TECH

A measure adopted in the United States highlights the impact of AI on the power grid

The expansion of artificial intelligence is often associated with technological advances, innovation, and increased productivity. However, behind the most sophisticated models lies an infrastructure that consumes vast amounts of energy. This reality became apparent during a recent heatwave in the United States, when authorities had to implement an emergency measure to prevent grid overloads, reigniting the debate over the environmental impacts of the new AI era.

Extreme temperatures across the eastern United States placed the country's largest power grid under severe strain. With millions of households running air conditioners simultaneously, electricity consumption neared historic highs.

Faced with this situation, the U.S. Department of Energy authorized an exceptional measure to maintain system stability.

The authorization allowed PJM Interconnection—the grid operator serving approximately 67 million people across parts of the Midwest and the U.S. East Coast—to tap into emergency generators owned by major energy consumers, such as data centers and industrial facilities.

These units typically remain idle, used only in the event of a power outage.

This time, however, they served as an additional power source to relieve the load on the main grid.

The authorization took effect as a temporary measure during the period of highest overload risk.

According to the operator, demand reached approximately 162,700 megawatts, very close to the region's all-time record.

Experts point out that the rapid growth of data centers is playing an increasingly significant role in this rise in energy consumption.

The expansion of artificial intelligence requires massive computing infrastructure capable of continuous operation, making these facilities some of the country's largest electricity consumers.

Data centers have become an essential—and problematic—part of the electrical infrastructure... At first glance, using the data centers' own generators seems like a highly logical solution. If these facilities can generate their own power during a supply outage, utilizing that capacity during critical moments helps relieve pressure on the public grid.

However, there is a significant aspect to this strategy.

Many of these emergency systems run on diesel or natural gas engines.

While highly effective at ensuring operational continuity, they also significantly increase pollutant emissions—precisely on days when air quality is often already compromised by intense heat.

In states like Virginia—considered a global hub for data centers—thousands of these generators are located near urban areas.

Recent studies indicate that the frequent operation of this equipment can increase emissions of nitrogen oxides, fine particulate matter, and other pollutants linked to the worsening of respiratory and cardiovascular diseases.

Researchers also link these emissions to a rise in asthma attacks and other health issues among populations living near these facilities.

Thus, the solution devised to prevent blackouts ends up creating another significant environmental challenge.

The growth of artificial intelligence also necessitates a new discussion about energy.

This situation highlights a transformation that often goes unnoticed.

Data centers have evolved beyond mere technical facilities for storing internet data.

Today, they form the physical foundation supporting cloud computing services, digital platforms, and—above all—large-scale artificial intelligence models.

As new AI applications emerge across virtually every economic sector, the energy required to power this infrastructure grows as well.

At the same time, factors such as more intense heatwaves, aging electrical grids, and increased electrification place further strain on distribution systems.

US authorities emphasize that the authorization granted was temporary and necessary to prevent supply interruptions during a critical period. Indeed, a major blackout amidst extreme temperatures could put hospitals, homes, public services, and millions of people at risk.

Several recent, aggressive measures highlight the U.S. power grid's vulnerability to surging AI and data center energy demands. The most immediate step was an emergency directive issued by the U.S. Department of Energy (DOE) authorizing grid operators to force large-scale AI data centers to disconnect from utility lines and run on backup power during brutal summer heatwaves.

The rapid integration of generative AI training models and commercial computing warehouses is driving residential and commercial power usage to record highs. Because of the severity of this crisis, several major, permanent, and long-term regulatory actions are currently rolling out:

Emergency backup order: During record-breaking heatwaves in the Eastern U.S., the DOE ordered dense clusters of AI data centers (particularly in Northern Virginia's "Data Center Alley") to switch to backup diesel or natural gas generators. This emergency measure successfully freed up critical megawatts to keep household air conditioners running.

