Artificial intelligence Archives | Page 30 of 711

NIST explores OT asset management to strengthen cybersecurity

NIST’s National Cybersecurity Center of Excellence (NCCoE) is seeking public feedback on a new project focused on operational technology (OT) asset management as the foundation for stronger OT cybersecurity.

The draft project description, Asset Management as a Foundation for OT Cybersecurity, outlines the project’s scope, challenges and technical approach. The NCCoE plans to demonstrate practical methods for OT asset discovery, inventory, configuration and change management.

The project will involve collaboration with asset owners, operators, and solution providers. The NCCoE plans to demonstrate real-world OT asset management and visibility solutions using commercially available products.

The proposal also includes a high-level reference architecture, desired technical capabilities and alignment with relevant standards, including outcomes from the NIST Cybersecurity Framework 2.0.

The NCCoE said AI is accelerating both the discovery and exploitation of vulnerabilities, making strong OT asset management increasingly important as organisations modernise industrial systems, adopt zero trust architectures and respond to AI-driven cyber threats.

Many organisations struggle to maintain a complete inventory of OT assets. Without effective asset management, activities such as risk assessment, network segmentation, vulnerability management, incident response and technology modernisation become significantly more difficult.

The NCCoE said the laboratory demonstration will support the development of source code, scripts, architectures, procedures, and guidelines. These resources are intended to help organisations gain the visibility needed to detect and respond to modern cyber threats in OT environments.

The centre is seeking input from asset owners, operators, technology providers, and cybersecurity practitioners. Feedback will help refine the project scope, use cases, reference architecture, and demonstration objectives.

Following the consultation, the NCCoE plans to recruit collaborators for project demonstrations and development activities. Public comments on the draft are open until 31 July 2026.

Why does it matter?

Operational technology underpins critical infrastructure, manufacturing and industrial operations, making accurate asset visibility a prerequisite for effective cybersecurity. As AI enables attackers to identify and exploit vulnerabilities more quickly, organisations need reliable inventories, configuration management and continuous monitoring to support risk assessments, zero trust strategies and incident response.

The project also reflects a broader shift towards practical cybersecurity guidance. By working with industry to develop reference architectures, tools and implementation guidance aligned with the NIST Cybersecurity Framework 2.0, the NCCoE aims to help organisations translate cybersecurity best practices into operational improvements across industrial environments.

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China links AI data centre to direct green electricity supply

China has launched what state media described as the country’s first AI data centre powered entirely through a direct green electricity connection, linking AI infrastructure more closely with renewable energy supply.

The facility has started operations in Zhongwei, in the Ningxia Hui Autonomous Region, a western region that has become central to China’s computing and clean-energy strategy.

Operated by China Telecom Ningxia Branch, the data centre is built to a wind-powered liquid-cooling standard. According to the company, the facility achieves a Power Usage Effectiveness rating of 1.15, supporting high-performance AI computing while reducing energy use compared with conventional data centres.

The project is part of China’s wider effort to connect computing capacity with renewable energy resources. Ningxia has already hosted large-scale projects that directly supply green electricity to data centre clusters, including a 500 MW solar facility in Zhongwei linked to China’s computing-electricity coordination model.

Zhongwei is also a key node in China’s ‘Eastern Data, Western Computing’ initiative, which aims to shift data-intensive workloads from eastern economic centres to western regions with more land and renewable-energy resources.

The new facility is expected to support AI computing, data processing and industrial digital transformation. It could also increase demand for servers, AI chips, liquid-cooling equipment and other parts of China’s domestic technology supply chain.

The project highlights how energy availability and efficiency are becoming central to AI infrastructure policy, as countries and companies face rising power demand from data centres and advanced AI systems.

Why does it matter?

AI infrastructure is becoming an energy-policy issue. China’s green-powered data centre model shows how governments may try to match growing AI compute demand with renewable-energy deployment, regional data-centre planning and industrial supply-chain development. For China, the project also supports a broader strategy of moving compute workloads westward, reducing pressure on eastern cities and using renewable resources in regions such as Ningxia. The challenge will be proving that such facilities can deliver reliable AI computing at scale while genuinely reducing emissions across the full power and data-centre system.

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Google proposes a balanced approach to AI governance in the US

Google has published a policy paper proposing a two-track approach to AI governance in the United States, separating oversight of frontier AI models from rules for widely deployed AI applications.

The paper argues that AI policy should avoid what Google describes as a false choice between over-regulation and no regulation. Instead, the company calls for a pragmatic, evidence-based framework that treats the most advanced AI systems differently from everyday AI tools such as chatbots.

For frontier AI, Google proposes the creation of a Frontier AI Regulatory Organisation, or FARO. The industry-funded body would operate under federal oversight and develop standards for safety, security, incident reporting and transparency.

