Today, Microsoft published its 2026 Responsible AI Transparency Report. The report highlights the progress we’ve made in building and deploying AI responsibly, supporting our customers, and strengthening our responsible AI governance, tools, and practices. You can explore the report in its entirety here.

AI is moving fast, and so are societal expectations. The boundaries of what people can accomplish with AI are expanding, and communities are asking more questions about how AI systems are designed, built, and used. As AI becomes more integral to how we live and work, confidence that AI systems are operating reliably and securely is becoming an essential prerequisite to their broad and beneficial adoption.

At Microsoft, we have been building a responsible AI program for nearly a decade, rooted in two core beliefs: that trust is foundational to realizing the benefits of AI and that the empowerment of people and organizations must remain at the center of our strategy. As capabilities advance and adoption accelerates, that experience is helping us meet this moment and adapt for what comes next.

Our third annual Responsible AI Transparency Report shares how our program is evolving and the priorities that continue to shape our work. Over the last year, investing in three specific areas has enabled us to embed trust more deeply and at greater scale: adaptive governance and technical risk management, practical tools and capabilities, and shared practices and strong partnerships. These investments cut across five trends shaping the AI landscape, including the rapid expansion of agentic AI.

Taken together, these trends and investments underscore our view that model capability alone will not determine the impact of AI. That will depend on organizations that develop and deploy AI technologies that deliver real value—and govern them with the rigor and adaptability needed to earn and sustain trust.

Adaptive governance and technical risk management

As the frontiers of AI advance, we are making our governance more adaptive and more tightly integrated with engineering workflows. In practice, this means that we have updated our policies to better match the AI tech stack and AI value chain, evolved our risk management practices to address emerging AI capabilities and risks, and strengthened the readiness of our responsible AI community: the people who operationalize our program at enterprise scale.

This year, we re-engineered our Responsible AI Standard to make it more adaptive to evolving technical realities, uses, risks, and regulatory requirements. The new Standard is structured by reference to different components of the tech stack—models, platform services, and applications—and the role that Microsoft plays in developing or deploying those components. It combines core requirements that always apply with more targeted, scenario-specific requirements that can evolve as capabilities and risks change. For example, we apply some of our most rigorous risk management measures to AI systems with the most significant cyber capabilities, helping ensure that advances in AI favor the defenders responsible for securing critical digital infrastructure.

We are also evolving our technical risk management practices. Increasingly capable systems can retain memory, use tools, access data, and take actions on behalf of users. Governing these systems requires us to think beyond the behavior of an individual model or application to interactions among models, agents, applications, tools, data, and people. Our work increasingly focuses on controls such as agent identities, tool permissions, and monitoring of actions.

And governance only works when people can put it into practice. We have continued to build responsible AI capabilities across Microsoft, equipping thousands of engineers and product managers with training on topics such as agentic AI threat modeling and prompt injection defenses.

Together, these investments are helping us move toward a more continuous, lifecycle-based approach to AI governance. With agentic AI, risks can evolve as systems interact with their environments, users, and other systems. Our governance needs to evolve with these agentic capabilities—and incorporate what we learn from their testing and deployment.

Practical tools and capabilities

Effective governance depends on tools that help translate policy goals into action. As developers and organizations navigate a more complex technical and regulatory environment, they need practical ways to identify risks, evaluate systems, establish controls, and monitor how AI behaves in the real world.

We are applying what we learn from governing AI at Microsoft into tools, capabilities, and resources that help developers and organizations beyond Microsoft do just that—whether they build on our platforms or leverage open-source projects.

We have expanded tools to evaluate AI systems across the lifecycle. A new AI Red Teaming Agent helps accelerate the identification and evaluation of risks. Agent evaluators help developers measure the quality, safety, and performance of agentic applications. RAMPART turns red team findings into repeatable tests, enabling more continuous coverage as systems change.