FERC interconnection overhaul: The Federal Energy Regulatory Commission (FERC) issued sweeping orders forcing six regional grid operators to revamp their connection rules. This FERC Action provides a fast-track "priority lane" so energy-hungry data centers can plug into the electric grid more quickly, while establishing clear standards and timelines for transmission costs.

DOE power capacity commitment: The DOE previously committed $3 billion specifically for AI-centric smart grid programs to modernize grid operations and enable predictive analytics to better balance supply and demand.

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TECH

Secrets of the brain: how computers are helping scientists unravel the organ's mysteries

For decades, neuroscientists have sought to understand how neurons generate thoughts, memories, and emotions. Recent advances in computer simulations, artificial intelligence, and mathematical models have revolutionized this field of knowledge. Known as computational neuroscience, this discipline is expanding our theoretical understanding of how the brain processes information.

It studies everything from the electrical and chemical mechanisms that make a single neuron function to complex networks of millions of neurons working in unison. It also links biological processes to cognitive functions, such as learning and decision-making. This enables the creation of digital simulations of brain regions and the testing of hypotheses that could not be investigated directly in a laboratory setting.

Consequently, computational neuroscience has paved the way for new technologies capable of, for instance, predicting epileptic seizures and restoring movement in paralyzed individuals. Furthermore, it inspires new breakthroughs in computing, artificial intelligence, robotics, and intelligent systems.

A complex system...The human brain is composed of billions of neurons interconnected by trillions of synapses. Each neuron receives signals (both electrical and chemical) from hundreds of others simultaneously. When these signals accumulate and cross a certain threshold, the neuron fires an electrical impulse to transmit its message. This communication occurs at the synapses: the sending neuron releases neurotransmitters that fit into receptors like a key in a lock.

One of the most fascinating characteristics of this system is its ability to reorganize itself. Frequently used connections strengthen, while inactive ones weaken. This mechanism—known as synaptic plasticity—is what allows us to learn and form memories. Although individual neurons perform relatively simple tasks, their collective interaction gives rise to sophisticated behaviors, such as recognizing faces, experiencing fear, and planning a trip. Simulated brains... To study all this complexity, computational neuroscientists create digital simulators that mimic the behavior of neurons and neural networks, supported by new software architectures. Their starting point was the groundbreaking work of British physiologists Alan Hodgkin and Andrew Huxley; in 1952, they mathematically described how a neuron fires an electrical signal—work that earned them a Nobel Prize and remains the foundation of the field to this day.

One of the most widely used digital neuron simulation models today is known as "integrate-and-fire." In this model, the neuron functions like a bucket being filled with drops of water. Each incoming signal adds a few drops. When the level exceeds a certain threshold, the bucket tips over and empties, firing its contents to other neurons. The process then begins anew. This model is computationally efficient, as it allows for the simulation of thousands of neurons simultaneously.

One of the most ambitious research initiatives in this field today is the Blue Brain Project in Switzerland, which seeks to digitally reconstruct sections of the rat cortex with immense biological detail. Even so, the result still represents only a tiny fraction of what a real brain does.

Simulating the 86 billion human neurons and their 100 trillion connections remains far beyond the capabilities of current technology. Consequently, scientists work with varying levels of abstraction, selecting the appropriate level of detail needed to answer each specific question that arises.

The inspiration for modern AI...The relationship between computational neuroscience and artificial intelligence is a two-way street. Back in the 1940s, neurophysiologist Warren McCulloch and cognitive scientist Walter Pitts created the first abstract model of an artificial neuron, directly inspired by the biological neuron.

Decades later, by stacking layers of these artificial units, scientists developed computational models of artificial neural networks. This is the foundation of deep learning, which drives technologies such as speech recognition, medical imaging, and language models.

The comparison also revealed significant differences: the brain learns from very few examples and consumes about 20 watts—less than a light bulb. In contrast, training a large AI model can consume as much energy as hundreds of households over the course of several days. Understanding how the brain achieves such efficiency remains one of the field's major questions. The answer could transform the way we build artificial intelligence.

