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Forty years of business research in China: a critical reflection and projection

  • Ji-Ye Mao 1  

Frontiers of Business Research in China volume  12 , Article number:  24 ( 2018 ) Cite this article

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Indigenous business research has largely mirrored the economic growth in China over the past 40 years, which has reached a critical juncture. It is, therefore, important to take stock of the past progress to identify critical success factors and remaining challenges, in searching for paths to the next leap forward. To this end, this commentary will first review the key milestones in indigenous business research over the past four decades. Then it will highlight two paradoxes, namely, the lack of indigenous theories despite the phenomenal growth of Chinese firms, and the growing divergence between scientific rigor and low relevance to practice, which will need to be addressed in the future. Lastly, several predications and suggestions will be offered.

This year marks the 40th anniversary of China’s reform and opening up commenced in late 1978, which has fundamentally transformed the nation and lifted it out of poverty to a large extent. After four decades of phenomenal growth, the nation has reached a critical junction, and is now searching for both new directions and drivers for the next round of growth, while trying to steer away from the middle income trap. Despite the phenomenal growth in the past, there are structural problems in the economy that are difficult and painful to resolve after low hanging fruits have been picked up. However, it is also clear that the old path of growth is no longer sustainable, and a new mode of growth is overdue.

In many ways, progress in business research in China has mirrored the growth pattern of the Chinese economy, and registered an equally impressive growth curve. Whereas the economic success is widely known to the world as reflected in not only all kinds of statistics but also everyday living, few have a reasonable grasp of the nature and extent of progress in business research in China, nor the remaining challenges and opportunities. China has emerged as a top producer of business research papers in both quantity and influence measured in citations (Li 2015 ). Whereas the notion of empirical business research was entirely alien to Chinese researchers till late 1980s, it is now firmly entrenched in the academia. Similar to the current state of the economy, business research in China also needs another round of transformation, in order to establish its own identity and to make greater contributions to the global community of business researchers.

It is against such a backdrop that this commentary is composed with a threefold purpose. First, it will briefly take stock of the transformation of business research from nearly nonexistence to a flourishing state demonstrated by both the quantity and quality of publications. Second and third, it will identify key challenges and contradictions, and to speculate on future directions.

An overview of the past progress

Business research in China has made remarkable strides over the past four decades. Whereas it is important to take stock, neither is it desirable nor feasible to present a complete historical review of the development process in this commentary. Instead, it shall present a brief overview only, while highlighting several key milestones as the background for the critical reflection to follow.

In a comprehensive review of business research in China over the period between 1978 to 2008 based on a survey of senior Chinese scholars in the field, Su and Liu ( 2009 ) identified 55 important milestones and divided the development history of business research in China into three stages, the awareness stage (1978–1986), formation stage (1987–1996), and rapid development stage (since 1997). During the awareness stage, the importance of business research was gradually recognized by the state, firms, and academia. However, previously there was no real research on market-oriented modern organizations beyond isolated exploration on productivity enhancement, because enterprises followed executive orders from the state as part of the planned economy. Hardly was there any indigenous management research based on generally adopted research methodologies by Western researchers, be it empirical or mathematical. Research at that time was largely translating Western textbooks and preparing lecture notes.

During the formation stage, business research gained formal recognition by major stakeholders, and was institutionalized, especially after Deng Xiaoping’s famous tour in southern China, where he called for greater degrees of adamant reform and opening up in the spring of 1992. This was a landmark event in modern Chinese history, which jump-started the then stagnating reform and accelerated the pace of transformation in all sectors including science and technology development. Gradually, business research was recognized as an academic discipline by the state authorities. In particular, the management sciences division of the national Natural Science Foundation of China (NSFC) was promoted to a full-fledged department as other recognized disciplines in 1996, 10 years after the diversion’s establishment from the very beginning of the NSFC. The first MBA programs were also launched in several universities on a trial basis in 1990, which stimulated business research and created the need for researchers.

Lastly, since 1997 business research as an academic discipline entered a stage of rapid growth. Two events significantly shaped the subsequent development in particular. First, through its newly formed Management Sciences Department, Footnote 1 the NSFC became the primary source of funding to business researchers and provided the largest research grants on average to scientists on a competitive basis. The success rate has always been under 20%, and used to be much lower hovering above the 10% mark, and thus a grant from NSFC carried high esteem. Applications were subjected to a peer review process, which weighed heavily the soundness of the research methodology and scientific rigor in particular. The influence of the NSFC was partly reflected in the funding for papers published in English language journals. According to a report by the Management Science Department of the NSFC (Li, 2015 ), in 2009 among the papers authored by Chinese mainland-based researchers and were indexed by the Web of Science (WoS), 37% of them were funded by the NSFC, far ahead of under 8% funded by the Ministry of Education, the second largest source of research funding. These two ratios rose to 47% and 15%, respectively, in 2013, while the rest were funded by the third to the fifth largest national funding sources including the Ministry of Science and Technology, and the Ministry of Human Resources and Social Security of China. 66% of the highly cited papers were funded by NSFC in 2013. Clearly, NSFC has established its position as the primary funding source for business research in China. Second, also during this stage, Professor Anne Tsui, organized a series of workshops on empirical business research methodology at the Hong Kong University of Science and Technology, from 1999 to 2002. The workshops rightfully targeted junior faculty in Chinese universities, and each trained dozens of junior researchers, who later became academic leaders in their own institute and respective research field.

