Google’s cybersecurity division, Mandiant, has warned about the growing threat of AI-driven adaptive malware, highlighting how AI is reshaping the cyber threat landscape.
According to a recent report, adaptive malware can modify its behaviour and code in response to the environment it encounters, thereby evading traditional security tools. By analysing the security systems protecting a target, the malware can rewrite parts of its code to bypass detection.
Unlike traditional malware, which typically follows fixed instructions, adaptive malware can adjust its behaviour during an attack. This capability makes it more difficult for conventional cybersecurity tools to detect and block malicious activity.
Mandiant noted that such malware is increasingly associated with advanced persistent threat (APT) groups that conduct long-term, targeted cyber operations. These groups often pursue espionage objectives or financial gain while maintaining prolonged access to compromised systems.
AI is also being used to automate elements of cyberattacks. Machine learning algorithms allow malicious software to anticipate defensive measures and adjust its behaviour in real time. In some cases, attackers are integrating AI into broader automated attack chains. AI-driven malware can gather information, adapt its strategy, and continue operating with minimal human intervention.
Security researchers say autonomous AI agents may be capable of managing multiple stages of an attack, including reconnaissance, exploitation, and persistence, while remaining undetected.
To address these evolving threats, Mandiant recommends that organisations strengthen their cybersecurity strategies by deploying advanced detection and response tools, including AI-based systems that can identify anomalous behaviour. As AI capabilities continue to develop, cybersecurity experts say understanding adaptive malware and automated attack techniques will be essential for organisations seeking to protect their systems and data.
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Cybersecurity risks are increasing as digital connectivity expands across governments, businesses and households.
According to Thales Group, a growing number of connected devices and digital services has significantly expanded the potential entry points for cyberattacks.
AI is reshaping the cybersecurity landscape by enabling attackers to identify vulnerabilities at unprecedented speed.
Security specialists increasingly describe the environment as a contest in which defensive systems must deploy AI to counter adversaries using similar technologies to exploit weaknesses in digital infrastructure.
Security concerns also extend beyond large institutions. Connected devices in homes, including smart cameras and speakers, often lack robust security protections, increasing exposure for individuals and networks.
Policymakers in Europe are responding through measures such as the Cyber Resilience Act, which will introduce mandatory security requirements for connected products sold in the EU.
Long-term risks are also emerging from advances in quantum computing.
Experts warn that powerful future machines could eventually break widely used encryption systems that currently protect communications, financial data and government networks, prompting organisations to adopt quantum-resistant security methods.
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Researchers at MIT have developed a hybrid AI framework designed to improve how robots plan and perform complex visual tasks. The approach combines generative AI with classical planning software, allowing machines to analyse images, simulate actions, and generate reliable plans to reach a goal.
The system relies on two specialised vision-language models. One model analyses an image, describes the environment, and simulates possible actions, while a second model converts those simulations into a formal programming language used for planning.
Generated files are then processed by established planning software to produce a step-by-step strategy.
Testing showed a significant improvement compared with existing techniques. The framework achieved an average success rate of about 70 percent, while many baseline methods reached roughly 30 percent.
Performance remained strong in unfamiliar scenarios, demonstrating the system’s ability to adapt to changing conditions.
The method could support applications such as robot navigation, autonomous driving, and multi-robot assembly systems. Continued development aims to handle more complex environments and reduce errors caused by AI model hallucinations.
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YouTube is expanding its likeness-detection technology designed to identify AI-generated deepfakes, extending access to a pilot group of government officials, political candidates, and journalists.
The tool allows participants to detect unauthorised AI-generated videos that simulate their faces and request removal if the content violates YouTube policies. The system builds on technology launched last year for around four million creators in the YouTube Partner Program.
Similar to YouTube’s Content ID system, which detects copyrighted material in uploaded videos, the likeness detection feature scans for AI-generated faces created with deepfake tools. Such technologies are increasingly used to spread misinformation or manipulate public perception by making prominent figures appear to say or do things they never did.
According to YouTube, the pilot programme aims to balance free expression with safeguards against AI impersonation, particularly in sensitive civic contexts.
‘This expansion is really about the integrity of the public conversation,’ said Leslie Miller, YouTube’s vice president of Government Affairs and Public Policy. ‘We know that the risks of AI impersonation are particularly high for those in the civic space. But while we are providing this new shield, we’re also being careful about how we use it.’
Removal requests will be assessed individually under YouTube’s privacy policy rules to determine whether the content constitutes parody or political critique, which remain protected forms of expression. Participants must verify their identity by uploading a selfie and a government-issued ID before accessing the tool. Once verified, they can review detected matches and submit removal requests for content they believe violates policy.
YouTube also said it supports the proposed NO FAKES Act in the United States, which aims to regulate the unauthorised use of an individual’s voice or visual likeness in AI-generated media. AI-generated videos on the platform are already labelled, though label placement varies depending on the topic’s sensitivity.
‘There’s a lot of content that’s produced with AI, but that distinction’s actually not material to the content itself,’ said Amjad Hanif, YouTube’s vice president of Creator Products. The company said it plans to expand the technology over time to detect AI-generated voices and other intellectual property.
