Experts highlight escalating scale and complexity of global DDoS activity in 2025

Netscout has released new research examining the current state of distributed denial-of-service (DDoS) attacks, noting both their growing volume and increasing technical sophistication.

The company recorded more than eight million DDoS attacks worldwide in the first half of 2025, including over 3.2 million in the EMEA region. Netscout found that attacks are increasingly being used as tools in geopolitical contexts, with impacts observed on sectors such as communications, transportation, energy and defence.

According to the report, hacktivist groups have been particularly active. For example, NoName057(16) claimed responsibility for more than 475 incidents in March 2025—over three times the number of the next most active group—focusing on government websites in Spain, Taiwan and Ukraine. Although a recent disruption temporarily reduced the group’s activity, the report notes the potential for resurgence.

Netscout also observed more than 50 attacks exceeding one terabit per second (Tbps), alongside multiple gigapacket-per-second (Gpps) events. Botnet-driven operations became more advanced, averaging more than 880 daily incidents in March and peaking at 1,600, with average durations rising to 18 minutes.

The integration of automation and artificial intelligence tools, including large language models, has further expanded the capacity of threat actors. Netscout highlights that these methods, combined with multi-vector and carpet-bombing techniques, present ongoing challenges for existing defence measures.

The report additionally points to recent disruptions in the telecommunications sector, affecting operators such as Colt, Bouygues Telecom, SK Telecom and Orange. Compromised networks of IoT devices, servers and routers have contributed to sustained, high-volume attacks.

Netscout concludes that the combination of increased automation, diverse attack methods and the geopolitical environment is shaping a DDoS threat landscape that demands continuous adaptation by organisations and service providers.

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AI redefines how cybersecurity teams detect and respond

AI, especially generative models, has become a staple in cybersecurity operations, extending its role from traditional machine learning tools to core functions within CyberOps.

Generative AI now supports forensics, incident investigation, log parsing, orchestration, vulnerability prioritisation and report writing. It accelerates workflows, enabling teams to ramp up detection and response and to concentrate human efforts on strategic tasks.

Experts highlight that it is not what CyberOps do that AI is remastering, but how they do it. AI scales routine tasks, like SOC level-1 and -2 operations, allowing analysts to shift focus from triage to investigation and threat modelling.

Junior staff benefit particularly from AI, which boosts accuracy and consistency. Senior analysts and CISOs also gain from AI’s capacity to amplify productivity while safeguarding oversight, a true force multiplier.

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Google alerts users after detecting malware spread through captive portals

Warnings have been issued by Google to some users after detecting a web traffic hijacking campaign that delivered malware through manipulated login portals.

According to the company’s Threat Intelligence Group, attackers compromised network edge devices to modify captive portals, the login pages often seen when joining public Wi-Fi or corporate networks.

Instead of leading to legitimate security updates, the altered portals redirected users to a fake page presenting an ‘Adobe Plugin’ update. The file, once installed, deployed malware known as CANONSTAGER, which enabled the installation of a backdoor called SOGU.SEC.

The software, named AdobePlugins.exe, was signed with a valid GlobalSign certificate linked to Chengdu Nuoxin Times Technology Co, Ltd. Google stated it is tracking multiple malware samples connected to the same certificate.

The company attributed the campaign to a group it tracks as UNC6384, also known by other names including Mustang Panda, Silk Typhoon, and TEMP.Hex.

Google said it first detected the campaign in March 2025 and sent alerts to affected Gmail and Workspace users. The operation reportedly targeted diplomats in Southeast Asia and other entities worldwide, suggesting a potential link to cyber espionage activities.

Google advised users to enable Enhanced Safe Browsing in Chrome, keep devices updated, and use two-step verification for stronger protection.

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Google Cloud develops blockchain network for financial institutions

Google Cloud is creating its own blockchain platform, the Google Cloud Universal Ledger (GCUL), targeting the financial sector. The network provides a neutral, compliant infrastructure for payment automation and digital asset management through a single API.

GCUL allows financial institutions to build Python-based smart contracts, with support for various use cases such as wholesale payments and asset tokenisation. Although called a Layer 1 network, its private, permissioned design raises debate over its status as a decentralised blockchain.