We are also building greater visibility and control into agentic systems. With ASSERT and Agent Control Specification, developers can evaluate agents against their policies, place runtime controls at critical points in an agent’s workflow, and monitor behavior.

These tools and capabilities reflect a shift: as systems become more dynamic, governance needs to become more operational. Organizations need to be able to see what their systems are doing, test how they behave, and intervene when necessary—not just assess them before deployment.

Organizations also need confidence—and increasingly need to demonstrate—that responsible AI practices are being implemented consistently. Microsoft is one of the few companies certified against ISO 42001 across a broad portfolio, including Microsoft 365 Copilot, Foundry, and GitHub Copilot. Over the last year, we have simplified and strengthened our internal processes that support that certification.

Ultimately, responsible AI governance is a shared responsibility across the AI value chain. Our goal is to help make the practices and capabilities needed to meet that responsibility more accessible, practical, and scalable.

Shared practices and strong partnerships

The challenges of governing AI are bigger than any one company, and increasingly interconnected AI systems make collaboration even more essential.

As AI adoption expands across borders and sectors, we need shared expectations for how systems are evaluated, monitored, and governed, as well as interoperable standards that enable visibility into interactions across tools, data, and systems. We also need to keep advancing the underlying science and technical practices so that we can benefit from rigorous, applied insights into what effective governance looks like and where the remaining gaps are.

That starts with research. Over the past year, we advanced our work with the US Center for AI Standards and Innovation and AI Safety and Security Institutes in Australia, Singapore, and the UK to strengthen the science and practice of AI evaluation. We also launched an External Red Team Alliance with 18 universities across six continents to expand understanding of priority risks.

Common technical practices and standards are critical. Through the Frontier Model Forum, OpenTelemetry, and the Appia Foundation, we are helping develop approaches spanning frontier cyber benchmarks, end-to-end observability for increasingly agentic systems, and AI assurance across supply chains and sectors. We are also contributing to efforts that make transparency reporting more interoperable across organizations and jurisdictions, including through an OECD-led informal task force that developed the Hiroshima AI Process Reporting Framework version 2.0.

We also need shared ways to measure progress. We cannot meaningfully assess progress if every organization measures AI risks differently. Through our work with MLCommons, we are helping expand AILuminate into a broader suite of reliability benchmarks, creating common approaches for evaluating areas such as jailbreak resilience, multilingual performance, and psychosocial risk in conversational AI.

Shared learning, shared practices and standards, and shared measurement can help the entire ecosystem develop while raising shared expectations for trust.

Meeting the moment and investing for the future

Our experience over the past year has reinforced that responsible AI cannot be static. It has to be embedded in development processes, supported by practical tools, and continually informed by what we learn. That is why our responsible AI investments extend from the systems we build, to the tools we provide our customers, to the research, practices, and measurement approaches we help develop with the broader ecosystem.

Our 2026 Responsible AI Transparency Report explores this work in more depth—from how we re-engineered our Responsible AI Standard to how we are strengthening governance for agentic AI, advancing evaluation, and addressing AI misuse. We invite you to explore the report to see what we have learned, what we have changed, and how we are putting our priorities into practice.

As AI becomes more powerful and more present in people’s lives, our commitment is to keep listening and learning, to keep strengthening our safeguards, and to keep putting the empowerment of people and organizations at the center of our strategy.

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Today, Microsoft is announcing new agreements with the Center for AI Standards and Innovation (CAISI) in the US and the AI Security Institute (AISI) in the UK to advance the science of AI testing and evaluation, including through collaborative work to test Microsoft’s frontier models, assess safeguards, and help mitigate national security and large-scale public safety risks. These agreements matter because ongoing, rigorous testing is essential to building trust and confidence in advanced AI systems. Well-constructed tests help us understand whether our systems are working as intended and delivering the benefits they are designed to provide. Testing also helps us stay ahead of risks, such as AI-driven cyberattacks and other criminal misuses of AI systems, that can emerge once advanced AI systems are deployed in the world. 

