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Enhancing AI security through global AI red teaming

July 27, 2026 · Microsoft Security · Severity: HIGH

The article argues that AI safety testing is too isolated within organizations, missing critical failure modes. It advocates for global AI red teaming to incorporate diverse expertise and regional understanding. This collaborative approach is necessary to address high-risk vulnerabilities in modern AI systems.

Most AI safety testing still happens inside the walls of individual organizations. That has resulted in a fundamental disconnect: many of the highest-risk failure modes in modern AI systems require deep domain expertise, multilingual context, or regional understanding that no single internal team can fully replicate on its own. 

As frontier models become more capable, the attack surface expands with them. AI red teaming is no longer just about prompt injection or content safety edge cases. It increasingly involves security operations, misuse scenarios, multilingual harms, alignment failures, and domain-specific abuse patterns that can vary significantly across geographies and languages. 

Microsoft’s AI Red Team has observed that meaningful testing of advanced AI systems- and models similarly requires broader participation from researchers and practitioners who operate outside traditional corporate security boundaries. To address that gap, today we are announcing the External Red Team Alliance (EXTRA), a formalized global extension of Microsoft’s AI Red Team designed to support and encourage external expertise to advance AI safety and security testing.  We are proud to share we are funding the development of new AI safety assessments on six continents through unrestricted gifts. 

Building a global alliance

EXTRA is a two-part initiative focused on expanding AI safety research and strengthening external collaboration. 

The first component supports a global academic network focused on advancing AI safety and security research. Microsoft’s AI Red Team has provided unrestricted gifts to 18 university labs spanning six continents. The goal is intentionally broad: support researchers who are already investigating difficult, unresolved questions in AI safety and help them continue pushing that work forward independently. 

Some of the supporting institutions include: 

“Academic research is critical to understanding the cyber security landscape and finding solutions that work for all of society – and partnerships like this with industry are essential to delivering on that promise. Through partnerships, civil society and public institutions researchers gain access to frontier technology to understand how models work and bring their expertise to the task of determining risk and developing more effective countermeasures for the benefit of society as a whole.”

Nicolas Papernot, professor, University of Toronto. 

The second component of EXTRA focuses on operational collaboration. Microsoft is building a distributed network of specialists who can participate directly in red teaming highly specialized areas where deeper expertise is required. That includes researchers, practitioners, and regional experts who understand specific attack classes, languages, cultural contexts, or technical domains that internal teams may not fully cover alone. 

Beyond expanding participation, EXTRA is also intended to help advance the science of AI safety evaluation. By bringing together academic researchers, security practitioners, and domain experts from around the world, the initiative aims to contribute to the development of more robust methodologies and testing practices for increasingly capable AI systems. Today’s cybersecurity ecosystem depends on coordinated vulnerability research, responsible disclosure programs, academic inquiry, and global communities of independent security researchers who routinely identify risks that vendors alone would not find. Likewise, advancing AI safety will benefit from ongoing contributions from experts across institutions, disciplines, and geographies to identify emerging threats, strengthen safeguards, and improve evaluation practices. 

“As frontier model capabilities advance, they create new risk opportunities, particularly in low-resource settings. Partnerships, such as this EXTRA, bring greater attention to the stu

Key Takeaways

  • AI safety testing within individual organizations misses critical failure modes.
  • Global red teaming brings diverse domain expertise and multilingual context.
  • Collaborative testing is essential for identifying high-risk AI vulnerabilities.
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