The company also revealed a series of AI-driven security enhancements at its Security Summit 2025.

These include an ‘agentic security operations centre’ for proactive threat detection, the Alert Investigation agent for automated analysis, and Model Armour to prevent prompt injection, jailbreaking, and data leaks.

Currently in a private testnet, GCUL was first announced in March in collaboration with the CME Group, which is piloting solutions on the platform. Google Cloud plans to reveal more details in the future as the project develops.

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AI firms under scrutiny for exposing children to harmful content

The National Association of Attorneys General has called on 13 AI firms, including OpenAI and Meta, to strengthen child protection measures. Authorities warned that AI chatbots have been exposing minors to sexually suggestive material, raising urgent safety concerns.

Growing use of AI tools among children has amplified worries. In the US, surveys show that over three-quarters of teenagers regularly interact with AI companions. The UK data indicates that half of online 8-15-year-olds have used generative AI in the past year.

Parents, schools, and children’s rights organisations are increasingly alarmed by potential risks such as grooming, bullying, and privacy breaches.

Meta faced scrutiny after leaked documents revealed its AI Assistants engaged in ‘flirty’ interactions with children, some as young as eight. The NAAG described the revelations as shocking and warned that other AI firms could pose similar threats.

Lawsuits against Google and Character.ai underscore the potential real-world consequences of sexualised AI interactions.

Officials insist that companies cannot justify policies that normalise sexualised behaviour with minors. Tennessee Attorney General Jonathan Skrmetti warned that such practices are a ‘plague’ and urged innovation to avoid harming children.

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Tencent Cloud sites exposed credentials and source code in major security lapse

Researchers have uncovered severe misconfigurations in two Tencent Cloud sites that exposed sensitive credentials and internal source code to the public. The flaws could have given attackers access to Tencent’s backend infrastructure and critical internal services.

Cybernews discovered the data leaks in July 2025, finding hardcoded plain-text passwords, a sensitive internal .git directory, and configuration files linked to Tencent’s load balancer and JEECG development platform.

Weak passwords, built from predictable patterns like the company name and year, increased the risk of exploitation.

The exposed data may have been accessible since April, leaving months of opportunity for scraping bots or malicious actors.

With administrative console access, attackers could have tampered with APIs, planted malicious code, pivoted deeper into Tencent’s systems, or abused the trusted domain for phishing campaigns.

Tencent confirmed the incident as a ‘known issue’ and has since closed access, though questions remain over how many parties may have already retrieved the exposed information.

Security experts warn that even minor oversights in cloud operations can cascade into serious vulnerabilities, especially for platforms trusted by millions worldwide.

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ChatGPT faces scrutiny as OpenAI updates protections after teen suicide case

OpenAI has announced new safety measures for its popular chatbot following a lawsuit filed by the parents of a 16-year-old boy who died by suicide after relying on ChatGPT for guidance.

The parents allege the chatbot isolated their son and contributed to his death earlier in the year.

The company said it will improve ChatGPT’s ability to detect signs of mental distress, including indirect expressions such as users mentioning sleep deprivation or feelings of invincibility.

It will also strengthen safeguards around suicide-related conversations, which OpenAI admitted can break down in prolonged chats. Planned updates include parental controls, access to usage details, and clickable links to local emergency services.

OpenAI stressed that its safeguards work best during short interactions, acknowledging weaknesses in longer exchanges. It also said it is considering building a network of licensed professionals that users could access through ChatGPT.

The company added that content filtering errors, where serious risks are underestimated, will also be addressed.

The lawsuit comes amid wider scrutiny of AI tools by regulators and mental health experts. Attorneys general from more than 40 US states recently warned AI companies of their duty to protect children from harmful or inappropriate chatbot interactions.

Critics argue that reliance on chatbots for support instead of professional care poses growing risks as usage expands globally.

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Green AI and the battle between progress and sustainability

AI is increasingly recognised for its transformative potential and growing environmental footprint across industries. The development and deployment of large-scale AI models require vast computational resources, significant amounts of electricity, and extensive cooling infrastructure.

For instance, studies have shown that training a single large language model can consume as much electricity as several hundred households use in a year, while data centres operated by companies like Google and Microsoft require millions of litres of water annually to keep servers cool.