While Microsoft regularly undertakes many types of AI testing on its own, testing for national security and large-scale public safety risks necessarily must be a collaborative endeavor with governments. This type of testing depends on deep technical, scientific, and national security expertise that is uniquely held by institutions like CAISI in the US and AISI in the UK and the government agencies they work with. By combining that government expertise with Microsoft’s experience building and deploying AI systems at global scale, together we are better positioned to anticipate and manage national security and public safety risks in ways that build public trust and confidence in advanced AI systems.  

Improving AI evaluation science through cooperative research and operational experience 

Advancing the science of AI evaluation requires more than isolated research or one-off testing. It depends on sustained collaboration between industry, government, and research institutions. Through our new and expanded partnerships with the US and UK governments—alongside national security–focused evaluations of model capabilities—Microsoft is bringing technical expertise and operational experience to strengthen AI evaluation methods and practical testing foundations.  

• In the US, with CAISI, Microsoft and NIST will collaborate on improving methodologies for adversarial assessments—testing AI systems in ways that probe unexpected behaviors, misuse pathways, and failure modes, much like stress-testing whether airbags, seatbelts, and braking systems work effectively and reliably in safety-critical driving scenarios. This work involves co-developing more systematic and reproducible approaches to evaluation, including shared frameworks, datasets, and workflows for assessing safety, security, and robustness risks in advanced AI systems. It also builds on our AI Red Team’s novel research and tools to detect compromised models at scale. 

• In the UK, with AISI, Microsoft will collaborate on research related to frontier safety and security, including methods for evaluating high-risk capabilities and the effectiveness of the safeguards used to address them. The partnership will also include societal resilience research examining how conversational AI systems interact with users in sensitive contexts.  

These collaborations are designed to improve measurement science, evaluation methodologies, practical testing workflows, and real-world mitigation impact. They reflect a shared commitment to rigorous, practical approaches that can make safeguards stronger and evaluations more reliable. 

Looking ahead 

No organization can address these challenges alone. Our partnerships with CAISI and AISI are a key part of a wider effort to build the institutions, research base, and shared methodologies needed for effective AI testing. This effort also includes: 

• Pursuing research and evaluation in collaboration with other AI institutes globally while helping advance shared priorities and methodologies for testing through the International Network for AI Measurement, Evaluation and Science. 

• Helping deliver industry best practices through the Frontier Model Forum (FMF), an initiative dedicated to advancing the science and practice of frontier AI safety and security. Through the FMF, we are working with other leading AI developers to support independent research, develop shared evaluation methodologies, and promote transparency around risk mitigation strategies.  

• Contributing to MLCommons, a multistakeholder non-profit that develops and operationalizes testing tools such as AILuminate, a family of safety and security benchmarks. In February, we announced efforts underway with institutions in India, Japan, Korea, and Singapore to expand AILuminate to support multilingual, multicultural, and multimodal evaluation, helping to make sure that AI systems work well in the languages and cultural contexts in which people around the world use them. 

As AI capabilities advance, so too must the rigor of the testing and safeguards that underpin them. We will apply what we learn from these partnerships directly into how we design, test, and deploy AI systems, ensuring that progress in evaluation science translates into safer, more secure products for our customers. As these partnerships progress, we will share what we learn and look for opportunities to apply insights and best practices to AI testing more broadly.   

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Microsoft announces at the India AI Impact Summit it is on pace to invest USD $50 billion by the end of the decade to help bring AI to countries across the Global South  

Artificial intelligence is diffusing at an impressive speed, but its adoption around the world remains profoundly uneven. As Microsoft’s latest AI Diffusion Report shows, AI usage in the Global North is roughly twice that of the Global South. And this divide continues to widen. This disparity impacts not only national and regional economic growth, but whether AI can deliver on its broader promise of expanding opportunity and prosperity around the world.

