AI, cloud computing, and cross-border data flows have made questions about control and jurisdiction increasingly important for governments and businesses. In Asia, the debate around digital sovereignty often focuses on ‘US versus non-US cloud’ providers or data localisation.
Such simplifications miss the practical challenges organisations face when choosing hosting locations or training AI models while navigating diverse regulatory regimes.
At the same time, Asia’s digital economy is building its own regulatory foundations. In Vietnam and Indonesia, new rules such as Vietnam’s Decree 53 and Indonesia’s data protection framework show how governments are shaping data governance while still relying on global cloud and AI platforms. Most organisations across the region continue to operate using a mix of local, regional, and international providers.
Organisations must address key questions about data jurisdiction and workload mobility when risks change. They must also control who can access sensitive systems during incidents. Digital sovereignty is clearer when seen through three pillars: data sovereignty, technical sovereignty, and operational sovereignty.
Data sovereignty is about jurisdiction, not just data storage. As AI regulation expands, businesses need to know which authorities can access their data and how it may be used. Technical sovereignty is the ability to move or redesign systems as regulations or geopolitics shift. Multi-cloud and hybrid strategies help organisations remain adaptable.
Operational sovereignty focuses on governance and control. It addresses who can access systems, from where, and under what safeguards, thus linking sovereignty directly to cybersecurity and incident response.
For Asia-Pacific organisations, digital sovereignty should not be a simple procurement checklist. Instead, it should guide cloud and AI strategies from the start, ensuring legal clarity, technical flexibility, and operational trust as the digital landscape evolves.
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New York lawmakers are considering legislation that would ban AI chatbots from providing legal or medical advice. The bill aims to stop automated systems from impersonating licensed professionals such as doctors and lawyers.
The proposal would also require chatbot operators to clearly inform users that they are interacting with an AI system. Notices must be prominent, written in the same language as the chatbot, and use a readable font.
A key feature of the bill is a private right of action. However, this would allow users to file civil lawsuits against chatbot owners who violate the law, recovering damages and legal fees. Experts say this enforcement tool strengthens the rules and deters abuse.
Supporters of the legislation argue it protects New Yorkers’ safety, particularly minors. Other bills in the same package would regulate online platforms like Roblox and set standards for generative AI, synthetic content, and the handling of biometric data.
The bill’s author, state Senator Kristen Gonzalez, said AI innovation should not come at the expense of public safety. She pointed to recent cases where AI chatbots were linked to harmful outcomes for minors, highlighting the need for transparency and accountability.
If passed, the law would take effect 90 days after the governor signs it. Lawmakers hope it will balance innovation with user protection, ensuring AI tools are used responsibly and safely across the state.
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The operation, led by Microsoft, Europol, and several industry partners, targeted the infrastructure behind Tycoon 2FA, which enabled large-scale phishing campaigns against more than 500,000 organisations each month.
By mid-2025, Tycoon 2FA accounted for 62% of the phishing attempts blocked by Microsoft, with over 30 million malicious emails blocked in a single month. Experts link the platform to around 96,000 global victims since 2023, including 55,000 Microsoft customers.
Researchers from Resecurity found cybercriminals widely used the platform to impersonate legitimate users and gain unauthorised access to accounts such as Microsoft 365, Outlook and Gmail. The service relied on techniques such as URL rotation using open redirect vulnerabilities and the misuse of Cloudflare Workers to hide malicious infrastructure.
‘The author of Tycoon 2FA is actively updating the tool with regular kit updates,’ reads the report published by Resecurity. ‘What makes Tycoon 2FA so special is that the kit effectively combines multiple methods to deliver phishing at scale—from PDF attachments to QR codes.’
Authorities say taking the infrastructure offline disrupts a key pathway for account takeover attacks and prevents additional threats, such as data theft, ransomware, business email compromise, and financial fraud.
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Clemson University has introduced ChatGPT Edu to its students, faculty, and staff, providing them free access to the secure, institutionally managed version of the AI platform.
The rollout is part of Clemson’s partnership with OpenAI. It forms part of the university’s broader AI Initiative, which aims to develop a human-centred approach to AI across education, research, and operations.
University officials said the ChatGPT Edu environment will expand access to generative AI tools while ensuring institutional data remains protected and is not used to train external AI systems.