That has sparked an emerging debate around what is now often called ‘Green AI’, the effort to balance technological progress with sustainability concerns. On one side, critics warn that the rapid expansion of AI comes at a steep ecological cost, from high carbon emissions to intensive water and energy consumption.

On the other hand, proponents argue that AI can be a powerful tool for achieving sustainability goals, helping optimise energy use, supporting climate research, and enabling greener industrial practices. The tension between sustainability and progress is becoming central to discussions on digital policy, raising key questions.

Should governments and companies prioritise environmental responsibility, even if it slows down innovation? Or should innovation come first, with sustainability challenges addressed through technological solutions as they emerge?

Sustainability challenges

In the following paragraphs, we present the main sustainability challenges associated with the rapid expansion of AI technologies.

Energy consumption

The training of large-scale AI models requires massive computational power. Estimates suggest that developing state-of-the-art language models can demand thousands of GPUs running continuously for weeks or even months.

According to a 2019 study from the University of Massachusetts Amherst, training a single natural language processing model consumed roughly 284 tons of CO₂, equivalent to the lifetime emissions of five cars. As AI systems grow larger, their energy appetite only increases, raising concerns about the long-term sustainability of this trajectory.

Carbon emissions

Carbon emissions are closely tied to energy use. Unless powered by renewable sources, data centres rely heavily on electricity grids dominated by fossil fuels. Research indicates that the carbon footprint of training advanced models like GPT-3 and beyond is several orders of magnitude higher than that of earlier generations. That research highlights the environmental trade-offs of pursuing ever more powerful AI systems in a world struggling to meet climate targets.

Water usage and cooling needs

Beyond electricity, AI infrastructure consumes vast amounts of water for cooling. For example, Google reported that in 2021 its data centre in The Dalles, Oregon, used over 1.2 billion litres of water to keep servers cool. Similarly, Microsoft faced criticism in Arizona for operating data centres in drought-prone areas while local communities dealt with water restrictions. Such cases highlight the growing tension between AI infrastructure needs and local environmental realities.

Resource extraction and hardware demands

The production of AI hardware also has ecological costs. High-performance chips and GPUs depend on rare earth minerals and other raw materials, the extraction of which often involves environmentally damaging mining practices. That adds a hidden, but significant footprint to AI development, extending beyond data centres to global supply chains.

Inequality in resource distribution

Finally, the environmental footprint of AI amplifies global inequalities. Wealthier countries and major corporations can afford the infrastructure and energy needed to sustain AI research, while developing countries face barriers to entry.

At the same time, the environmental consequences, whether in the form of emissions or resource shortages, are shared globally. That creates a digital divide where the benefits of AI are unevenly distributed, while the costs are widely externalised.

Progress & solutions

While AI consumes vast amounts of energy, it is also being deployed to reduce energy use in other domains. Google’s DeepMind, for example, developed an AI system that optimised cooling in its data centres, cutting energy consumption for cooling by up to 40%. Similarly, IBM has used AI to optimise building energy management, reducing operational costs and emissions. These cases show how the same technology that drives consumption can also be leveraged to reduce it.

AI has also become crucial in climate modelling, weather prediction, and renewable energy management. For example, Microsoft’s AI for Earth program supports projects worldwide that use AI to address biodiversity loss, climate resilience, and water scarcity.

Artificial intelligence also plays a role in integrating renewable energy into smart grids, such as in Denmark, where AI systems balance fluctuations in wind power supply with real-time demand.

There is growing momentum toward making AI itself more sustainable. OpenAI and other research groups have increasingly focused on techniques like model distillation (compressing large models into smaller versions) and low-rank adaptation (LoRA) methods, which allow for fine-tuning large models without retraining the entire system.

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Meanwhile, startups like Hugging Face promote open-source, lightweight models (like DistilBERT) that drastically cut training and inference costs while remaining highly effective.

Hardware manufacturers are also moving toward greener solutions. NVIDIA and Intel are working on chips with lower energy requirements per computation. On the infrastructure side, major providers are pledging ambitious climate goals.