Paradox 1: Business success vs. lack of indigenous theories

As a result of the rapid growth of Chinese economy over the past four decades, the number of Chinese companies in the global Fortune 500 has reached 115 in 2017, including 109 based in Chinese mainland and Chinese Hong Kong (Fortune, 2017 ). Not only in size but also in quality and innovation have Chinese companies managed to grow. China is leading the world or among the frontrunners in e-commerce, mobile payment, sharing economy, artificial intelligence, 3D printing, and pilotless planes. The top four e-commerce giants BATJ (an acronym to refer to Baidu, Alibaba, Tencent, and , and especially Alibaba and Tencent) are powering new business models in the name of New Retail, which refers to the combination of omni-channels (online and offline), socialization in addition to merely retail transactions, and the use of big data to personalize consumer experiences. However, no well-known management tool, method, concept, or theory has emerged from best practices of Chinese firms, let alone anything generalizable and adopted beyond a single company. In contrast, during the late 1980s and later, when the success of Japanese firms produced new manufacturing methodologies such as lean manufacturing, the Kanban system, just-in-time inventory management, popularized by the Toyota Production System.

It begs the question of how have the Chinese firms achieved the phenomenal growth? Is there a distinctive growth model or winning formula for corporate China? Or is Chinese firms’ success largely due to the large size of the domestic market protected by a unique institutional environment, and the so-called population dividend and latecomer advantage? Alternatively, is it because indigenous research has turned a blind eye to the best practices of Chinese firms? My personal view is that the past success of Chinese firms was largely attributable to the shortage economy featured in the early stage of reform and opening up and lasted till earl 2010s when the economy has maintained a high growth rate. There was a huge demand from consumers for any product of reasonable quality and price. In other words, just riding the rising tides was good enough for corporate China. Moreover, it is likely because that Chinese firms have been playing a catch up game, and achieved success simply by adopting well-established Western managerial processes and methods and sometimes creatively adapting these to the local context. As an anecdote, a former colleague of mine and a long-time senior advisor to Huawei, widely considered the most successful Chinese firm, believed that the most important success factor of Huawei was continuously adopting Western management processes and methodologies such as the Integrated Product Development process of IBM. To date, the best-known new to the world and originated from China method, “ Rendanheyi ,” which means to align every employee to customer requirements in order to produce quality product to satisfy customer needs, is a methodology proposed by Haier’s supreme leader Mr. Zhang Ruimin. However, it has hardly been studied by Chinese researchers, nor was it widely adopted beyond Haier. Therefore, there exists hardly any evidence of its effectiveness, let alone theorizing around the method.

Paradox 2: Divergence between research rigor and relevance

According to the same report by the Management Science Department of the NSFC (Li, 2015 ), in 2004, Chinese researchers published only 682 papers referenced in the SCI/SSCI (WoS) databases, which could be considered an indicator of quality, and this number rose to 5288 after an impressive 6.8 fold increase in 2013 only after that of the US and the UK, 19,221 and 7063, respectively. More importantly, the number of citations per paper, which is often taken as a measure of quality and influence, by Chinese researchers in the SCI/SSCI databases is ranked the second in the world, only after that of the Netherlands. In fact, two Chinese business schools have broken into the top 100 in the world in the University of Texas at Dallas list of top 24 business research journals. All of these indicate that business research by Chinese scientists has achieved an acceptable degree of methodological maturity and scientific rigor.

However, the practical impact of the business research has been minimal. Part of the reason is that junior researchers in the top-tier business schools are hired from overseas with solid training to produce high quality research, but the research support and culture are not always up to the standard in the elite research schools in the US. Promotion and other incentives disproportionally favor research excellence, i.e., publications are preferred by peers who also prefer rigor to relevance. In contrast with the past, at least some of the earlier generations of business faculty have worked closely with the industry, e.g., the well-known Six Gentlemen for Huawei, i.e., the six professors from Renmin University of China, who advised Huawei and helped its success in its early years. However, by and large, few faculty hired over the past two decades are focused on applied research or choose to closely engage the business world. Many researchers have taken notice of this disturbing fact that research papers are increasingly more methodologically rigorous, but less and less relevant to practice, to the extent that research papers are neither targeting practitioners, nor are they used in classroom teaching. For many researchers, publication is just for the sake of it, and this situation is due for a change.

Regrettably, existing research papers on business administration in China virtually show no signs of indigenous characteristics (Tsui and Zhang 2011 ). As a result, no adequate progress has been made to address the criticisms on Chinese or Asian management research in general, such as the lack of self-confidence (Meyer 2006 ), weaknesses in theory development or relevance for management practices (White 2002 ), and heavily utilizing existing management theories but rarely proposing new theories (Tsui, 2009 ).

Despite the two paradoxes and challenges discussed earlier, a quantum leap has been made in business research in China. It is because scientific rigor must be established first, which was needed the most over the past 40 years, i.e., addressing the primary weakness head-on. In other words, the past success has laid a solid foundation for the future. Rigor will remain the most important issue for the years ahead. In the past, the effort was well spent on catching up the methodology of organizational research. Therefore, Chinese researchers should stick to the winning-formula and keep pursuing methodological rigor continuously.

Future directions

Next, this commentary will conclude with several suggestions for ways to move forward, and some practical advice.