Google says FARO could set scientific benchmarks for frontier capabilities, particularly in areas such as cybersecurity and chemical, biological, radiological and nuclear risks. It could also oversee independent audits and require frontier AI companies to publish and follow safety frameworks before releasing highly capable models.

For widely deployed AI applications, Google argues that the federal government should rely mainly on existing legal frameworks, with targeted updates where needed. The paper says policy should focus on real-world harms and outputs rather than micromanaging AI development.

The company identifies several priority areas, including workforce preparedness, child safety, information integrity, copyright, privacy and energy infrastructure for data centres.

Google supports measures such as AI interaction guidelines for children, disclosures that chatbots are not sentient, rules for self-harm-related queries, watermarking and provenance standards for generative AI, privacy-enhancing technologies and workforce reskilling.

The paper presents the model as a way to address national security and consumer protection risks while preserving US leadership in AI development.

Why does it matter?

Google’s paper is a significant industry intervention in the US AI policy debate. Its two-track model reflects a broader governance trend: frontier AI is increasingly being treated as a national security and safety issue, while everyday AI applications are being handled through consumer protection, child safety, privacy, copyright and labour policy. The proposal could influence federal discussions, but it also reflects Google’s own regulatory preferences, including industry-funded oversight, confidential audit reports and reliance on existing law for many AI applications.

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EU signs Pax Silica Declaration on AI supply chains

The European Commission has signed the Pax Silica Declaration on behalf of the EU, joining an international initiative focused on AI security and resilient silicon supply chains.

Pax Silica is a US-led initiative that aims to strengthen cooperation among allies and trusted partners across the AI supply chain, from critical minerals and energy inputs to semiconductor manufacturing, AI infrastructure and logistics.

The Commission said secure access to silicon and related technologies is becoming increasingly important as AI reshapes economies, security and industrial competitiveness.

The declaration commits signatories to closer cooperation on trusted technology ecosystems and more resilient supply chains. It also aims to reduce strategic dependencies and improve coordination on the materials, infrastructure and manufacturing capacity needed for AI development.

The EU’s signature follows the adoption of the European Technological Sovereignty Package, which includes Chips Act 2.0 and measures to strengthen Europe’s capacity in semiconductors, AI, cloud and open-source technologies.

The Commission said participation in Pax Silica could support European businesses, strengthen international partnerships and contribute to Europe’s broader technological sovereignty objectives.

Why does it matter?

AI development depends on far more than models and software. Advanced chips, critical minerals, energy, manufacturing capacity, cloud infrastructure and logistics are becoming strategic layers of the AI economy. By joining Pax Silica, the EU is linking AI competitiveness and security to semiconductor supply-chain resilience and cooperation with trusted partners. The move also shows how digital sovereignty is increasingly pursued through both domestic capacity-building and selective international alignment.

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UN report outlines AI standards for Digital Public Goods

A new report from the United Nations University Institute in Macau, the Asian Development Bank (ADB), and the UN Office for Digital and Emerging Technologies examines the conditions under which AI systems can qualify as Digital Public Goods. The study was launched during UN Open Source Week 2026 and focuses on aligning AI development with public interest goals.

The report argues that AI systems cannot be assessed in the same way as conventional open-source software because they rely on datasets, model weights, computing infrastructure and ongoing governance. While openness can improve transparency and reuse, it does not automatically guarantee safety, equity or alignment with the Sustainable Development Goals (SDGs).

The study concludes that AI governance should be treated as a continuous lifecycle process rather than a one-time certification exercise. It also highlights that equitable access depends on enabling factors such as computing infrastructure, local-language datasets and institutional capacity, particularly in developing countries.

To address these challenges, the report proposes a SAFE framework covering Standards, Accountability, Finance and Equity. It recommends stronger stewardship of public-interest data, improved accountability mechanisms and greater investment in local AI evaluation capacity to support inclusive and responsible AI deployment.

Why does it matter?

The report broadens the debate around AI governance by arguing that openness alone is not enough to ensure that AI serves the public interest. As governments increasingly adopt AI in public services and development programmes, questions of governance, accountability and long-term oversight are becoming as important as technical performance.

It also highlights the growing role of Digital Public Goods in international AI policy. By emphasising equitable access to computing resources, local datasets and institutional capacity, the report argues that AI should be developed as shared public infrastructure that supports sustainable development rather than reinforcing existing digital divides.

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New MIT development reduces energy use in AI systems

Researchers from MIT and Microsoft have developed a system called Murakkab to improve the speed and energy efficiency of agentic AI workflows.

Agentic workflows combine multiple AI models and external tools to complete complex, multi-step tasks, such as analysing video or generating code. MIT said these systems are becoming more important for cloud providers, but their fragmented design can waste computation, energy and money.

