Cybersecurity is set to receive the largest budget increases over the next 12 months, as organisations respond to rising geopolitical tensions and a surge in high-profile cyber-attacks, according to the KPMG Global Tech Report 2026.
More than half of UK firms plan to lift cybersecurity spending by over 10 percent, outpacing global averages and reflecting heightened concern over digital resilience.
AI and data analytics are also attracting substantial investment, with most organisations increasing budgets as they anticipate stronger returns by the end of 2026. Executives expect AI to shift from an efficiency tool to a core revenue driver, signalling a move toward large-scale deployment.
Despite strong investment momentum, scaling remains a major challenge. Fewer than one in 10 organisations report fully deployed AI or cybersecurity systems today, although around half expect to reach that stage within a year.
Structural barriers, fragmented ownership, and unclear accountability continue to slow execution, highlighting the complexity of translating strategy into operational impact.
Agentic AI is emerging as a central focus, with most organisations already embedding autonomous systems into workflows. Demand for specialist AI roles is rising, alongside closer collaboration to ensure secure deployment, governance, and continuous monitoring.
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According to a January 2026 survey of 2,520 US adults aged 18 to 29, roughly 20 percent of Gen Z have queried AI chatbots about STIs/STDs, and 1 in 10 specifically sought help with diagnosis or suspicion of infection.
Among those who later sought formal medical testing, about 31 percent said the chatbot’s assessment was incorrect, highlighting risks of relying on AI for health diagnostics.
Respondents often shared symptom details and even photos with the bots, and many said they were more comfortable discussing sensitive topics with an AI than with a clinician, despite potential privacy and accuracy limitations.
Medical experts emphasise that while AI can support general health education, these tools are not replacements for clinical diagnosis or professional medical testing, which remain necessary for accurate STI/STD identification and treatment.
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Aquila has completed a €5 million investment in AI-driven warehouse automation at its Ilfov, Dragomiresti logistics centre. The project is a strategic response to increasing portfolio complexity and growing distribution volumes in the FMCG sector.
The automation solution is built around AI-based vision systems that identify products directly from images using shape, colour and visual characteristics. The technology removes the need for labels or manual scanning, even when packaging orientation or appearance shows minor variations.
According to the company, the system improves the speed and accuracy of warehouse operations while reducing manual work and optimising storage space. These efficiency gains allow better use of operational resources.
The investment enables Aquila to scale logistics operations without proportional increases in resources. The company reports improved internal efficiency, stronger service quality for customers and the creation of medium-term competitive advantages.
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Companies are reporting net job losses linked to AI adoption, with research showing a sharper impact than in other major economies. A Morgan Stanley survey found that firms using the technology for at least a year cut more roles than they created, particularly across the UK labour market.
The study covered sectors including retail, real estate, transport, healthcare equipment and automotive manufacturing, showing an average productivity increase of 11.5% among UK businesses. Comparable firms in the United States reported similar efficiency gains but continued to expand employment overall.
Researchers pointed to higher operating costs and tax pressures as factors amplifying the employment impact in Britain. Unemployment has reached a four-year high, while increases in the minimum wage and employer national insurance contributions have tightened hiring across industries.
Public concern over AI-driven displacement is also rising, with more than a quarter of UK workers fearing their roles could disappear within five years, according to recruitment firm Randstad. Younger workers expressed the highest anxiety, while older generations showed greater confidence in adapting.
Political leaders warn that unmanaged AI-driven change could disrupt labour markets. London mayor Sadiq Khan said the technology may cut many white-collar jobs, calling for action to create replacement roles.
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Evidence from threat intelligence reporting and incident analysis in 2025 suggests that AI will move from experimental use to routine deployment in malicious cyber operations in 2026. Rather than introducing entirely new threats, AI is expected to accelerate existing attack techniques, reduce operational costs for attackers, and increase the scale and persistence of campaigns.
AI-enabled malware is expected to adapt during execution. Threat intelligence reporting indicates that malware using AI models is already capable of modifying behaviour in real time. In 2026, such capabilities are expected to become more common, allowing malicious code to adjust tactics in response to defensive measures.
AI agents are likely to automate key stages of cyberattacks. Researchers expect wider use of agentic AI systems that can independently conduct reconnaissance, exploit vulnerabilities, and maintain persistence, reducing the need for continuous human control.
Prompt injection will be treated as a practical attack technique against AI deployments. As organisations embed AI assistants and agents into workflows, attackers are expected to target the AI layer itself (e.g. through prompt injection, unsafe tool use, and weak guardrails) to trigger unintended actions or expose data.
Threat actors will use AI to target humans at scale. The text emphasises AI-enhanced social engineering: conversational bots, real-time manipulation, and automated account takeover, shifting attacks from isolated human-led attempts to continuous, scalable interaction.
AI will expose APIs as a too-easily-exploited attack surface. The experts argue that AI agents capable of discovering and interacting with software interfaces will lower the barrier to abusing APIs, including undocumented or unintended ones. As agents gain broader permissions and access to cloud services, APIs are expected to become a more frequent point of exploitation and concealment.
Extortion will evolve beyond ransomware encryption. Extortion campaigns are expected to rely less on encryption alone and more on a combination of tactics, including data theft, threats to leak or alter information, and disruption of cloud services, backups, and supply chains.
Cyber incidents will increasingly spread from IT into industrial operations. Ransomware and related intrusions are expected to move beyond enterprise IT systems and disrupt operational technology and industrial control environments, amplifying downtime, supply-chain disruption, and operational impact.
The insider threat will increasingly include imposter employees. Analysts anticipate insider risks will extend beyond malicious or negligent staff to include external actors who gain physical or remote access by posing as legitimate employees, including through hardware implants or direct device access that bypasses end point security.