First, it could be highly promising for Chinese researchers to address questions that target indigenous management problems. Unfortunately, not enough has been done in this regard. In a review of 270 empirical research papers related to Chinese contexts published in six top-tier general management journals in the world over the previous three decades till 2010 and Management and Organization Review (MOR) from its launch in 2005 to 2010, Jia et al. ( 2012 ) found that only 10 of the 270 featured some degree of Chinese contextualization in their concepts or constructs, relationships, and the logics underlying the relationships. A key conclusion was that Chinese-context-centered studies only offered three new concepts, market transition, network capitalism, and guanxi , though they have re-conceptualized concepts such as trust, citizenship behavior, and emotional intelligence. However, the Chinese context has failed to contribute new theoretical logics, except for Confucianism and related concepts such as guanxi , face, wulun , renqing , and traditionality. Interestingly, Li and Tsui ( 2002 ) showed that impactful studies (measured by citations) tend to have strong indigenous features, which means contextual factors are the key in theory-building as independent variables or moderators.

Not only is contextualized research theoretically important, but also increasingly feasible and practical. Whereas China has emerged as the world factory, it has also become the largest laboratory for organization research. Given the fast pace and magnitude of changes in Chinese firms, many interesting phenomena are amplified and intensified, and thus could be easier to observe. For example, the continuous reform and associated frequent policy changes combined with the technology advance and globalization, the operating environment for Chinese firms are particularly dynamic, which make it ideal for advancing theories on dynamic capabilities, and strategic transformation. A personal anecdotal example is what I have observed from my own research on IT outsourcing. Whereas most existing research on offshore IT outsourcing adopted a client perspective because the research was conducted by researchers in the West, I had access to Chinese IT vendors only when I started my research in this area in 2004 in China. This limitation was turned into an opportunity that allowed me to fill a gap in the literature from a vendor’s perspective (Jarvenpaa and Mao 2008 ) and to complement the existing research. Similar opportunities exist in areas that China is on the leading edge such as e-commerce, 3D printing, pilotless plains, AI applications, and shared economies. Chinese researchers have an opportunity to make unique contributions in these areas to the global management community.

Moreover, from a practical perspective, it is also important to contextualize business research. Situated in a unique political, social, and cultural environment, Chinese firms have to overcome numerous unique challenges. In particular, given the size of the Chinese economy, Chinese researchers shoulder a heavy responsibility to help domestic firms with their research, and thus must pay attention to critical issues of practical importance to the development of these firms. They should not simply recycle Western theories and turn a blind eye to critical issues faced by Chinese companies, though it might make sense for researchers in a small nation or region to overlook local issues. It is the responsibility of Chinese researchers to conduct research that is relevant to the local practice. Through solving real management problems, useful theories can be developed. Therefore, more effort should be directed to local management issues, which could also yield high return. As an increasing number of companies are operating on the global stage and becoming multinational, critical issues to Chinese firms can be highly relevant and of interest to firms in other emerging economies as well as the developed world. As a popular Chinese saying suggests, the more national, the more international.

Second, Chinese researchers should ask theoretically important questions in their research, which is the prerequisite for any high impact research and highly regarded by researchers as part of the current paradigm of research. To this end, indigenous research must engage in a dialogue with the mainstream literature and frontiers of business research in the world, to identify a major gap or weaknesses in the extant research. After all, any research contribution is an extension or revision of the existing theories.

Third, there is a growing need to embrace the diversity in research methods. Whereas traditional empirical research has primarily used questionnaire-based survey data, today it is increasingly more important to extend the traditional data collection to the macro end or the micro end, i.e., big data or case-based rich data. In particularly, qualitative research, which tends to be case-based (Eisenhardt 1989 ; Eisenhardt and Graebner 2007 ) for inductive theory development has gained more traction in recent years (Mao and Su 2016 ) because of case studies’ advantages in creating new discoveries and new insights. The comparative advantages of qualitative research are important for theory-building that is grounded in complex real-world problems. Case studies and inductive qualitative research in general can be expected to be used more widely to address the issues identified by Tsui ( 2009 ), “research in Chinese management has exploited existing questions, theories, constructs, and methods developed in the Western context. Lagging are exploratory studies to address questions relevant to Chinese firms and to develop theories that offer meaningful explanations of Chinese phenomena” (p. 1).

Meanwhile, the arrival of the big data era has also provided exciting opportunities for collecting massive high quality data, as Chinese firms such as China Mobile, China Life, Alibaba, and Tencent, possess the largest databases in the world. Again, Chinese researchers have an advantages because they are closer to the big data sources than their Western colleagues, and research collaboration between local and overseas researchers can yield high quality publications in top-tier journals.

Fourth and lastly, the past progress in business research can be attributed to international collaboration, as over 50% of the top-tier journals published in management sciences with funding from NSFC involved collaboration with co-authors affiliated with overseas institutions (Li 2015 ). International collaboration brought in not only methodological rigor, but also experience in theorizing, which takes a long career to develop, given that empirical research in business administration began only in the mid-1990s. A casual observation of the top-tier journal publications by Chinese mainland-based researchers, the majority of whom have a doctoral degree overseas, reveals that they are usually the result of international collaboration with more established overseas co-authors. A complementary strength of the local researchers is their close engagement with the frontline innovation and best-practices by Chinese companies, while their overseas collaborators are more experienced with the revision and publication process. It is safe to expect that international collaboration will remain important. The biennial conference of the International Association for Chinese Management Research (IACMR) has been instrumental in promoting the engagement between local Chinese researchers and those overseas. Therefore, in the future Chinese business researchers have both the need and means to extend the scope and deepen the depth of international collaboration.