Members of the Clemson community who want to use the platform must request access through a ChatGPT Edu account request form. Once approved, accounts are automatically created, and users can sign in through Clemson’s single sign-on system.
Even if students or staff members already have a ChatGPT account linked to their Clemson email, they will still need to request access to ChatGPT Edu. After approval, they can merge your current account or download your chat history before creating a new one.
The university said the launch reflects its view that access to emerging technologies should be paired with clear guidance and responsible use. Users are advised to review Clemson’s updated AI guidelines before using the system.
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A draft initiative from the European Commission seeks to introduce a new legal structure designed to simplify how companies operate across the EU.
The proposal, often referred to as the ‘EU Inc’ initiative, explores the creation of a so-called ’28th regime’ that would exist alongside national corporate frameworks used by member states.
A concept that aims to provide startups and technology firms with a single legal structure that applies across the EU.
Instead of navigating different national rules in each country, companies could operate under a unified regulatory model intended to reduce administrative barriers and encourage cross-border innovation.
According to the draft, the initiative may rely on an EU regulation rather than separate national legislation. Such an approach could enable faster implementation, as the EU regulations apply directly across all member states without requiring domestic transposition.
However, the legal basis of the proposal could raise institutional concerns. Using a regulation as the primary mechanism may constitute an unconventional shortcut in the EU lawmaking, potentially sparking debate among policymakers over the approach’s scope and legitimacy.
The initiative reflects broader efforts within the Union to simplify regulatory frameworks and strengthen the competitiveness of European startups. If adopted, the ‘EU Inc’ model could reshape how young companies expand across the single market.
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On 28 February 2026, Anthropic’s Claude rose to No. 1 in Apple’s US App Store free rankings, overtaking OpenAI’s ChatGPT. The surge came shortly after OpenAI announced a partnership with the US Department of Defense (DoD), making its technology available to the US Army. The development prompted discussion among users and observers about whether concerns over military partnerships were influencing the shift to alternative AI tools.
Mere hours before the USD $200 million OpenAI-DoD deal was finalised, Anthropic was informed that its potential deal with the Pentagon had fallen through, largely because the AI company refused to relinquish total control of its technology for domestic mass surveillance. According to reporting, discussions broke down after Anthropic declined to grant the US government unrestricted control over its models, particularly for potential uses related to large-scale surveillance.
Following the breakdown of negotiations, US officials reportedly designated Anthropic as a ‘supply chain risk to national security’. The decision effectively limited the company’s participation in certain defence-related projects and highlighted growing tensions between AI developers’ safety policies and government expectations regarding national security technologies.
I resigned from OpenAI. I care deeply about the Robotics team and the work we built together. This wasn’t an easy call. AI has an important role in national security. But surveillance of Americans without judicial oversight and lethal autonomy without human authorization are…
The debate over military partnerships sparked internal and industry-wide discussion. Caitlin Kalinowski, the former head of AR glasses hardware at Meta and the hardware leader at OpenAI, resigned soon after the US DoD deal, citing ethical concerns about the company’s involvement in military AI applications.
AI has driven recent technological innovation, with companies like Anduril and Palantir collaborating with the US DoD to deploy AI on and off the battlefield. The debate over AI’s role in military operations, surveillance, and security has intensified, especially as Middle East conflicts highlight its potential uses and risks.
Against this backdrop, the dispute between Anthropic and the Pentagon reflects a wider debate on how AI should be used in security and defence. Governments are increasingly relying on private tech companies to develop the systems that shape modern military capabilities, while those same companies are trying to set limits on how their technologies can be used.
As AI becomes more deeply integrated into security strategies around the world, the challenge may no longer be whether the technology will be used, but how it should be governed. The question is: who should ultimately decide where the limits of military AI lie?
Anthropic’s approach to military AI
Anthropic’s approach is closely tied to its concept of ‘constitutional AI’, a training method that guides how the model behaves by embedding a set of principles directly into its responses. Such principles are intended to reduce harmful outputs and ensure the system avoids unsafe or unethical uses. While such safeguards are intended to improve reliability and trust, they can also limit how the technology can be deployed in more sensitive contexts such as military operations.
Anthropic’s Constitution says its AI assistant should be ‘genuinely helpful’ to people and society, while avoiding unsafe, unethical, or deceptive actions. The document reflects the company’s broader effort to build safeguards into model deployment. In practice, Anthropic has set limits on certain applications of its technology, including uses related to large-scale surveillance or military operations.
