APEC Forum Backs Responsible AI Adoption and Secure Open Source

APEC member economies issued a statement after the July 24 High-Level Forum on AI in Chengdu calling for secure AI infrastructure, responsible adoption, AI literacy, cross-border data flows, and open-source projects with strong security assurance. The statement advances regional cooperation and capacity building, but it does not create a binding Asia-Pacific AI rulebook.
APEC member economies issued a statement after the High-Level Forum on AI held in Chengdu, China, on July 24. The document calls for cooperation on secure infrastructure, responsible adoption, skills, cross-border data flows, open-source development, and broader participation in the AI economy.
The forum is distinct from APEC's Digital and AI Ministerial Meeting held on July 23. The July 24 event brought together ministers, policymakers, industry representatives, and academics and resulted in its own Statement on Promoting AI Development in the Asia-Pacific Region.
What the forum statement says
The statement organizes cooperation around four practical areas:
- •Secure development and infrastructure: economies are encouraged to exchange practices for reliable, accessible, scalable, trusted, secure, and resilient AI and digital infrastructure.
- •Responsible adoption and skills: the document supports AI use across economic sectors, accessible education and training, and greater AI literacy.
- •Innovation and open source: it encourages support for open-source models and projects that use strong security assurance while respecting data protection and intellectual property rights.
- •Capacity building: APEC economies commit to continued policy dialogue, knowledge sharing, and cooperation with public- and private-sector participants.
The statement also supports cross-border data flows while calling for stronger consumer and business trust. It identifies trade, customs modernization, and business competitiveness as areas where economies can exchange implementation experience.
Antara's July 27 report on the forum emphasizes public trust, transparency from AI developers, interoperable governance approaches, and the differing levels of AI readiness across APEC economies. APEC's own report says participants focused on turning model advances into useful adoption for businesses and communities.
Cooperation, not a common AI law
The language is encouraging rather than binding. It repeatedly asks economies to exchange practices, consider policies, and support voluntary cooperation. It does not impose shared compliance duties, model-testing thresholds, or enforcement rules across APEC's 21 member economies.
For data and ML teams operating across the region, the useful signal is the convergence of policy priorities: secure infrastructure, skills, trustworthy data use, accountable deployment, and guarded support for open-source systems. Implementation will still depend on each economy's laws, procurement rules, and technical standards.
That distinction matters. The forum statement can shape future capacity-building programs and national policy discussions, but it should not be read as a regional certification or a substitute for country-specific compliance work.
Key Points
- 1The July 24 APEC AI forum statement calls for secure infrastructure, responsible adoption, AI literacy, cross-border data flows, and capacity building.
- 2APEC encouraged open-source models and projects that use strong security assurance while respecting data protection and intellectual property rights.
- 3The statement coordinates priorities across economies with different policies; it does not establish a binding regional AI compliance regime.
Scoring Rationale
The official forum statement is relevant to regional AI infrastructure, open-source policy, skills, and governance cooperation. Its impact is moderate because it is non-binding and leaves implementation to APEC member economies.
Sources
Primary source and supporting public references used for this report.
Practice interview problems based on real data
1,625 SQL & Python problems across 15 industry datasets — the exact type of data you work with.
Try 250 free problems