In essence, it is all about adopting scientific rigor, i.e., to tell the Chinese stories with an international language. Clearly indigenous business research is poised to make the next leap forward. This journal, Frontiers of Business Research in China , which is also a by-product of the reform and opening up, is committed to becoming a premier outlet for high quality business research with strong implications for management in China.


In sum, over the past four decades, business research in China has completed a full circle of spiral climb by adopting international standards and scientific rigor in methodology. This has laid a solid foundation for the next round of climbing. It also is important to identify the critical success factors of the past success, and how to leverage the past success. This research commentary reviews the key milestones in indigenous business research over the past four decades to commemorate the 40th anniversary of the reform and opening up in China. It also highlights two paradoxes, i.e., the lack of indigenous theories despite the phenomenal growth of Chinese firms, and the growing divergence between scientific rigor and low relevance to practice, which will need to be addressed in the future. Lastly, several predications and suggestions are offered to address the two paradoxes. First, more effort should be directed to local management issues. A particularly fruitful future avenue would be to contextualize research, i.e., to more closely examine unique challenges and issues faced by indigenous Chinese companies while staying more relevant to the local businesses. Second, indigenous research must engage in a dialogue with the mainstream literature and frontiers of business research in the world so that important research questions can be asked and theoretical contributions can be made. Third, there is a growing need to embrace the diversity in research methods such as qualitative research and big-data based approaches. Fourth and lastly, Chinese business researchers should extend the scope and deepen the depth of international collaboration, which was a critical success factor in the past.

The department covers three narrower disciplines of management sciences, including operations research, business administration, and public administration and managerial economics. The term management sciences will be used with the same meaning subsequently.

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The author would like to acknowledge the funding from the MOE Project of Key Research Institute of Humanities and Social Sciences at Universities (Project No. 10JJD630012).

This study was supported by the MOE Project of Key Research Institute of Humanities and Social Sciences at Universities (Project No. 10JJD630012).

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September 2016, october 2016, september 2018.

This business owner says she's being 'penalized' for bringing manufacturing to Canada from China

Children's furniture company is facing surprise tariffs after bringing manufacturing to canada.

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A Canadian company that manufactures children's toy couches finds itself facing a stiff bill for import tariffs after bringing production home to this country.

While Barumba Play is no longer importing a majority of its product, a single component of the couches has been reclassified by the Canada Border Services Agency and is no longer tariff-free.

The company's flagship product is a couch for children made of pieces that can be easily taken apart and reassembled for play. Sara Feldstein founded the company in Markham, Ont., in 2021 and initially produced the couches entirely in China.

As the couches were classified as a children's toy, Feldstein told CBC News they were not subject to tariffs and were brought into Canada without import fees. Tariffs can be used by the Canadian government as a form of taxation on imports to protect Canadian economic development .

Trouble started for her in 2023, when Feldstein opted to move production of the couches to Canada from China.

"I on-shored my manufacturing to Canada from China and have been penalized for it," she said.

A woman is sitting on a pink toy couch.

Feldstein was able to manufacture every part of the couch in Canada except for cloth slipcovers, which she had to keep producing in China.

She received a letter from the Canada Border Services Agency in the summer of 2023 indicating it felt classifying the slipcovers as part of a toy was incorrect.   This contradicted what Feldstein was told to expect from business advisors and industry experts that she turned to for advice before opting to transfer manufacturing most of her product to Canada.

Instead, Feldman says the slipcovers have been lumped in with textiles such as carpets, bed linens and table linens — and now she's expected to pay 18 per cent duty on imports.

Three cloth slipcovers are pictured on the floor.

The CBSA declined an interview request from CBC News, and did not provide a written statement or any comment by publication deadline.

According to Feldstein, her business now owes at least $47,000 in retroactive tariffs, and she expects costs could escalate up to $70,000 while she waits for the appeals process to play out.

Businesses must pay, even during appeals

It's a cost she's not sure her business can bear, because she must pay the tariffs now even while she tries to appeal the decision.

That appeal process could take close to a year, according to the CBSA's current processing times .

"It would make me want to tell others, don't bother bringing your business back to Canada. Do it overseas. It's safer that way," she said.

It's not unusual for businesses to be caught in the complicated web of tariffs, according to lawyer David Rotfleisch of, a law firm specializing in tax and business.

A bald man with blue geometric eyeglasses is pictured in front of a bookcase, wearing a suit.

He confirmed that businesses such as Feldstein's need to pay assessed tariffs even while mounting a legal challenge because collection is not paused or halted when an appeal is launched. 

"Tariff classifications are complex and make income tax look relatively simple," Rotfleisch said.

"Wrong assessments affect a lot of businesses because they can't pay it, and by the time the appeal process runs its course, it's going to take time and [businesses] can't manage it. So they have to literally shut their doors."

  • Like British cheddar? Enjoy it while you can still find it

Suspending payments may not be solution

But eliminating the requirement to pay, even before appeals are exhausted, may not be the right solution, according to Jenifer Bartman, a business advisor based in Winnipeg.

"You could have companies not paying attention to the rules, saying, 'We'll go ahead and do this, and if it goes wrong, we're not going to be out of pocket any time soon,'" she said.

A woman with long blonde hair in a blue sweater faces the camera on a video call.

Bartman pointed out that importing products to Canada, whether partial or fully manufactured, requires a lot of preparation and advice.

"It's really important for business leaders, especially if they're venturing into a new aspect of their supply chain …  to understand what the rules are in advance because they can save themselves a lot of time and trouble down the line."