Anthropic presents these safeguards as proof of its commitment to responsible AI. Reports indicate that concerns over unrestricted model access led to the breakdown in talks with the US DoD.
At the same time, Anthropic clarifies that its concerns are specific to certain uses of its technology. The company does not generally oppose cooperation with national security institutions. In a statement following the Pentagon’s designation of the company as a ‘supply chain risk to national security’, CEO Dario Amodei said, ‘Anthropic has much more in common with the US DoD than we have differences.’ He added that the company remains committed to ‘advancing US national security and defending the American people.’
The episode, therefore, highlights a nuanced position. Anthropic appears open to defence partnerships but seeks to maintain clearer limits on the deployment of its AI systems. The disagreement with the Pentagon ultimately reflects not a fundamental difference in goals, but rather different views on how far military institutions should be able to control and use advanced AI technologies.
Anthropic’s position illustrates a broader challenge facing governments and tech companies as AI becomes increasingly integrated into national security systems. While military and security institutions are eager to deploy advanced AI tools to support intelligence analysis, logistics, and operational planning, the companies developing these technologies are also seeking to establish safeguards for their use. Anthropic’s willingness to step back from a major defence partnership and challenge the Pentagon’s response underscores how some AI developers are trying to set limits on military uses of their systems.
Defence partnerships that shape the AI industry
While Anthropic has taken a cautious approach to military deployment of AI, other technology companies have pursued closer partnerships with defence institutions. One notable example is Palantir, the US data analytics firm co-founded by Peter Thiel that has longstanding relationships with numerous government agencies. Documents leaked in 2013 suggested that the company had contracts with at least 12 US government bodies. More recently, Palantir has expanded its defence offering through its Artificial Intelligence Platform (AIP), designed to support intelligence analysis and operational decision-making for military and security institutions.
Another prominent player is Anduril Industries, a US defence technology company focused on developing AI-enabled defence systems. The firm produces autonomous and semi-autonomous technologies, including unmanned aerial systems and surveillance platforms, which it supplies to the US DoD.
Shield AI, meanwhile, is developing autonomous flight software designed to operate in environments where GPS and communications may be unavailable. Its Hivemind AI platform powers drones that can navigate buildings and complex environments without human control. The company has worked with the US military to test these systems in training exercises and operational scenarios, including aircraft autonomy projects aimed at supporting fighter pilots.
The aforementioned partnerships illustrate how the US government has increasingly embraced AI as a key pillar of national defence and future military operations. In many cases, these technologies are already being used in operational contexts. Palantir’s Gotham and AIP, for instance, have supported US military and intelligence operations by processing satellite imagery, drone footage, and intercepted communications to help analysts identify patterns and potential threats.
Other companies are contributing to defence capabilities through autonomous systems development and hardware integration. Anduril supplies the US DoD with AI-enabled surveillance, drone, and counter-air systems designed to detect and respond to potential threats. At the same time, OpenAI’s technology is increasingly being integrated into national security and defence projects through growing collaboration with US defence institutions.
Such developments show that AI is no longer a supporting tool but a fundamental part of military infrastructure, influencing how defence organisations process information and make decisions. As governments deepen their reliance on private-sector AI, the emerging interplay among innovation, operational effectiveness, and oversight will define the central debate on military AI adoption.
The potential benefits of military AI
The debate over Anthropic’s restrictions on military AI use highlights the reasons governments invest in such technologies: defence institutions are drawn to AI because it processes vast amounts of information much faster than human analysts. Military operations generate massive data streams from satellites, drones, sensors, and communication networks, and AI systems can analyse them in near real time.
In 2017, the US DoD launched Project Maven to apply machine learning to drone and satellite imagery, enabling analysts to identify objects, movements, and potential threats on the battlefield faster than with traditional manual methods.
AI is increasingly used in military logistics and operational planning. It helps commanders anticipate equipment failures, enables predictive maintenance, optimises supply chains, and improves field asset readiness.
Recent conflicts have shown that AI-driven tools can enhance military intelligence and planning. In Ukraine, for example, forces reportedly used software to analyse satellite imagery, drone footage, and battlefield data. Key benefits include more efficient target identification, real-time tracking of troop movements, and clearer battlefield awareness through the integration of multiple data sources.