Microsoft has committed to becoming carbon negative by 2030, while Google aims to operate on 24/7 carbon-free energy by 2030. Amazon Web Services is also investing heavily in renewable-powered data centres to offset the footprint of its rapidly growing cloud services.

Governments and international organisations are beginning to address the sustainability dimension of AI. The European Union’s AI Act introduces transparency and reporting requirements that could extend to environmental considerations in the future.

In addition, initiatives such as the OECD’s AI Principles highlight sustainability as a core value for responsible AI. Beyond regulation, some governments fund research into ‘green AI’ practices, including Canada’s support for climate-oriented AI startups and the European Commission’s Horizon Europe program, which allocates resources to environmentally conscious AI projects.

Balancing the two sides

The debate around Green AI ultimately comes down to finding the right balance between environmental responsibility and technological progress. On one side, the race to build ever larger and more powerful models has accelerated innovation, driving breakthroughs in natural language processing, robotics, and healthcare. In contrast, the ‘bigger is better’ approach comes with significant sustainability costs that are increasingly difficult to ignore.

Some argue that scaling up is essential for global competitiveness. If one region imposes strict environmental constraints on AI development, while another prioritises innovation at any cost, the former risks falling behind in technological leadership. The following dilemma raises a geopolitical question that sustainability standards may be desirable, but they must also account for the competitive dynamics of global AI development.

Malaysia aims to lead Asia’s clean tech revolution through rare earth processing and circular economy efforts.

At the same time, advocates of smaller and more efficient models suggest that technological progress does not necessarily require exponential growth in size and energy demand. Innovations in model efficiency, greener hardware, and renewable-powered infrastructure demonstrate that sustainability and progress are not mutually exclusive.

Instead, they can be pursued in tandem if the right incentives, investments, and policies are in place. That type of development leaves governments, companies, and researchers facing a complex but urgent question. Should the future of AI prioritise scale and speed, or should it embrace efficiency and sustainability as guiding principles?

Conclusion

The discussion on Green AI highlights one of the central dilemmas of our digital age. How to pursue technological progress without undermining environmental sustainability. On the one hand, the growth of large-scale AI systems brings undeniable costs in terms of energy, water, and resource consumption. At the same time, the very same technology holds the potential to accelerate solutions to global challenges, from optimising renewable energy to advancing climate research.

Rather than framing sustainability and innovation as opposing forces, the debate increasingly suggests the need for integration. Policies, corporate strategies, and research initiatives will play a decisive role in shaping this balance. Whether through regulations that encourage transparency, investments in renewable infrastructure, or innovations in model efficiency, the path forward will depend on aligning technological ambition with ecological responsibility.

In the end, the future of AI may not rest on choosing between sustainability and progress, but on finding ways to ensure that progress itself becomes sustainable.

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Cyberattack disrupts Nevada government systems

The State of Nevada reported a cyberattack affecting several state government systems, with recovery efforts underway. Some websites and phone lines may be slow or offline while officials restore operations.

Governor Joe Lombardo’s office stated there is no evidence that personal information has been compromised, emphasising that the issue is limited to state systems. The incident is under investigation by both state and federal authorities, although technical details have not been released.

Several agencies, including the Department of Motor Vehicles, have been affected, prompting temporary office closures until normal operations can resume. Emergency services, including 911, continue to operate without disruption.

Officials prioritise system validation and safe restoration to prevent further disruption to state services.

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Greece strengthens crypto rules to align with EU standards

Greek authorities are enforcing stricter regulations on the crypto sector to strengthen oversight and align with European standards. The move targets money laundering and tax evasion, reflecting Athens’ intent to bring order to the industry.

Digital asset exchanges and wallet providers will face a rigorous licensing process. Applicants must submit a complete business dossier, disclose management and shareholder details, and pass extensive checks before being allowed to operate.

Non-compliant platforms risk being barred from the market.

Financial regulators will monitor crypto transactions closely, with powers to freeze suspicious digital assets and trace funds. Authorities aim to prevent illegal capital flows while boosting investor confidence through enhanced transparency.

Taxation rules for crypto are expected this fall, with capital gains taxes set at 15% for private investors and potentially higher for companies. Some crypto services may also be subject to 24% VAT, with final rates announced in the coming months.

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