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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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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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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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NEURA Robotics and Qualcomm have formed a long-term strategic collaboration to advance physical AI and next-generation robotics platforms.
A partnership that aims to bring intelligent robots into real-world environments more rapidly by combining advanced AI processors with full-stack robotic systems.
The cooperation focuses on developing ‘Brain + Nervous System’ reference architectures that integrate high-level cognition, such as perception, reasoning and planning, with ultra-low-latency control systems.
Qualcomm’s robotics processors, including the Dragonwing IQ10 Series, will provide AI compute and connectivity, while NEURA contributes robotic hardware platforms and embodied AI software.
Both companies intend to support deployment across multiple robotic forms, including robotic arms, mobile robots, service machines and humanoid platforms.
NEURA’s cloud environment, Neuraverse, will serve as a shared platform for simulation, training and lifecycle management of robotic intelligence, allowing innovations developed by one robot to spread across entire fleets.
The collaboration also aims to establish a global developer ecosystem for robotics applications. Standardised runtime environments and deployment interfaces are expected to simplify how AI workloads move from development into production while maintaining reliability and safety.
Executives from both companies emphasised that robotics represents one of the most demanding AI environments, as decisions must be made instantly and locally.
By combining edge AI processing with cognitive robotic systems, the partnership aims to accelerate commercial deployment of humanoid and general-purpose robots capable of operating safely alongside humans across industries.
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Ten pilot schools in Buriram and Si Sa Ket provinces have launched Smart Classrooms under the UNESCO–Huawei TEOSA initiative, supporting Thailand’s drive to expand digital education.
Led by UNESCO Bangkok in partnership with Thailand’s Ministry of Education and Huawei Technologies Co., Ltd, the Smart Classrooms initiative aims to strengthen digital learning environments, equip teachers with digital and AI competencies, and support policy development for AI in education. The programme also supports Thailand’s ‘Transforming Education in the Digital Era’ policy and the National AI Strategy and Action Plan (2022–2027).
Each province has one designated ‘mother school’ that serves as a regional digital hub, supporting four surrounding ‘child schools’ by sharing resources, training, and expertise. The ten pilot schools in total have received high-speed internet, interactive digital displays, and collaborative learning platforms that support real-time content sharing and blended learning. Forty-five teachers from the pilot schools also participated in hands-on demonstrations of Smart Classrooms systems on 4–5 March.
‘This new technology will help translate theory into practice, allowing students to experiment, test strategies, and see results immediately,’ said Pathanapong Momprakhon, Principal of Paisan Pittayakom School. UNESCO Bangkok’s Deputy Director and Chief of Education, Marina Patrier, highlighted the importance of combining infrastructure with teacher capacity-building.
‘At UNESCO, we are committed to promoting the ethical and inclusive use of AI in ways that empower teachers and expand opportunities for every learner,’ Ms Patrier said at the launch. ‘While Smart Classrooms provide important tools, it is teachers’ creativity, professional judgement and leadership that ultimately bring these innovations to life.’
Chitralada Chanyaem of the Thai National Commission for UNESCO highlighted the importance of collaboration in advancing digital education.
‘The UNESCO–Huawei Funds-in-Trust Project on Technology-Enabled Open Schools for All stands as a powerful example of collaboration dedicated to transforming education into a system that is open, inclusive, flexible, and resilient in the face of a rapidly changing world, she said. ‘As the future of education cannot be confined within classroom walls, it must bridge sectors and communities, working collaboratively to create equitable and sustainable opportunities for all.’
Teachers observed Huawei technical staff and master teachers demonstrate how digital tools and AI-supported applications can be used in everyday lessons. Ms Piyaporn Kidsirianan, Public Relations Manager at Huawei Technologies (Thailand) Co., Ltd, said the initiative aims to reduce digital inequality.
‘The Open Schools for All initiative represents a commitment to using technology as a bridge to deliver quality education to remote and underserved communities.’ The TEOSA Smart Classrooms initiative combines policy support, digital infrastructure upgrades, and teacher training to help translate Thailand’s digital education ambitions into practical impact at the school level.
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Samsung has confirmed key details about its upcoming AI smart glasses, including a camera positioned at ‘eye level’ and smartphone connectivity, ahead of a planned 2026 launch.
The device is being developed in partnership with Qualcomm and Google, building on the same ecosystem that produced the Galaxy XR headset, and will be powered by Google’s Gemini AI.
Samsung executive Jay Kim indicated that the glasses will be able to understand ‘where you’re looking at’, allowing the AI to analyse objects or scenes in the user’s field of view and provide contextual information in real time.
Processing is expected to take place on a connected smartphone rather than within the glasses themselves, and Samsung has not confirmed whether a built-in display will be included, suggesting multiple versions may be in development.
The announcement puts Samsung on a direct collision course with Meta, whose Ray-Ban Meta Gen 2 glasses are already on the market, offering 3K video recording and up to eight hours of battery life. Meta has also launched the Oakley Meta HSTN glasses, aimed at sports and outdoor users.
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