Murakkab allows developers to describe an AI application in high-level terms rather than manually specifying every model, tool, hardware choice and execution step. The system then identifies suitable models and tools, decides which components should run sequentially or in parallel, and selects hardware resources for cloud deployment.

The system can adjust configurations during execution based on user priorities such as accuracy, speed, latency and cost. It also gives cloud providers more visibility into workflows, allowing them to allocate computing resources more efficiently across multiple tasks.

In tests of video-question-answering and code-generation workflows, Murakkab met user requirements while using about 35% of the computational resources required by other methods. It also consumed about 27% as much energy and cost less than 25% as much as the comparison approaches.

In one case, the system reduced energy consumption by more than an order of magnitude with only about a 2% drop in accuracy. The researchers plan to expand Murakkab to more complex workflows and larger computing clusters.

Why does it matter?

Agentic AI systems are becoming more complex and resource-intensive, especially as cloud providers deploy workflows that combine many models, tools and hardware configurations. Murakkab points to a shift from optimising individual models to optimising the whole AI workflow and its cloud deployment. That matters because energy use, compute costs, and data centre capacity are becoming central constraints on AI growth.

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EU targets AWS and Azure under the DMA

The European Commission has informed Amazon and Microsoft of its preliminary view that their cloud computing services, Amazon Web Services and Microsoft Azure, should be designated as gatekeepers under the Digital Markets Act.

The move could extend the DMA’s reach into cloud infrastructure, a sector the Commission describes as critical to Europe’s digital economy and AI development.

The Commission opened market investigations into AWS and Azure in November 2025. It has now been provisionally concluded that both services act as important gateways between businesses and customers in the EU, despite not meeting the DMA’s standard quantitative thresholds.

According to the Commission, AWS and Azure benefit from large and established user bases, high switching costs, loyalty effects, broad cloud ecosystems and long-standing market positions. It also said their AI tool portfolios and partnerships are becoming increasingly important for cloud customers.

Amazon and Microsoft now have the opportunity to examine the investigation files and respond to the preliminary findings. If the Commission confirms its assessment, AWS and Azure would be designated as gatekeepers, and the companies would have six months to comply with DMA obligations.

The Commission said fair and competitive cloud markets are important for secure, sustainable and interoperable cloud services in Europe. It also linked the case to Europe’s wider technological sovereignty objectives, as cloud infrastructure underpins AI systems, enterprise software and public services.

Why does it matter?

The case shows how the EU competition policy is moving deeper into the infrastructure behind the AI economy. Cloud platforms are no longer just business services; they shape access to compute, data, AI tools, software ecosystems and switching options for companies and public institutions. If AWS and Azure are designated as DMA gatekeepers, the decision could affect cloud interoperability, customer lock-in and the balance of power between US hyperscalers and European cloud providers.

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China’s latest supercomputer strengthens AI ambitions

China has regained the world’s leading position in supercomputing after the LineShine system became the fastest computer in the latest TOP500 ranking, replacing the US’s El Capitan at the top of the list.

The achievement marks China’s return to first place for the first time since 2017 and highlights the growing strategic importance of high-performance computing in the AI era.

Unlike many recent AI-focused supercomputers that rely heavily on graphics processing units (GPUs), LineShine achieves exascale performance using conventional central processing units (CPUs).

Beyond topping benchmark rankings, the system is expected to support scientific research, advanced simulations, climate modelling, pharmaceutical development and the training of increasingly sophisticated AI models.

The announcement also reflects the broader ambition of China to strengthen technological leadership while presenting its innovation ecosystem as a contributor to global technological development.

Europe also remains a major player in high-performance computing. Four European systems rank among the world’s ten fastest supercomputers, while the EU continues to invest in AI factories, next-generation supercomputing infrastructure and collaborative research centres.

The growing investment in supercomputers reflects how computing infrastructure is increasingly being treated as a strategic asset alongside semiconductors, cloud infrastructure and advanced data centres.

As governments increasingly link AI capabilities with economic competitiveness, scientific leadership and national security, access to world-class computing resources is becoming one of the defining factors shaping the global technology balance.

Why does it matter?

The latest TOP500 ranking underline that computing capacity is becoming a defining factor in AI development and scientific competitiveness. As frontier AI models require ever-greater computational resources for training and inference, access to world-class supercomputers is emerging as a strategic advantage alongside semiconductor manufacturing and cloud infrastructure.

China’s return to the top of the rankings also highlights the geopolitical dimension of high-performance computing. At the same time, continued European investment in AI factories and supercomputing infrastructure reflects a broader effort to strengthen technological sovereignty and reduce dependence on external computing resources as countries compete for leadership in AI and advanced research.