Nation-state cyber activity will continue to target Western governments and industries. Experts point to continued cyber operations by state-linked actors, including financially motivated campaigns and influence operations, with increased use of social engineering, deception techniques, and AI-enabled tools to scale and refine targeting.
Identity management is expected to remain a primary failure point. The rapid growth of human and machine identities, including AI agents, across SaaS, cloud platforms and third-party environments is likely to reinforce credential misuse as a leading cause of major breaches.
Taken together, these trends suggest that in 2026, cyber risk will increasingly reflect systemic exposure created by the combination of AI adoption, identity sprawl, and interconnected digital infrastructure, rather than isolated technical failures.
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France has unveiled a new push to reduce Europe’s dependence on US and Chinese technology suppliers, placing digital sovereignty back at the centre of the EU policy debates.
Speaking in Paris, France’s minister for AI and digital affairs, Anne Le Hénanff, presented initiatives to expose and address the structural reliance on non-EU technologies across public administrations and private companies.
Central to the strategy is the creation of a Digital Sovereignty Observatory, which will map foreign technology dependencies and assess organisational exposure to geopolitical and supply-chain risks.
The body, led by former Europe minister Clément Beaune, is intended to provide the evidence base needed for coordinated action rather than symbolic declarations of autonomy.
France is also advancing a Digital Resilience Index, expected to publish its first findings in early 2026. The index will measure reliance on foreign digital services and products, identifying vulnerabilities linked to cloud infrastructure, AI, cybersecurity and emerging technologies.
Industry data suggests Europe’s dependence on external tech providers costs the continent hundreds of billions of euros annually.
Paris is using the initiative to renew calls for a European preference in public-sector digital procurement and for a standard EU definition of European digital services.
Such proposals remain contentious among member states, yet France argues they are essential for restoring strategic control over critical digital infrastructure.
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Microsoft has unveiled Maia 200, a next-generation AI inference accelerator built to boost performance, efficiency, and cost-effectiveness at scale. Built on TSMC’s 3-nanometre process, the chip boosts speed, efficiency, and memory throughput for advanced AI models.
The new accelerator will power Microsoft’s cloud infrastructure across Azure, Microsoft Foundry, and Microsoft 365 Copilot, including workloads for OpenAI’s latest GPT-5.2 models.
Internal teams will use Maia 200 for synthetic data generation and reinforcement learning, accelerating AI development. Maia 200 is being rolled out in Microsoft’s US Central data centre region, with further deployments planned across additional global locations.
A preview version of the Maia software development kit is also being released, offering developers access to PyTorch integration, optimised compilers, and low-level programming tools to fine-tune AI models across heterogeneous computing environments.
The system introduces a redesigned networking and memory architecture optimised for high-bandwidth data movement and large-scale inference clusters.
Microsoft says the platform delivers significant improvements in performance per dollar, scalability, and power efficiency, positioning Maia 200 as a cornerstone of its long-term AI infrastructure strategy.
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Apple is preparing a major AI upgrade for Siri powered by Google’s Gemini models, expected in the second half of February, according to Bloomberg. The update will run on Apple’s Private Cloud Compute infrastructure using high-end Mac chips.
The iOS 26.4 release is set to introduce ‘World Knowledge Answers’, enabling Siri to provide web-based summaries with citations similar to ChatGPT and Perplexity. Deeper integration across core apps such as Mail, Photos, Music, TV, and Xcode is also planned.
Expanded voice controls are expected to let users search for and edit photos by spoken description, as well as generate emails based on calendar activity. Bloomberg also reported Apple is paying Google around $1 billion annually to access Gemini’s underlying AI technology.
Market reaction to the news pushed Apple shares higher, while Alphabet stock also rose following confirmation of the partnership. A spokesperson for Apple declined to comment on the reported developments.
Looking ahead, Apple is developing a chatbot-style assistant known internally as ‘Campos’ to eventually replace the current Siri interface. The system would analyse on-screen activity, suggest actions, and expand device control across future operating systems.
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Google’s health-related search results increasingly draw on YouTube rather than hospitals, government agencies, or academic institutions, as new research reveals how AI Overviews select citation sources in automated results.
An analysis by SEO platform SE Ranking reviewed more than 50,000 German-language health queries and found AI Overviews appeared on over 82% of searches, making healthcare one of the most AI-influenced information categories on Google.
Across all cited sources, YouTube ranked first by a wide margin, accounting for more than 20,000 references and surpassing medical publishers, hospital websites, and public health authorities.
Academic journals and research institutions accounted for less than 1% of citations, while national and international government health bodies accounted for under 0.5%, highlighting a sharp imbalance in source authority.
Researchers warn that when platform-scale content outweighs evidence-based medical sources, the risk extends beyond misinformation to long-term erosion of trust in AI-powered search systems.
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The organisation Google.org has selected twelve recipients for its $20 million AI for Science fund, which aims to accelerate research in health, agriculture, biodiversity, and climate.
The initiative backs academic, nonprofit, and startup teams using AI to turn scientific insights into real-world solutions. In health and life sciences, projects target genetic decoding, neural mapping, disease prediction, and faster detection of drug resistance.
Research groups are applying advanced AI models to unlock hidden regions of the human genome, simulate disease pathways, and dramatically reduce detection times for life-threatening pathogens, shifting medicine towards earlier intervention and prevention.
Agriculture and food systems are another focus, using AI to breed resistant crops, improve nutrition, and cut livestock methane emissions. Projects seek to strengthen food security, boost sustainability, and support climate resilience.
Biodiversity and clean energy efforts target species mapping, conservation planning, fusion research, and large-scale carbon capture. Open science principles ensure datasets and tools remain accessible, scalable, and capable of driving wider breakthroughs.
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