  • Furniture prices could spike again as industry group pushes for more tariffs on imports

Business owner says she did her research

For her part, business owner Feldstein said she did consult with experts prior to repatriating manufacturing of her couches to Canada. The decision by CBSA to reclassify surprised her.

Feldstein maintains the slipcovers currently being classified as textiles by CBSA should still be considered just a part of the couches she sells as a children's toy, and not a separate linen that could be used on its own. 

If the slipcovers are a part of the toy couch, they would not have the tens of thousands of dollars in tariffs assessed.

  • Canada to seek judicial review of latest U.S. decision on softwood lumber duties

According to the CBSA's website , to be considered a "part," the item must meet criteria including that it has no alternative function, be marketed and shipped along with other parts of the product, needed for "safe and prudent use" of the item, and be "committed" to use with the unit.

Barumba Play's founder isn't sure what comes next, but until the problem is resolved she's holding off on growing her business.

"I'm very hesitant to spend money on other items right now when this is in limbo," said Feldstein.


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Senior Reporter

Anis Heydari is a senior business reporter at CBC News. Prior to that, he was on the founding team of CBC Radio's "The Cost of Living" and has also reported for NPR's "The Indicator from Planet Money." He's lived and worked in Edmonton, Edinburgh, southwestern Ontario and Toronto, and is currently based in Calgary. Email him at [email protected].

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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Microplastics Are a Big Problem, a New Film Warns

At SXSW, a documentary traces the arc of plastics in our lives, and highlights evolving research of the potential harm of its presence in our bodies.

Hands hold dirt-covered plastic beads.

By Andrew Jacobs

It’s been more than five decades since Dustin Hoffman’s character in “The Graduate” was offered a kernel of wisdom about the path to prosperity.

“Plastics,” he’s told by Mr. McGuire , the starched corporate executive who offers the advice. “There’s a great future in plastics.”

Plastics have indeed been a game changer for humanity, and the enormous range of cheap, durable plastic goods, from food containers and PVC pipes to polyester clothing and single-use medical products, have inarguably improved life.

The problem, as nearly everyone knows, is that plastics are forever and very little of it has been recycled . The U.N. has estimated that most of the 400 million metric tons churned out annually — a doubling of production since 2000 — will remain on Earth in some form as they are broken down into teeny specks by sunlight, wind and the sea.

Roughly 20 years ago, Richard Thompson, a marine biologist, first discovered a worrisome accumulation of small plastic particles in ocean habitats and coined the word “microplastics.” Since then, scientists have been finding these fragments everywhere , from remote mountain peaks and the Arctic to the ocean seafloor .

In the decade that followed, scientists began to discover microplastics embedded in a wide range of living creatures , including in the seafood we eat. More recently, microplastics have been found inside the human body: in our lungs, our blood, our feces and in breast milk .

In 2021, Italian researchers for the first time identified microplastics in human placenta .

The question, scientists have been asking with increasing urgency, is whether these synthetic, foreign bodies pose a threat to human health.

“We know microplastics are everywhere, we know they are harmful to marine life and to our fisheries, but the research side of how they impact humans is still catching up,” said Imari Walker-Franklin , an environmental engineer and chemistry researcher at RTI International who studies microplastics.

“Plastic People,” a new documentary directed by Ben Addelman and Ziya Tong, surveys the emerging science on microplastics and arrives at a troubling conclusion: The potential health risks associated with plastic pollution are becoming hard to ignore.

The film, which debuts Saturday at the SXSW Film Festival in Austin, Texas, follows the work of microplastic researchers in a half-dozen countries, including a pair of Turkish scientists who said they recently discovered microplastics inside the human brain. Some of the particles were found deep inside the tissue of cancerous brain tumors.

“The revelation that the human body is full of microplastics is a recent one and I think the implications will become one of the most dominant health and environmental stories of our time,” said Rick Smith, president of the Canadian Climate Institute and one of the film’s executive producers. “It doesn’t matter if you’re rich or poor, there’s no sheltering yourself from this kind of new pollution.”

Microplastics, fragments less than five millimeters in size that can usually be seen by the naked eye, are not to be confused with nanoplastics , which are smaller than a speck of dust and are often the inadvertent byproduct of plastic production. Research on the potential health effects of nanoplastics is still in its infancy, at least compared with the studies on microplastics, a field that has been rapidly expanding over the past few years.

Scientific evidence of the effects of microplastics on humans is limited, at least in peer-reviewed literature. A study in the journal Environmental Science & Technology in 2022 found that patients with inflammatory bowel disease had a significantly higher amount of microplastics in their feces than those without the disease. A small University of Hawaii study published last November cataloged the growing presence of microplastics in the placentas of new mothers.

And a paper published on Thursday in the New England Journal of Medicine reported that people who had microplastics in their cardiovascular systems were at heightened risk for complications from heart attacks and strokes.

The researchers found that microplastics had become embedded in the fatty plaque that clings to the walls of blood vessels, and that patients with plastic-infused plaque were 4.5 times as likely to experience a heart attack, stroke or death compared with those whose plaque was free of microplastics. The study included 312 people who had undergone surgery to remove plaque from the carotid artery in the neck. Researchers followed them for nearly three years.

Dr. Giuseppe Paolisso, an author of the study, said it appeared that microplastics, along with nanoplastics, made those fatty blobs of plaque more frail, increasing the risk that they could dislodge from the artery wall, block the flow of blood in a smaller vessel and prompt a heart attack or stroke.