Healthcare applications...In practice, this research is already changing lives. In experiments using brain-computer interfaces (i.e., with sensors implanted in the brain), completely paralyzed individuals have managed to control robotic arms simply by thinking about moving. Although the movements lack speed and precision, they demonstrate that regaining function is feasible, even years after a central nervous system injury.

Some computational models can already identify brain patterns signaling the onset of an epileptic seizure, providing minutes or even hours of advance warning. For those with drug-resistant epilepsy, such a warning can significantly improve their daily routine and autonomy.

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TECH

Russia uses "shadow fleet" to spy on military bases in Europe

Tensions between Russia and the West have reached a new, alarming level. A recent report by the International Institute for Strategic Studies (IISS) revealed that the Kremlin has been spying on Europe in a manner that is, to say the least, both ingenious and unsettling.

For months, Moscow utilized its controversial "shadow fleet"—commercial vessels and oil tankers with their tracking systems turned off—to launch and retrieve surveillance drones. This ghost fleet acted as a veritable invisible aircraft carrier, operating in international waters right under the noses of European authorities.

Most concerning of all is the demonstrated ineffectiveness of NATO's air defenses. Throughout this Russian campaign, not a single one of these devices was shot down or captured, exposing a strategic vulnerability that Europe must address urgently.

IISS experts conducted an in-depth analysis of this covert operation, which took place between August 2024 and February 2026. During this eighteen-month period, Russian drones violated the airspace of 12 NATO countries as well as Ireland, roaming freely over sensitive infrastructure. It is deeply unsettling to realize that European nations failed to mount an adequate response to this constant provocation.

Sites of critical importance were among the primary targets monitored by the drones. The devices flew over bases housing nuclear weaponry, such as RAF Lakenheath in the UK and the nuclear submarine base at Île Longue in France. Furthermore, the disruptions extended to civil aviation, forcing the temporary closure of airports in Copenhagen and other locations across the continent.

A tactic to test NATO's limits... As you might imagine, the choice of small, slow-moving drones was no accident on the part of Russian intelligence services (GRU). Flying at low altitudes, these low-cost assets easily confuse traditional radar systems, which often mistake them for birds or mere background noise.

Since European air defenses were designed primarily to intercept fast missiles and supersonic fighter jets, dealing with this threat has proven to be a real challenge. This meticulous campaign allowed Russia to gather crucial data and achieve several strategic objectives:

Mapping NATO air defense vulnerabilities in detail.

Monitoring European nuclear deterrence infrastructure and logistical movements.

Testing military response times in a genuine "reconnaissance-by-fire" scenario.

Assessing the supply routes supporting the war effort in Ukraine.

To counter this troublesome fleet, European navies have recently begun inspecting and detaining these vessels in international waters, causing a sharp drop in incursions. Nevertheless, the warning has been served, and Europe is well aware of the urgent need to modernize its anti-drone technologies to avoid being caught off guard again.

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TECH

Don't let AI shape humanity's future: UN chief

The United Nations chief called Monday for a global governance system to shape artificial intelligence for the good of humanity, warning against allowing the technology to "vibe-code" our future.

With AI advancing at "runaway speed," UN Secretary-General Antonio Guterres cautioned that "an experiment is being run on our own societies, without a plan and without consent."

"That is not sustainable," he insisted, speaking in Geneva at the opening of the first Global Dialogue on AI Governance.

The two-day event is bringing together more than 4,000 participants representing governments, tech companies, academia and civil society to launch an inclusive discussion on how best to harness a technology that is already rapidly transforming our world.

"The question is whether we will shape this transformation together, or let it shape us," Guterres told the gathering.

He warned that AI systems were "no longer tools awaiting instruction."

"They are writing code, acting online and making choices with less and less human oversight," he said.

"Our institutions were built to govern machines that follow commands. They are not ready for machines that decide."