AI-assisted analysis has been used in intelligence and targeting during the Gaza conflict. Israeli defence systems use AI tools to rapidly process large datasets for surveillance and intelligence operations. The tools help analysts identify potential militant infrastructure, track movements, and prioritise key intelligence, thus speeding up information processing for teams during periods of high operational activity.
More broadly, AI is transforming the way militaries coordinate across land, air, sea, and cyber domains. AI integrates data from diverse sources, equipping commanders to interpret complex operational situations and enabling faster, informed decision-making. The advances reinforce why many governments see AI as essential for future defence planning.
Ethical concerns and Anthropic’s limits on military AI
Despite the operational advantages of military AI, its growing role in national defence systems has raised ethical concerns. Critics warn that overreliance on AI for intelligence analysis, targeting, or operational planning could introduce risks if the systems produce inaccurate outputs or are deployed without sufficient human oversight. Even highly capable models can generate misleading or incomplete information, which in high-stakes military contexts could have serious consequences.
Concerns about the reliability of AI systems are also linked to the quality of the data they learn from. Many models still struggle to distinguish authentic information from synthetic or manipulated content online. As generative AI becomes more widespread, the risk that systems may absorb inaccurate or fabricated data increases, potentially affecting how these tools interpret intelligence or analyse complex operational environments.
Questions about autonomy have also become a major issue in discussions around military AI. As AI systems become increasingly capable of analysing battlefield data and identifying potential targets, debates have emerged over how much decision-making authority they should be given. Many experts argue that decisions involving the use of lethal force should remain under meaningful human control to prevent unintended consequences or misidentification of targets.
Another area of concern relates to the potential expansion of surveillance capabilities. AI systems can analyse satellite imagery, communications data, and online activity at a scale beyond the capacity of human analysts alone. While such tools may help intelligence agencies detect threats more efficiently, critics warn that they could also enable large-scale monitoring if deployed without clear legal and institutional safeguards.
It is within this ethical landscape that Anthropic has attempted to position itself as a more cautious actor in the AI industry. Through initiatives such as Claude’s Constitution and its broader emphasis on AI safety, the company argues that powerful AI systems should include safeguards that limit harmful or unethical uses. Anthropic’s reported refusal to grant the Pentagon unrestricted control over its models during negotiations reflects this approach.
The disagreement between Anthropic and the US DoD therefore highlights a broader tension in the development of military AI. Governments increasingly view AI as a strategic technology capable of strengthening defence and intelligence capabilities, while some developers seek to impose limits on how their systems are deployed. As AI becomes more deeply embedded in national security strategies, the question may no longer be whether these technologies will be used, but who should define the boundaries of their use.
Military AI and the limits of corporate control
Anthropic’s dispute with the Pentagon shows that the debate over military AI is no longer only about technological capability. Questions of speed, efficiency, and battlefield advantage now collide with concerns over surveillance, autonomy, human oversight, and corporate responsibility. Governments increasingly see AI as a strategic asset, while companies such as Anthropic are trying to draw boundaries around how far their systems can go once they enter defence environments.
Contrasting approaches across the industry make the tension even clearer. Palantir, Anduril, Shield AI, and OpenAI have moved closer to defence partnerships, reflecting a broader push to integrate advanced AI into military infrastructure. Anthropic, by comparison, has tried to keep one foot in national security cooperation while resisting uses it views as unsafe or unethical. A divide of that kind suggests that the future of military AI may be shaped as much by company policies as by government strategy.
The growing reliance on private firms to build national security technologies has made governance harder to define. Military institutions want flexibility, scale, and operational control, while AI developers increasingly face pressure to decide whether they are simply suppliers or active gatekeepers of how their models are deployed. Anthropic’s position does not outright defence cooperation, but it does expose how fragile the relationship becomes when state priorities and corporate safeguards no longer align.
Military AI will continue to expand, whether through intelligence analysis, logistics, surveillance, or autonomous systems. Governance, however, remains the unresolved issue at the centre of that expansion. As AI becomes more deeply embedded in defence policy and military planning, should governments alone decide how far these systems can go, or should companies like Anthropic retain the power to set limits on their use?
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Researchers in the US have found that AI analysis of mammograms may help identify women at risk of heart disease. The study examined breast scans to measure calcium deposits in arteries, a sign linked to cardiovascular problems.