“This is the first evidence that microplastic pollution in the blood is related to a disease,” said Dr. Paolisso, a professor of internal medicine at the University of Campania Luigi Vanvitelli in Caserta, Italy. More research is needed to confirm the findings, he added.

There are a number of theories about how microplastics affect the body. They include the potential for inflammation caused by a foreign body that lodges in human tissue and the toxic compounds that make up many plastics, many of which are known to harm human health.

Nienke Vrisekoop, a microplastics researcher at the University Medical Center Utrecht in the Netherlands, said she found that immune cells that come in contact with microplastics die three times as quickly than those that do not. She said that the polystyrene commonly used to produce packing materials was especially toxic to the immune cells that consumed them.

Research conducted by another Dutch researcher, Barbro Melgert, found that microplastics inhibited the development of lung structures grown in her lab. Professor Melgert, a respiratory immunologist at the University of Groningen, said nylon seemed to be most damaging to the lung structures. Polyvinyl chloride, or PVC, she discovered, was the least toxic of the plastics she tested.

Professor Melgert is still trying to understand how microplastics affect living cells, but she suspects that the damage may be related to any number of chemicals that can leach from plastics into the human body.

Although she knows the results of the study do not definitively prove harm to humans, nor do they quantify the risks, previous research on nylon factory workers showed extensive lung damage among those exposed to large amounts of nylon particles.

Foreign particulate matter like asbestos, coal dust or cigarette smoke often proves problematic to human health, she noted. “If the particulate is organic and digestible, at least your body can break it down eventually and get rid of it,” Professor Melgert said. “Plastic is different. It can just stay in the lung.”

The same can most likely be said for microplastics that find their way into the brain. The discovery, arguably the new film’s most significant revelation, was made by two Turkish researchers, Sedat Gündoğdu, a biologist, and Emrah Çeltikçi, a neurosurgeon.

Dr. Gündoğdu, a researcher at Cukurova University , has been studying microplastic pollution since 2016. Over the years, he has collaborated on scores of peer-reviewed studies documenting microplastics in fisheries , soil , table salt and intravenous fluid bags , and his alarm has grown with each new discovery.

It was only a matter of time, he said, before researchers would discover microplastics in the human brain. “It’s scary but not surprising,” he said.

Of the 15 samples examined so far, six plastic particles have been identified in tissue from two patients with tumors, Dr. Gündoğdu said. It was unclear how the fragments made their way into the brain, but he said that given the documented presence of microplastics in the blood, they most likely arrived via vessels feeding the tumors.

Despite the sense of urgency and doom conveyed by “Plastic People,” Ms. Tong, the co-director and a former host of the Discovery Channel science show “ Daily Planet ,” hopes the film can inspire change, the way “Silent Spring,” the 1962 book that documented the dangers of agricultural pesticides and helped lead to a ban on DDT, did.

On an individual level, that means encouraging consumers to reduce their reliance on single-use plastics, which make up 40 percent of global plastics production, she said.

But that also means persuading political leaders to take regulatory action. At the moment, Ms. Tong has her eye on a U.N. gathering next month in Ottawa, where delegates from 175 countries will resume negotiations on a treaty proposal that would curb the explosive growth of plastic pollution. The talks have been snagged by industry opposition at times.

“It’s not like we need some remarkable new invention to address the problem,” Ms. Tong said. “We just have to use less plastic.”

Andrew Jacobs is a Times reporter focused on how healthcare policy, politics and corporate interests affect people’s lives. More about Andrew Jacobs

Unpacking the Plastic Problem

It’s in our clothes, phones and sunscreen. but also, increasingly, in marine food chains and immense garbage patches in the oceans. how do we fix this.

Recycling options are limited for personal medical devices  like inhalers and EpiPens, which are made from high-quality plastic. Some companies are trying to change that .

Here’s what scientists know so far about the health effects of nanoplastics, and what you can do to reduce your exposure .

As more consumers try to cut down on plastic waste, start-ups and big brands are hoping to usher in a new age of refillable household cleaners .

Plastic waste is everywhere, and today’s recycling systems fall far short of fixing the global mess . Here’s why, and what needs to change .

Plastic is all around us, despite its adverse effects on the planet. In a 24-hour experiment, this journalist tried to go plastic free .

Want to cut down on your plastic  use? Here are nine steps  to get you started.

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2024年3月 セキュリティアップデート解説:Microsoft社はHyper-Vの緊急の脆弱性含む64件、Adobe社は56件に対応


By: Zero Day Initiative March 15, 2024 Read time:  ( words)

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2024年3月、Adobe社とMicrosoft社が新たなセキュリティ更新をリリースしました。最新のアドバイザリの詳細を見ていきましょう。また、 こちら の動画(英語)からも概要を確認いただけます。


2024年3月、Adobe社は、Adobe Experience Manager、Premiere Pro、ColdFusion、Adobe Bridge、Lightroom、Adobe Animateに存在する脆弱性56件に対応する6つの修正パッチをリリースしました。このうち2件の脆弱性はZDI(Zero Day Initiative)プログラムを通じて報告されました。最大のアップデートは Experience Manager に対処するものあり、44件の脆弱性を修正しています。ただし、これら44件のうち42件が単純なクロスサイトスクリプティング(XSS)関連の脆弱性となっています。一方、 Adobe Animate では、4件の脆弱性が修正され、そのうちの1件は深刻度が「緊急」に分類されており、この脆弱性が悪用されると、特定のファイルを開いた際に任意のコードが実行される可能性があります。残りの3件の脆弱性は、範囲外読み込み(OOB Read)の不具合によるメモリリークに関するものです。 Premiere Pro では、悪用に際してユーザ操作の介在が必要な「緊急」に分類された2件の脆弱性に対処されています。