Guterres voiced concern at how AI was obscuring what is true and false.

He also warned there was a growing tendency to leave important tasks to the technology and blindly trust the results.

So-called "vibe-coding," or using AI to tell a machine what you want instead of coding it yourself, "can do wonders," he acknowledged.

"But we cannot vibe-code the truth. We cannot vibe-code the future of humanity."

Major risks...Another risk flagged by Guterres was the concentration of power in a handful of AI companies and a handful of countries. Most countries "have had no say in decisions that will shape their futures," he warned.

Countries, he said, now faced a stark choice "between governing by design and drifting by default."

The UN chief highlighted the potential of AI technologies for everything from accelerating development to improving health care and providing broader access to education.

But he insisted developments needed to be guided by several key priorities, including safety and respect for human rights, to ensure that people everywhere reap the benefits.

He called for "common methods to evaluate and verify risks" and jointly agreed standards, particularly for ensuring the safety of children accessing AI systems.

"We do not let medicine reach a child until it is proven safe. We test every toy," Guterres pointed out.

He called for an AI Child Safety Pledge, requiring companies to prove that any system accessible to children is safe and has zero tolerance for sexual abuse.

The systems must also connect any child showing signs of distress to real human support, he said.

"No child should be a guinea pig for unregulated AI," he insisted.

'Killer robots'...Boosting AI capacity and access in developing countries was also key, he said, to ensure that the existing deep digital divide does not "harden into an AI divide."

Guterres said he would urge the U.N. General Assembly to create a Global Fund for AI, "to build skills, data and affordable computing power everywhere."

General Assembly President Annalena Baerbock said it was "crystal clear" that the fund would be established but did not provide specific amounts.

"In a world with billions and trillions, money is hardly the challenge," she told reporters, adding that "the main challenge is that it's spent for the benefit of all."

The UN chief, meanwhile, said that his biggest concern revolved around AI in military settings, and in particular so-called lethal autonomous weapons systems.

"Let us call them what they are: killer robots," he said.

"Machines selecting and engaging their target and taking a life, without human control and judgment.

"That is morally repugnant ... and it must be banned by international law."

Guterres stressed the urgency of putting AI guardrails in place.

"We may be the last generation able to set the terms on which humanity and machines coexist," he said.

"The door is still open. It will not stay open long."

No concrete decisions are expected from the Geneva event, but dialogue co-chair Egriselda Lopez told reporters it would create good "foundations" for the road forward, with a second dialogue scheduled in New York next year.

© 2026 AFP

TECH

Why gen Z has turned against technology and big tech

Hundreds of people gathered in New York between late June and early July to participate in "Summer of Ludd," a series of free events teaching city residents how to live with less technology. Held primarily in Tompkins Square Park in the East Village, the festival featured workshops, plays, and discussions on reducing reliance on mobile phones, social media, and artificial intelligence.

Photography, recording, and the use of mobile phones were prohibited during the activities, and none of the events were advertised online; instead, promotion relied on posters and flyers distributed throughout the neighborhood. This report comes from the American magazine *WIRED*.

The festival's name alludes to Luddism, a movement of artisans and weavers who, in the early 19th century, resisted the replacement of their labor by machines during England's Industrial Revolution. Today, the term "Luddite" has resurfaced to describe those who question technology's central role in daily life, even without rejecting it entirely.

Dissatisfaction among a generation raised online...The movement has attracted primarily young people from Generation Z—the first generation to grow up entirely in the digital age. A Pew Research Center survey released in 2025 shows that 48% of American teens interviewed in 2024 stated that social media has a negative impact on people their age, up from 32% in 2022.