The India AI Impact Summit rightly has placed this challenge at the center of its agenda. For more than a century, unequal access to electricity exacerbated a growing economic gap between the Global North and South. Unless we act with urgency, a growing AI divide will perpetuate this disparity in the century ahead.

Solutions will not come easily. The needs are multifaceted, and will require substantial investments and hard work by governments, the private sector, and nonprofit organizations. But the opportunity is clear. If AI is deployed broadly and used well by a young and growing population, it offers a real prospect for catch-up economic growth for the Global South. It might even provide the biggest such opportunity of the 21^st century.

As a company, we are committed to playing an ambitious and constructive role in supporting this opportunity. This week in Delhi, we’re sharing that Microsoft is on pace to invest $50 billion by the end of the decade to help bring AI to countries across the Global South. This is based on a five-part program to drive AI impact, consisting of the following:

• Building the infrastructure needed for AI diffusion
• Empowering people through technology and skills for schools and nonprofits
• Strengthening multilingual and multicultural AI capabilities
• Enabling local AI innovations that address community needs
• Measuring AI diffusion to guide future AI policies and investments

One thing that is clear this week at the summit in India is that success will require many deep partnerships. These must span borders and bring people and organizations together across the public, private, and nonprofit sectors.

1. Building the infrastructure needed for AI diffusion

Infrastructure is a prerequisite for AI diffusion, requiring reliable electricity, connectivity, and compute capacity. To help address infrastructure gaps and support the growing needs of the Global South, Microsoft has steadily increased its investments in AI-enabling infrastructure across these regions. In our last fiscal year alone, Microsoft invested more than  $8 billion in datacenter infrastructure serving the Global South. This includes new infrastructure in India, Mexico, and countries in Africa, South America, Southeast Asia, and the Middle East.

We’re coupling our investments in datacenters with an ambitious effort to help close the Global South’s connectivity divide. We’ve been pursuing aggressively a global goal to extend internet access to 250 million people in unserved and underserved communities in the Global South, including 100 million people in Africa.

As we announced in November, we’ve already reached 117 million people across Africa through partnerships with organizations such as Cassava Technologies, Mawingu, and others that are building last‑mile networks across rural and urban communities alike. We’re closing in on our global goal of reaching 250 million people and will share an update on that progress soon.

We’re investing in AI infrastructure with sensitivity to digital sovereignty needs. We recognize that in a fragmented world, we must offer customers attractive choices for the use of our offerings. This includes sovereign controls in the public cloud, private sovereign offerings, and close collaboration with national partners.

We pursue all this with commitments to protect cybersecurity, privacy, and resilience. In the age of AI, we ensure that our customers’ AI-based innovations and intellectual property remain in their hands and under their control, rather than being transferred to AI providers.

Critically, we balance our focus on national sovereignty with our efforts to support digital trust and stability across borders. The Global South requires enormous investments to fund infrastructure for datacenters, connectivity, and electricity. It is difficult to imagine meeting all these needs without foreign direct investment, including from international technology firms.

This need is part of what informed our announcement last week at the Munich Security Conference of the new Trusted Tech Alliance. This new partnership brings together 16 leading technology companies from 11 countries and four continents. We’ve agreed together that we will adhere to five core principles designed to ensure trust in technology. Ultimately, we believe the Global South—as well as the rest of the world—needs both to protect its digital sovereignty and benefit from new investments and the best digital innovations the world has to offer.

2. Empowering people through technology and skills for schools and nonprofits

Ultimately, datacenters, connectivity, and electricity provide only part of the digital infrastructure a nation needs. History shows that the ability to provide access to technology and technology skills are equally important for economic development.

As a company, we’re focused on this in multiple ways. One critical aspect of our work is based on programs to provide cloud, AI, and other digital technologies to schools and nonprofits across the Global South. Another is our work to advance broad access to AI skills. In our last fiscal year, Microsoft invested more than $2 billion in these programs in the Global South. This includes direct financial grants, technology donations, skilling programs, and below-market product discounts.