ColdFusion を使用しているユーザの注意点として、任意のファイルシステム読み取り関連の「緊急」に分類された脆弱性への修正が挙げられます。この場合、Adobe社はColdFusionのJDK/JRE LTSバージョンを最新のアップデートに更新することを推奨しています。 Adobe Bridge では、「緊急」の脆弱性3件、「重要」の脆弱性1件が対処されており、深刻な被害としては、脆弱性悪用により、は特定のファイルを開いた際にコード実行が可能となります。最後に Lightroom では、リモートコード実行関連の脆弱性が修正されています。なお、今回、Adobe社は、修正パッチが利用可能になった時に Xの投稿による通知 を行わないという珍しい判断を下し、コミュニケーションをメールサブスクリプションのユーザのみに限定していました。多くの人々が(筆者も含めて)通知のためにXのフィードを頼りにしているので、今後、この判断が見直されることを期待しています。

今月のリリースにより、来週開催されるPwn2Ownバンクーバーイベントでは、Adobe Readerを狙う参加者は安心できるでしょう。イベント前に参加者が準備している「攻撃手法」が修正されることはなさそうです。



2024年3月、Microsoft社は、Microsoft Windowsとそのコンポーネント、Officeとそのコンポーネント、Azure、.NET FrameworkとVisual Studio、SQL Server、Windows Hyper-V、Skype、Android用Microsoftコンポーネント、Microsoft Dynamicsに関連する脆弱性を対象とした59件の修正パッチをリリースしました。これらに加え、Chromiumベースの脆弱性も複数修正され、脆弱性の総数は64件となっています。これらの脆弱性の中には、ZDIプログラムを通じて報告されたものもあります。

今回修正対応された脆弱性の中で、2件が「緊急」、57件が「重要」に分類されています。これは3月としては比較的少ない件数であり、特に来週開催される Pwn2Ownコンテスト の直前であることを考えると、その少なさが際立ちます。ソフトウェアベンダーは通常、対象を最新の状態に更新することを前提に、可能な限り多くの問題を修正しようとします。Microsoft社は、今回のコンテストでも対象であるため、今回の少ないリリース件数がどう影響するかは注目に値します。



CVE-2024-21407  - Windows Hyper-Vにおけるリモートコード実行の脆弱性


CVE-2024-26198  – Microsoft Exchange Serverにおけるリモートコード実行の脆弱性


CVE-2024-21334  – Open Management Infrastructure (OMI) におけるリモートコード実行の脆弱性

この脆弱性は、今回のリリースで最も高いCVSSスコア9.8となっています。この脆弱性が悪用されると、インターネット上のOMIインスタンスに対して、未認証の攻撃者がリモートコード実行できる可能性があります。これらのシステムがインターネット経由でどれほどアクセス可能かははっきりしませんが、相当数に達すると推測されます。Microsoft社は、この脆弱性の悪用可能性を「低」に分類していますが、ターゲットとして魅力的なシステムにUse After Free(UAF)のような単純な脆弱性の悪用が可能であるケースを考慮すると、関連するTCPポート5986へのスキャン活動が近い将来に増加することが予想されます。

CVE-2024-21400  - Microsoft Azure Kubernetes Service 機密コンテナにおける特権昇格の脆弱性

この脆弱性が悪用されると、未認証の攻撃者が信用されていないAKS KubernetesノードおよびAKS機密コンテナにアクセスし、機密性の高いゲストやコンテナを乗っ取ることが可能になります。そして攻撃者は、認証情報を窃取したり、他のリソースに影響を及ぼすしたりすることが可能となります。これだけでも深刻ですが、問題を解決するための修正パッチ適用が簡単ではない点です。この場合は、ユーザは「az confcom」とKataイメージの最新バージョン使用を確認する必要があります。この脆弱性の告知文には、必要なコマンドに関する詳細情報が記載されていますので、必ずご確認ください。


その他のリモートコード実行の脆弱性については、先月も確認したとおり、不正なSQLサーバに接続する必要があるSQLクライアントに多数の脆弱性が存在しています。ただしこれらの脆弱性の悪用は、ソーシャルエンジニアリングの手法なしには難しいといえます。しかし、Django Backend for SQL Serverの脆弱性はその限りではないようです。この脆弱性が悪用される手法は、未検証のパラメーターを介した典型的なSQLインジェクションとなるからです。また、Windows OLEには、DLLローディングの脆弱性が存在しています。その他、SharePointにおけるリモートコード実行の脆弱性は、悪用に際しては、特別に作成されたファイルを開くようユーザを促すユーザ介入の操作が必要となります。Skype for Consumerにおける脆弱性の場合も、悪用されるには、ソーシャルエンジニアリングの手法が必要となり、さらに自動アップグレードオプションがないことから、Skypeの最新バージョンを手動でダウンロードする必要もあるようです。その他2件のリモートコード実行の脆弱性では、ターゲットシステムへの物理的なアクセスを必要とする点で少し珍しいタイプといえます。どちらの脆弱性も、悪用に際しては、攻撃者がオープンなUSBポートにデバイスに物理的に接続する必要があるようです。このような物理的な攻撃経路に関連する脆弱性への修正パッチ対応は珍しいですが、このタイプの問題に対しても更新が実施されるのは好ましいことといえます。