A highlight of the program was the play *Luddite Recreations*, which retells the history of the Luddite movement to an audience of around 300 people. The schedule also included hands-on workshops, such as an amateur radio class and walks designed for "flirting without dating apps." An organization called the School of Radical Attention—dedicated to discussing the effects of digital platforms on human attention—also participated in the activities. The festival organizers prefer to remain anonymous and address the public through a puppet named Gowanus—a sort of blue-cloth spokesperson. According to Gowanus, the group consists of "organizers with no formal affiliation who noticed similar issues regarding isolation and over-reliance on big tech companies," and they began planning "Summer of Ludd" in January.

"We believe the event is a vehicle for social change, where people can meet in physical space," Gowanus stated. "When we try to organize online, Mark Zuckerberg’s eyes and Silicon Valley’s fingers are all over the sacred interactions of our lives."

Participants interviewed by WIRED shared personal experiences regarding stepping away from screens. Student Staoue, who preferred not to give her last name, said she became interested in the subject after switching from computer science to the humanities. According to her, the fast pace imposed by technology drives people to scroll through screens as a way to cope with stress, when they could instead be learning a language or a new hobby.

Meanwhile, 20-year-old Mara McGuire highlighted the value of direct contact with others. "What interested me most was the emphasis on human connection and gaining new perspectives by going out into the world," she said.

A former employee of a major tech company, who asked not to be identified for fear of retaliation, reported quitting his job after company leadership began encouraging non-technical staff to write code using artificial intelligence tools and deploy that code into production. He described the practice as a cause for concern for those working in systems security.

A broader movement rejecting technology...The New York festival is part of a wider trend of rejecting certain uses of technology. Dating apps are losing ground to in-person meetups—such as those organized by running clubs—according to a report by Forbes. Graduation speeches extolling artificial intelligence have also been met with boos from students at recent ceremonies in the United States.

According to Andrew Maynard, a professor of advanced technology transitions at Arizona State University, the original Luddite movement was driven by labor concerns rather than a blanket rejection of technology. Even so, he acknowledges that the term's current usage aptly describes those who resist technology's encroachment on their autonomy. However, the researcher believes such movements are unlikely to significantly alter people's day-to-day behavior.

Damian Thomas, the developer behind Unplatform—a site that aggregates alternatives to social media—took part in the festival's activities and noted that his professional background drew him to the subject. In his view, just as 19th-century Luddites relied on machines controlled by third parties, today's technology also concentrates power in the hands of a few companies. According to Thomas, most people won't abandon social media overnight, but they can adopt habits and tools that gradually reduce that dependency.

"We are where public opinion is," Thomas said, summarizing the movement's reach among those who did not directly participate in the festival.

Gen Z is leading a pushback against technology because they are seeking to regain control over their attention, mental health, and privacy. Having grown up immersed in the digital world, they are deeply aware of algorithmic manipulation, the pressure of social media performance, and the threat of artificial intelligence to human creativity and job security.

Digital fatigue and burnout: Young people report exhaustion from the constant A/B testing, metrics-driven validation, and performative curation popularized by older millennials.

Mental health concerns: Extensive research shows constant connectivity degrades mental health and shortens attention spans. Gen Z has grown increasingly critical of the platforms they use, with Pew Research noting a massive jump in teens stating social media has a negative effect.

The "dumbphone" trend: Seeking relief from constant notifications and toxic algorithms, many Gen Zers are voluntarily downgrading to basic, feature-limited "dumbphones". The resurgence of single-use devices like film cameras, standalone MP3 players, and paper planners is part of a broader shift toward digital minimalism.

Job displacement fears: The rapid push for AI automation has sparked major anxieties about the future of entry-level jobs and the stability of the modern workforce.

Erosion of authenticity: Young people fear that AI is eroding the quality of real human interaction and flooding the internet with low-quality, fabricated content.

Workplace resistance: A significant portion of Gen Z employees are deeply distrustful of corporate AI initiatives. Surveys have revealed that many deliberately resist or subvert their company's AI integration to protest job displacement and the paradoxical increase in their workload

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TECH

AI-powered social media can subtly manipulate opinion at scale, new study finds

The study found that large language models (LLMs) consistently changed the direction of social media posts on contested topics, even when explicitly instructed to preserve the original meaning. The researchers also show, through simulations of real-world social networks, how these small changes could accumulate across millions of interactions and gradually influence broader public opinion. 