AI skills are foundational to ensuring that AI expands opportunity and enables people to pursue more impactful real-world applications. With the launch of Microsoft Elevate in July, we committed to helping 20 million people in and beyond the Global South earn in-demand AI skilling credentials by 2028. After training 5.6 million people across India in 2025, we advanced this work by setting a goal last December to equip 20 million people in India with essential AI skills by 2030.

As part of that commitment, today we are announcing the launch of Elevate for Educators in India to strengthen the capacity of two million teachers across more than 200,000 schools, vocational institutes, and higher education settings. Our goal is to help the country’s teaching workforce lead confidently in an AI‑driven future. The program will be delivered in partnership with India’s national education and workforce training authorities, expanding equitable AI opportunities for eight million students.

Through Microsoft Elevate, we’re also working to introduce new educator credentials and a global professional learning community that enables teachers to share best practices with peers worldwide. This effort will involve large-scale capacity building initiatives, including AI Ambassadors, Educator Academies, AI Productivity Labs, and Centers of Excellence. It will equip 25,000 institutions with inclusive AI infrastructure while integrating AI learning pathways into major government platforms.

3. Strengthening multilingual and multicultural AI capabilities

Language is another major barrier to AI diffusion across the Global South, particularly in regions where digitally underrepresented languages prevail and access to essential services depends on local-language communication. For billions of people worldwide, AI systems perform less consistently in the languages they rely on most than in English.

That’s why we’re announcing this week new steps to increase our investments across the AI lifecycle, from data and models to evaluation and deployment, to strengthen multilingual and multicultural capabilities and support more inclusive AI systems that will better serve the Global South.

First, we’re investing upstream in language data and model capability. This includes support for LINGUA Africa, which builds on what we learned through LINGUA Europe: that investing in language data and model capability in partnership with local communities can materially improve AI performance for underrepresented languages.

Through LINGUA Africa—a $5.5 million open call led by the Masakhane African Languages Hub, Microsoft’s AI for Good Lab, and the Gates Foundation, with additional support from the UK government—we are prioritizing open, responsibly sourced data across text, speech, and vision as well as use-case-driven AI model development. By enabling African languages in high-impact sectors like education, food security, health, and government services, LINGUA Africa aims to ensure AI advances translate into tangible improvements in people’s daily lives.

Second, we’re advancing multilingual and multicultural evaluation tools. We’re helping expand the MLCommons AILuminate benchmark to include major Indic and Asian languages, enabling more reliable measurement of AI safety and security beyond English.

Today, even when automated evaluation tools expand language coverage, they too often rely on machine translation or English-first model behavior, with predictable failures when local expressions shift meaning. Partnering with academic and government institutions in India, Japan, Korea, and Singapore, and with industry, Microsoft is co-leading AILuminate’s multilingual, multicultural, and multimodal expansion that builds from the ground up. With a pilot dataset of 7,000 high-quality text-and-image prompts for Hindi, Tamil, Malay, Japanese, and Korean, we’re developing tools that reflect how risks manifest in local linguistic and cultural contexts, not just how they appear after translation.

Microsoft Research is also advancing Samiksha, a community-centered method for evaluating AI behavior in real-world contexts, in collaboration with Karya and The Collective Intelligence Project in India. Samiksha encodes local language use, culturally specific communication norms, and locally relevant use cases directly into core testing artifacts by surfacing failure modes that English-first evaluations routinely miss.

Finally, we’re working to scale content provenance for linguistic diversity. For trusted AI deployment, the ecosystem benefits from tools to identify the provenance of digital content like images, audio, or video, distinguishing whether it’s AI-generated. With partners in the Coalition for Content Provenance and Authenticity (C2PA), Microsoft is helping extend content provenance standards beyond an English-ready baseline. This includes forthcoming support for multiple Indic languages across metadata, specifications, and UX guidance, alongside efforts to support mobile-first deployment. With these investments, hundreds of millions more people in India will be better equipped to identify synthetic media in their primary language.