Scientists from Emory University in Atlanta analysed screening data from more than 120,000 women. Results showed women with higher levels of arterial calcium detected in mammograms faced significantly greater risk of heart attacks or strokes.
Researchers reported that even women under 50 years old showed increased cardiovascular risk when calcium deposits appeared on scans. Experts say the findings suggest routine breast screening could reveal hidden heart health risks.
Doctors in Atlanta say AI could allow mammograms to act as a dual screening tool for breast cancer and cardiovascular disease. Further research is planned before hospitals in the US widely adopt the method.
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Cybersecurity researchers have uncovered a malware campaign spreading through over 100 GitHub repositories disguised as free software tools. Hackers used SEO-heavy descriptions to make their fake repositories appear high in search results, close to legitimate software.
Users searching for popular programs were directed to counterfeit download pages. These pages offered ZIP files containing BoryptGrab, a malware designed to steal data from infected Windows systems. The files were disguised as cracked software, gaming cheats, or utility tools.
The malware collects sensitive information, including browser passwords, cookies, and cryptocurrency wallet details. It can access nine major browsers, including Chrome, Edge, Firefox, Opera, Brave, and Vivaldi, and bypass some security protections.
Certain variants also install additional tools allowing remote access and persistent control over infected machines. However, this enables hackers to run commands, maintain ongoing access, and steal more information without the user’s knowledge.
Trend Micro, the cybersecurity firm that reported the campaign, noted some code and logs suggest a possible Russian origin, though attribution is not confirmed. Experts warn that GitHub and search engine manipulation make this attack method especially dangerous.
Users are advised to download software only from trusted sources and to verify the authenticity of the repository. Organisations should follow security best practices such as software allowlisting, maintaining inventory, and removing unauthorised applications to prevent similar attacks.
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AI is increasingly viewed as a key global infrastructure. The CEO of Nvidia argues that AI should not be seen merely as software but as a foundational technology shaping economies and industries. As a result, companies and governments worldwide are expected to build and rely on AI systems increasingly.
At the same time, AI infrastructure expansion is still in its early stages. Nvidia’s CEO notes that although hundreds of billions of dollars have already been invested in data centres and computing systems, the broader AI buildout will likely require trillions of dollars in additional investment.
Moreover, governance and access decisions will play a critical role. According to Nvidia’s CEO, choices about how quickly AI is developed, who can access it, and how it is regulated will ultimately shape the technology’s long-term impact on society.
In addition, AI differs fundamentally from traditional software. While conventional software follows prewritten instructions, AI systems generate responses dynamically based on context. Consequently, AI can produce new outputs rather than simply retrieving stored commands.
Furthermore, AI development depends on multiple interconnected technological layers. The CEO of Nvidia describes a five-layer stack composed of energy, chips, infrastructure, models, and applications. Each layer supports the next, meaning AI services rely on everything from electricity supply to advanced computing hardware.
Finally, AI may also reshape the labour market. Nvidia’s CEO suggests that as AI increases productivity, companies could expand operations and create new jobs, particularly in infrastructure development and technical fields.
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Food safety monitoring across the EU is receiving a technological upgrade with the launch of TraceMap, a new AI platform designed to detect food fraud, contamination and disease outbreaks more quickly.
The European Commission introduced the tool as part of efforts to strengthen consumer protection and improve oversight of the agri-food supply chain.
TraceMap helps authorities analyse large volumes of data related to food production, distribution and trade. By identifying connections between operators, shipments and supply chains, the system allows investigators to spot suspicious activity and potential safety risks earlier.
National authorities in the EU member states can already access the platform, enabling them to conduct more targeted inspections and investigations without requiring additional resources.
The platform draws on data from existing EU systems such as the Rapid Alert System for Food and Feed (RASFF) and the Trade Control and Expert System (TRACES). Using AI to structure and interpret information, TraceMap can reveal patterns in production and trade flows that may indicate contamination, fraud, or other irregularities in the food supply chain.
Early testing of the platform has already demonstrated its practical value. A pilot version of TraceMap helped authorities identify and recall infant milk formula produced with contaminated ARA oil originating from China.
European officials say the system will strengthen the EU’s ability to respond rapidly to food safety risks while improving monitoring of both domestic production and imported products.
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