The findings raise questions about the growing use of AI-powered writing tools on social media platforms and suggest that AI-mediated communication could become a powerful new mechanism for influencing public discourse.  

The study, AI-Mediated Communication Can Steer Collective Opinion, authored by Dr. Stratis Tsirtsis, Kai Rawal, Professor Chris Russell, Professor Brent Mittelstadt and Professor Sandra Wachter, has been accepted for presentation at the AI4Good and Technical AI Governance Research workshops at the International Conference on Machine Learning (ICML 2026) in Seoul, South Korea. 

Key findings...AI writing and editing tools can introduce bias into social media posts. Large language models (LLMs) systematically altered the direction of users' messages on contested topics, even when instructed to preserve the original meaning.  

Biases were similar across different AI systems. Multiple models tended to nudge posts in similar directions, favouring some positions such as gun control, marijuana legalisation and feminism, while pushing against others such as atheism and the death penalty.  

Small changes in individual posts can influence public opinion over time. Simulations using real-world social network data showed that subtle biases introduced into posts can accumulate and gradually shift opinions across online communities.  

AI-assisted communication creates a new route for influencing public discourse. The researchers argue that AI systems embedded in social media platforms can shape how opinions spread online, creating new challenges for transparency, accountability and regulation.  

The source of bias is not just the AI model itself. The study shows that specific implementation decisions made by platforms can significantly affect the direction and magnitude of AI-generated influence. 

Methods...The researchers instructed large language models (LLMs) from different providers to transform human-written texts on contested topics into improved social media posts. They then analysed whether the AI-generated versions systematically changed the position expressed in the original posts. Next, they used mathematical modelling and computer simulations based on real social network data from X and Facebook to examine how these small changes could spread through online networks and affect broader public opinion over time. 

Example: Grok’s “Explain this post” feature 

By recreating and testing X’s “Explain this post” feature, with a focus on abortion-related posts, researchers found that Grok was more supportive of pro-life posts than pro-choice posts. By removing X’s instructions one by one, they traced this imbalance back to a single instruction telling Grok to “challenge mainstream narratives if necessary”. This experiment illustrates how targeted, easy-to-implement platform interventions can shape the way AI systems influence public discourse online. 

Implications for regulation...The research highlights AI-mediated communication as an emerging form of influence that existing regulatory frameworks do not yet cover. While initiatives such as the EU AI Act and Digital Services Act focus on systemic risks, harmful content, discrimination, and threats to democratic processes, they do not directly address the more subtle ways AI can shape opinions through drafting, editing, or contextualising online content. 

“Our research points to AI-mediated communication as a new and more subtle way of influencing opinions – one the law has yet to catch up with – and offers food for thought about who, or what, is shaping public discourse,” says senior author Sandra Wachter, Professor of Technology and Regulation at the Oxford Internet University of Oxford Institute, 

University of Oxford

DIGITAL LIFE

AI is already changing the way cyberattacks unfold

A new report reveals that criminals are already exploiting artificial intelligence models in unprecedented ways. This scenario concerns experts and could completely transform how companies protect their systems.

For a long time, artificial intelligence was viewed primarily as a tool to boost productivity, create content, and automate tasks. However, as these technologies evolve, they are increasingly being adopted by criminal groups for ever more sophisticated attacks. A recent report shows that this shift is already underway, indicating that cybersecurity may be entering a new era where intelligent programs face off against other equally intelligent programs.

A study highlights that language models are being used by cybercriminals in far more advanced ways than imagined just a few years ago. Rather than merely accelerating repetitive tasks, artificial intelligence has begun to play a direct role in decision-making during cyberattacks.