4. Enabling local AI innovations that address community needs

As India’s guiding sutras for the AI Impact Summit recognize, AI must be applied to address pressing challenges in collaboration with people and organizations in the Global South. Microsoft’s increasing investments prioritize locally defined problems, locally grounded expertise, and real-world impact. Our goal is straightforward: to ensure that AI solutions are not only technically sound, but socially relevant and sustainable.

Today, Microsoft is announcing a new AI initiative to strengthen food security across Sub-Saharan Africa, starting in Kenya and designed to scale across the region. Across Global South communities, food security and sustainable agriculture are critical to resilience and progress. In collaboration with NASA Harvest, the government of Kenya, the East Africa Grain Council, UNDP AI Hub for Sustainable Development, and FAO, our AI for Good Lab will use AI on top of satellite data to provide critical, timely food security insights. This builds on what we’ve learned in helping to address rice farming challenges in India, where severe groundwater depletion prompted 150,000 farmers in Punjab to adopt water-saving methods. In collaboration with The Nature Conservancy, Microsoft’s AI for Good Lab developed a classification system with satellite imagery to empower policymakers to track adoption of sustainable rice farming practices, target interventions, and measure water management impacts at scale.

Through Project Gecko, Microsoft Research is also co-designing AI technologies with local communities in East Africa and South Asia to support agriculture. This work includes the Paza family of automatic speech recognition models that can operate on mobile devices across six Kenyan languages, multilingual Copilots, and a Multimodal Critical Thinking (MMCT) Agent that can reason over community-generated video, voice, and text. Microsoft also launched PazaBench—the first automatic speech recognition leaderboard, with initial coverage of 39 African languages—and developed two playbooks for multilingual and multicultural capabilities, Paza and Vibhasha. Likewise, our AI for Good Lab developed a reproducible pipeline for adapting open-weight large language models to low-resource languages, demonstrating measurable gains for languages such as Chichewa, Inuktitut, and Māori.

5. Measuring AI diffusion to guide future AI policies and investments

Finally, accelerating diffusion requires a firm understanding of where AI is being used, how it is being adopted, and where gaps persist. Building on our AI Diffusion Reports and Microsoft GitHub’s long track record of contributing to the OECD AI Policy Observatory, the WIPO Global Innovation Index, and other cross‑country analyses, we’re increasing our investments in research and data sharing to track AI diffusion.

We’re advancing new methods for sharing AI adoption metrics. For example, based on models used in public code repositories hosted on Microsoft GitHub and privacy-preserving aggregated usage signals from Azure Foundry, we’re scaling this work through contributions to the forthcoming Global AI Adoption Index developed by the World Bank.

Signals from the global developer community that builds, adapts, and deploys AI-enabled software round out adoption research. At 24 million, the Indian developer community is the second largest national community on GitHub, where developers learn about and collaborate with the world on AI. The Indian community is also the fastest growing among the top 30 largest economies, with growth at more than 26 percent each year since 2020 and a recent surge of over 36 percent in annual growth as of Q4 2025. Indian developers rank second globally in open-source contributions, second in GitHub Education users, and second in contributions to public generative AI projects, with readiness to use tools like GitHub Copilot across academic, enterprise, and public interest settings enabling AI diffusion.

Insights from this evidence base help inform investments in infrastructure, language capabilities, skilling, or beyond, supporting more targeted and effective interventions to expand AI’s benefits. They also create a common empirical baseline to track progress over time—so AI diffusion becomes something we can measure and shape, not just observe.

Sustaining impact at scale through coordinated global action

For AI to diffuse broadly and deliver meaningful impact across regions, several conditions matter. As a company, we are focused on the need for accessible AI infrastructure, systems that work reliably in real-world contexts, and technologies that can be applied toward local challenges and opportunities. Microsoft is committed to working with partners to advance this work, including sharing data to track progress.

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