特権昇格関連では20件以上の脆弱性が修正対応されています。これらの脆弱性の悪用では、通常、ローカルの攻撃者が特別に作成したコードを実行することで、SYSTEM権限に昇格されます。ただし電話機能の脆弱性の場合は、「NT AUTHORITY\Network Service」という若干異なる権限が付与されるようです。また、Azure Data Studioの脆弱性では、悪用されても、アプリケーションを実行中のユーザの権限レベルにしか昇格されないようです。いずれにしろ、管理権限のアカウントで日常業務を行うべきではない注目点を改めて注意を促すものといえます。Microsoft Intune Linux Agentの脆弱性は、悪用されると、カスタム準拠スクリプトの使用時における準拠チェックを回避されてしまいます。その他、Authenticatorアプリの脆弱性は、悪用されると、二要素認証を回避できる可能性がありますが、この場合、ユーザ操作の介入が必要となるようです。つまり、攻撃者は既にターゲットのコードを実行中である中、ユーザにAuthenticatorアプリを閉じて再開させる必要があるようです。Windows Installerの脆弱性では、悪用されると、攻撃者によるファイル削除が可能となります。また、.NETフレームワークにおける類似脆弱性については、 最近公開した記事 もご参照ください。その他、OMIの脆弱性では、悪用されると、攻撃者がRootとしてOMIサーバと通信することが可能となります。また、Open Networking in the Cloud(SONiC)コンポーネントにおける脆弱性では、悪用されると、攻撃者がBorder Gateway Protocol(BGP)コンテナ内でRootに昇格し、コンテナから脱出するための特定のアクションを実行できるようになります。

セキュリティ機能バイパス関連では、Windows Defenderにおいて影響が大きい脆弱性3件に対する修正が実施されました。幸い、これらの場合、Defenderエンジンが自動的に更新されるため、基本的には何もする必要がありません。しかし、隔離された環境にいたりDefenderが無効になっていたりする場合は、Defenderのバージョンを手動で確認する必要が出てくる可能性はあります。この脆弱性が悪用されると、攻撃者によりDefenderの起動が妨げることが可能となるため、今回の修正パッチが適用されていることを必ず確認しておいてください。その他、ハイパーバイザーで保護されたコードの整合性(HVCI)に関する脆弱性は、悪用されると、コードの整合性保護が回避することが可能となります。なお、この悪用のためには、管理者レベルの権限が必要となります。管理者権限で悪用される脆弱性が修正されることは稀であるため、これも今回の珍しいケースといえます。また今回、Kerberosにおける脆弱性が修正されため、この脆弱性悪用により他のユーザになりすますことが可能だった問題が解決されました。

情報漏えい関連では、5件の脆弱性が修正対応されています。なお、今回、その中で未指定のメモリ内容漏えいに関する脆弱性は1件だけあり、これも今回の珍しいケースといえます。カーネルに関する脆弱性2件は、悪用されると、通常ではアクセスできないレジストリキーを閲覧することが可能となります。Teams for Androidの脆弱性では、悪用されると、アプリのプライベートディレクトリ内のファイルを読み取ることが可能となります。この脆弱性の修正対応のためには、 Google Play Store から手動でダウンロードする必要があります。 Outlook for Android にも同様のケースがあり、この脆弱性が悪用されると、ユーザにとって非常に重要なファイル内容等の情報が閲覧される可能性があります。また、今回のリリースでは、既に記載されているもの以外にも、さまざまな場所で発生するDoS攻撃関連の脆弱性5件への修正対応も含まれています。ただし、これらの脆弱性に関する具体的な情報や詳細は提供されていません。

なりすまし関連では、2件の脆弱性への修正対応が実施されていますが、Android用Microsoft Edgeの脆弱性への修正対応は若干異なった状況となっています。この脆弱性は、2023年3月初旬に 報告 されましたが、実際の修正はまだ提供されていません。代わりに「Android用Edgeのセキュリティ更新はすぐには利用できません」という注記がされています。Microsoft社が脆弱性情報を公開する一方で、修正を同時に提供しないのは不自然に思われますが、恐らく近い将来には更新されることでしょう。もう1件のなりすまし関連の脆弱性は、Azure SDKに存在するものであり、この脆弱性のリスクを完全な保護を得るためには、追加の手順が必要になるかもしれません。2023年10月19日以前に作成されたデプロイメントを使用している場合、Azure-coreをAzure Core Build 1.29.5以上へ、手動でアップグレードする必要があるからです。一方、10月19日以降にデプロイメントを行っている場合は、修正パッチが自動的に適用される予定です。

今回の新たな アドバイザリ としては、Microsoft社がExchange内で使用されているOracleのライブラリを廃止したことが挙げられます。これは長い間待ち望まれていた変更であり、Oracleがライブラリを更新するたびにExchangeが実質的にゼロデイ攻撃にさらされていたため、歓迎される対応といえます。

その他、Microsoft Dynamicsに存在したクロスサイトスクリプティングの脆弱性1件に修正対応が実施されています。




2024年3月にMicrosoft社が発表したCVEの全リストは こちら ご参照ください。

THE MARCH 2024 SECURITY UPDATE REVIEW By: Dustin Childs, Zero Day Initiative

翻訳:与那城 務(Core Technology Marketing, Trend Micro™ Research)

Zero Day Initiative

Vulnerability Researchers

Related Articles

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  • 攻撃グループ「Earth Lusca」が台湾総統選挙を目前に地政学的トピックを用いてサイバー諜報活動を展開
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