In practice, this means certain malicious programs can alter their own structure while running, modifying code segments to evade detection by traditional security tools.

This behavior marks a significant shift from conventional malware. Historically, antivirus software and protection platforms relied on analyzing digital signatures, known patterns, and previously cataloged behaviors to identify threats. Now, however, some programs can continuously alter these patterns, making detection far more complex.

According to experts, some groups also use language models to automatically create new routines, adapt strategies based on the target system's response, and modify execution methods to bypass defense mechanisms.

While this type of technology does not yet fully replace human involvement, it reduces the time required to plan attacks and enhances adaptability during an intrusion.

Another aspect highlighted in the report is the growing use of AI by cybercrime groups and state-sponsored organizations, demonstrating that the technology has moved beyond the experimental stage to become an integral part of actual operations. The advancement of artificial intelligence has also brought about another problem: criminals are learning to exploit AI models themselves to obtain information that facilitates the creation of malicious code.

Major commercial systems typically have security mechanisms in place to reject dangerous requests. Even so, researchers observe that some users attempt to bypass these barriers by presenting seemingly legitimate requests, such as academic exercises or information security research. This type of manipulation—known as AI-based social engineering—has become a new area of ​​focus for the companies responsible for these models.

Furthermore, experts warn of a growing underground market for AI-based tools sold to facilitate attacks such as phishing campaigns, the automated production of malicious code, and other illegal activities.

This scenario reinforces a trend already under discussion within the industry: the future of cybersecurity will increasingly depend on the use of artificial intelligence for defense as well.

Instead of relying solely on systems based on fixed rules, companies are investing in platforms capable of continuous learning, identifying unusual behaviors, and responding rapidly to novel threats.

The expectation is that the next generation of security solutions will utilize intelligent models to analyze vast amounts of data in real time, detect suspicious patterns, and react before an attack causes significant damage.

Ultimately, the transformation lies not only in the evolution of malware but in a shift in the digital battlefield itself. While the conflict once played out between hackers and traditional antivirus software, all signs point to a future confrontation between AIs trained to attack and those developed to thwart such attacks.

This technological race is just beginning, and its evolution could determine how companies, governments, and users protect their data in the years to come.

AI has already changed the nature of attacks by democratizing cybercrime, reducing execution time from days to seconds, and enabling large-scale intelligent automation. Advances in frontier AI and autonomous AI have redefined the digital threat landscape.

1. Lowering the barrier to entry...Attacks that once required advanced programming knowledge can now be executed by inexperienced criminals. Generative AI models can be instructed via simple prompts to write malware code, debug scripts, or structure entire campaigns.

2. Machine-speed attacks...AI analyzes thousands of lines of code, networks, and APIs for vulnerabilities at lightning speed, far surpassing human checking capabilities. Where humans once spent hours or days finding a flaw, AI takes only minutes.

3. Hyper-realistic social engineering...The classic phishing email riddled with grammatical errors is a thing of the past. AI now generates perfectly personalized and convincing text. Through voice cloning and video deepfakes, criminals can highly convincingly impersonate executives or technical support staff.

4. Polymorphic and autonomous malware...Criminals employ AI agents that operate continuously. They create malware capable of constantly altering its own code (polymorphism) to evade traditional defense systems. Furthermore, AI can navigate corporate networks, validate data, and exfiltrate it without human intervention. To combat a landscape where AI attacks and defends at the same speed, companies and institutions must adopt automated defenses and behavior-based intelligence, while also protecting their AI models against manipulation. You can follow key cybersecurity trends in the AI ​​era via Palo Alto Networks or read more about the impact on organizations from Microsoft. The continuous operation of AI-powered bots, which work tirelessly, represents another advantage for attackers. Threat actors use generative AI to craft phishing lures, translate content, summarize stolen data, and generate or debug code. AI tools are already capable of independently identifying thousands of critical vulnerabilities in operating systems and browsers.

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