the public, while recognising that risk tolerance and concrete requirements differ between jurisdictions.
The framework orders the seven objectives from upstream to downstream across three Pillars: developing secure AI, ensuring deployed AI is secure, and societal resilience. Each Objective contains intervention types and is linked in the interactive version to three to five touchstone resources for each intervention. The report also adds six preliminary AI Innovation Priorities for examining security–innovation trade-offs. Its second iteration drew on 83 sources, approximately 400 manually harmonised interventions, and feedback from 33 experts. It is intended to help actors identify what needs to happen, sequence interventions, and develop or stress-test comprehensive AI-security strategies; forthcoming case studies are expected to apply it to particular jurisdictions.
🧩 What’s Covered
The report moves from the problem of readiness to the framework, its uses, its development and its acknowledged constraints.
- The readiness challenge: Defines AI readiness and identifies threats including CBRN incidents, sophisticated cyber, influence and military operations, extreme economic disruption and AI control failures. It frames the task as setting objectives, identifying measures, and combining and sequencing them under fast-changing capabilities.
- AROs Framework: Sets out seven objectives in three upstream-to-downstream Pillars. “Develop Secure AI” covers technical means and standards; “Ensure Deployed AI is Secure” covers public visibility, public capacity to govern AI and deployment safeguards; “Societal Resilience” covers resilience to major disruptions and catastrophe.
- Intervention examples: The framework graphic specifies areas such as interpretability, alignment, AI control, evaluations and red-teaming; responsible scaling policies, safety cases and post-deployment monitoring; registration, data provenance, verification and incident monitoring; auditing ecosystems, risk modelling, licensing, compute controls, pauses and red lines.
- AI Innovation Priorities: Adds six considerations—encouraging development and AI diffusion, reliable systems, AI for public good, limiting capability caps, and decentralised authority—to analyse how security measures may support or undermine distinct aspects of innovation.
- Audiences and strategic applications: Identifies researchers, advocates and funders as primary users, and describes uses by policymakers, analysts, legislators, advocacy groups, research organisations, international bodies and standards bodies. It maps AROs against approaches including controlled development, defensive acceleration, red lines, international regulation, moratoria, strategic advantage and MAAIM.
- Relationship to other frameworks: Compares AROs with the Peregrine Report, MIT Risk Mitigation Database, AI Readiness Index, Project GRASP and OECD.AI Policy Navigator, positioning AROs as an objective-based complement to intervention, risk or policy catalogues.
- Method, limitations and future work: Describes literature review, expert consultation and manual grouping into 45 intervention categories. It records limitations in alignment and evaluation research, uncertain societal impacts, changing capabilities and incomplete interventions, then outlines US and EU case studies, indicators and an interdependency tool.
💡 Why it matters?
For people designing national AI-security strategies, the framework supplies a common structure for checking whether a proposed package of measures covers more than one preferred intervention or risk. It distinguishes upstream work on secure development from visibility, public governing capacity, enforcement and resilience when prevention fails.
The report also connects strategy choices to operational prerequisites. For example, it states that an international regulator requires standards, visibility, technical capacity and enforcement mechanisms. Its Innovation Priorities provide a separate lens for considering trade-offs between security interventions and a jurisdiction’s AI innovation ecosystem.
❓ What’s Missing
The report does not set quantitative thresholds for adequacy under any of the seven Objectives; it leaves these to users because jurisdictions have different requirements and resources. It is descriptive rather than prescriptive, does not recommend a particular degree of international cooperation, and does not provide a complete taxonomy of mutually exclusive intervention categories. The authors acknowledge that alignment and evaluation research have significant limitations, societal impacts are difficult to measure, measures can become insufficient within 12 months or less, and new interventions may not yet be represented. Detailed interactions between interventions, measurable indicators, and jurisdiction-specific recommendations are identified as forthcoming work rather than delivered here.
👥 Best For
This is best suited to policy researchers, advocates and funders prioritising AI-security projects, and to government policy teams assessing preparedness across development, deployment governance and resilience. It is also useful for analysts comparing the coverage and prerequisites of strategies such as red lines, international regulation or controlled development and deployment.
📄 Source Details
The AI Readiness Objectives Towards Sufficiency in National AI Security Strategies is a 23-page English report by GWYN GLASSER and ELLIOT MCKERNON, bearing the Convergence Analysis name on its cover. It describes the framework as its second iteration and as a living document. No publication year is printed. The cover and framework pages print aireadinessobjectives.com.