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EDPS strengthens monitoring of emerging technologies

The European Data Protection Supervisor (EDPS) has developed a structured framework for monitoring emerging technologies and assessing their implications for privacy and data protection. As digitalisation accelerates, the EDPS recognises that some new technologies do not merely improve existing processes but fundamentally alter how personal data is handled, requiring proactive and ongoing scrutiny.

At the heart of the framework is an annual monitoring cycle that moves from early signal detection to in-depth analysis and public engagement. The EDPS works with the Joint Research Centre’s TIM Analytics service to identify technologies at an early stage and prioritise those most likely to affect data protection over the short and medium term.

The main output of this foresight work is TechSonar, the EDPS’s flagship report on technologies expected to become relevant within the next one to five years. Designed for a broad audience, it outlines emerging technology trends and assesses their potential implications for personal data protection.

Complementing TechSonar, the TechDispatch series provides more detailed analysis of individual technologies, including factual descriptions, preliminary privacy assessments and consideration of how they interact with GDPR principles and data subject rights.

Complementing these publications is the Internet Privacy Engineering Network (IPEN), established by the EDPS in 2014. At least once a year, IPEN brings together public authorities, academics, open-source projects, and private businesses to discuss engineering solutions to privacy challenges, with findings feeding back into the broader technology monitoring work.

The EDPS also coordinates the Internet Privacy Engineering Network (IPEN), established in 2014, which brings together regulators, researchers, open-source communities and industry to discuss technical solutions to privacy challenges and feed those insights into its wider technology monitoring work. Recent activities have included a new video series on AI literacy and a newsletter covering AI governance, the Digital Omnibus debate, and AI use in hiring practices.

Why does it matter?

Emerging technologies such as AI are evolving faster than traditional regulatory processes, making early assessment increasingly important for protecting privacy and fundamental rights. By identifying technologies before they become mainstream, the EDPS aims to help policymakers, regulators and public institutions anticipate risks rather than respond only after new technologies are widely deployed.

The framework also supports greater consistency in European data protection governance. Through publications such as TechSonar and TechDispatch, together with collaboration via IPEN, the EDPS provides a common evidence base that can inform policy development, regulatory enforcement and privacy-by-design approaches across the EU as new technologies continue to emerge.

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UN secretary-general calls for greater transparency on AI’s climate impact

UN Secretary-General António Guterres has called on AI companies to publicly disclose the environmental impact of their operations, including carbon emissions, water consumption, and land use. Speaking at London Climate Action Week, Guterres proposed an AI Environmental Transparency Initiative, arguing that communities are often left without clear information about the environmental impact of nearby data centre developments.

Citing a UN study, Guterres said data centres consumed more electricity in 2025 than all but ten countries, accounting for around 1.5% of global electricity demand. That share could approach 3% by 2030, while AI-related water consumption and pollution are also projected to rise significantly. By 2030, that figure is projected to nearly double to close to 3 per cent, while the water use and pollution associated with AI are also expected to double within four years.

Guterres noted that coal still provides around 30% of the electricity used by data centres globally, while renewables account for approximately 27%. He called on AI companies to power their facilities entirely with renewable energy by 2030. Guterres called on AI firms to commit to powering their facilities entirely from renewable sources such as wind and solar by 2030, though existing clean energy commitments from major tech companies have already been complicated by the rapid pace of AI deployment.

Guterres linked the proposal to broader concerns about climate change and energy security, arguing that both are rooted in continued dependence on fossil fuels. He noted that the planet has just endured its eleven hottest years on record, and that last year marked the first time the three-year global temperature average broke through the 1.5 degrees Celsius threshold set by the 2015 Paris Agreement.

He also noted that renewable energy surpassed one-third of global electricity generation in 2025 for the first time, while coal’s share fell below one-third, although he cautioned that rising AI-related electricity demand could complicate progress.

Coal’s share of global generation also fell below one-third for the first time, though significant challenges remain, particularly given policy reversals in the US under President Donald Trump, who has embraced fossil fuels and cut support for renewables.

Guterres, whose term ends in December 2026, will convene world leaders again at the annual COP climate summit later this year. He reiterated calls for every major emitter to accelerate action, reduce methane emissions, and move away from coal, oil, and gas, with the speech delivered during a heatwave affecting much of London and Europe.

Why does it matter?

The rapid expansion of AI infrastructure is bringing its environmental footprint under increasing scrutiny. As data centres consume growing amounts of electricity and water, policymakers are beginning to ask whether AI companies should be subject to the same transparency expectations applied to other carbon-intensive industries. Standardised reporting could provide governments, investors and local communities with a clearer understanding of AI’s environmental impact.

The proposal also highlights the growing intersection between AI governance and climate policy. As countries seek to expand AI capabilities while meeting emissions targets, the availability of clean energy, sustainable infrastructure and transparent environmental reporting is likely to become an increasingly important part of discussions on responsible AI development.

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