In the UK, fraud losses reached £1.3billion, showcasing how fraud operates at a growing scale. One of the factors driving this growth is the increasing use of technologies, such as AI, which are becoming dangerous and effective weapons in the hands of a new generation of criminalsAs they continue to evolve, criminal networks are becoming faster and more sophisticated in their operations, and financial institutions face the challenge of adapting to a new era of cybersecurity and fraud prevention powered by the same force threatening the system and customers’ trust.AI is reinventing cybercrimeFraudsters are already using advanced technologies including generative AI to industrialize social engineering. As the marginal cost of a sophisticated attack approaches zero, criminal networks are undergoing a measurable structural shift in how they operate. Skills that previously lived inside a small number of highly specialized criminal groups are now commoditized.Generative AI and synthetic identity creation can now produce composite identities that pass Know Your Customer (KYC) checks, build believable credit histories and operate undetected for months before the fraud is executed. Deepfake-enabled authorization fraud produces voices and videos convincing enough to trick humans into authorizing transactions or transferring access credentials.These examples all reveal a common pattern: they attack the trust layer, the moment where a system or a human decides something is legitimate. Even with incredibly sophisticated models, the tactics will target the system and subsequent operations. AI can now manufacture trust at a speed and quality that was simply not possible before. Why traditional security is falling behindSuch technologies now have the upper-hand as they are largely defeating the customer, not the bank's systems. Fraudsters are using AI to manipulate people into authorizing transactions themselves with more convincing scam calls, more realistic impersonation, more personalized social engineering that erodes the skepticism that used to protect people. The fraud arrives at the bank's systems looking entirely legitimate, because in a technical sense it is.That is where the battle is being fought today. As banks continue to invest in AI-driven detection and the customer manipulation route becomes harder to exploit at scale, the incentive to attack the banks' systems directly increases.We are in a transitional phase, where technology is helping fraudsters to defeat people, this could evolve toward AI directly probing and attacking the institutions themselves. The financial sector should be preparing for that second phase now.Against an adversary that probes, adapts and iterates continuously, the current static nature of traditional security controls are not sufficient. In financial fraud and cybersecurity, AI-driven attacks are not just evading controls, they are mapping them. They behave almost as if they understand the shape of the detection system and are deliberately staying outside its boundaries. A symbiotic relationship between workforce and technologyIn banks and financial institutions, fraud prevention teams are increasingly struggling to keep up with the constant technological evolution of the threats, mostly because they have been trained to think in terms of incidents: if something happens, you detect it, you respond, you close the case.The ones making the most progress are those treating detection as a research function rather than an operational one. They are looking at shifts in behaviors that might suggest something is being tested against us. This different analytical posture ultimately aids in prevention over detection.The professionals who will matter most in five years are the ones who can reason about systems rather than just operate them. There is a need to move from expertise in specific tools to expertise in the underlying principles that make tools work. Principles, such as statistical reasoning, adversarial thinking, systems design, feedback loop analysis, compound over time and remain relevant regardless of what the technology stack looks like in years to come.Not all organizations are equipped to operate this way, and while hiring to fill this gap might be the obvious solution, the reality is that, thanks to advancements and widespread adoption, today we have cutting-edge AI tools that can be easily integrated within existing infrastructure and operations.The way forward is to invest in the work of external providers that make these tools accessible and easy to deploy, combining statistical analysis, reasoning and full explainability.Deploying new technologies without fueling risksYou don’t need to be technically sophisticated to deploy a technically sophisticated attack, and this democratization of capability is what makes the next wave genuinely different. The questions practitioners are working through now are not whether new technologies work, but how to leverage the next generation of capabilities to stay ahead of increasingly automated attacks.The most important principle is to treat your systems as attack surfaces from day one rather than as an afterthought once they are in production. Every model you deploy has assumptions baked into it, and adversarial actors will probe those assumptions systematically. If you have not done that probing yourself first, you are outsourcing your red team function to people with very different intentions.To be effective, AI-driven defenses must work in milliseconds - fast enough to spot risk without disrupting the customer experience. Every transaction is assessed in real time, analyzing hundreds of signals and comparing them against historical patterns to identify any signs of anomalous activity.We've featured the best endpoint protection software.This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

If you work in cybersecurity for long enough, you learn that we really love an analogy or a metaphor and come October you can barely move for talk of security nightmares, cyber monsters and threats lurking in the shadows.I enjoy a good analogy as much as anyone, but there’s one Halloween comparison I don't hear very often. And, for data security at least, I think it might be the most useful one right now: the mask.A mask doesn't make what's underneath disappear. It simply means you don't have to reveal its true identity to everyone who encounters it, and that’s what we’re trying to achieve with sensitive data.Developers need data to test applications, analysts need it to find patterns, and AI tools and agents increasingly need access to enterprise information to do useful work. But needing to use this data isn't necessarily the same as needing to see every sensitive value it contains.That's precisely where data masking comes in. By replacing sensitive information with realistic but unusable values, organizations can preserve the usefulness of data without unnecessarily exposing what's underneath.With AI dramatically increasing where data can travel, who can find it and what can act upon it, that distinction is becoming much more important.A disguise with a serious purposeAt its core, data masking replaces sensitive information with realistic but unusable values. The resulting dataset can retain characteristics necessary for development, testing, analytics and other business processes without unnecessarily exposing the sensitive information underneath. Think of it as a Halloween costume with a security purpose; the data can still show up to the party without revealing its true identity.That distinction matters because protecting data shouldn't simply mean preventing people from using it. Modern businesses depend upon being able to analyze, test against and increasingly train or ground AI systems in enterprise information. Instead, the question should be: does this person, application or AI system actually need the original sensitive value to perform this task?A developer testing an application may need representative customer data, for example, but not necessarily genuine customer identities. An analytics workload may need to preserve relationships and patterns without revealing every account number or personal identifier.If the real value isn't required, exposing it creates risk without creating corresponding business value.Masking removes that unnecessary exposure. Depending on the use case, organizations can also use techniques including tokenization and encryption. The important principle is to match protection to how the information needs to be used rather than defaulting to exposing raw sensitive data.You can't mask what you can't findThere is an obvious complication: before deciding what should wear a mask, businesses need to know where their sensitive information is.That remains surprisingly difficult. The 2026 Thales Data Threat Report found only 34% of surveyed organizations had complete knowledge of where their data was stored. Only 39% said they could classify all of it. Meanwhile, the average organization reported using seven different tools for data protection and monitoring. Cloud infrastructure compounds the problem. Information can be copied into data lakes, SaaS platforms, development environments and backups. Unstructured documents can contain sensitive information nobody realized was there. Applications created years after the data was collected can suddenly gain access to it.AI makes that visibility problem considerably more consequential because finding information is precisely what these systems are becoming very good at.This is where data security posture management (DSPM) becomes important. Rather than treating data security purely as a question of defending infrastructure, DSPM starts with the data itself: discovering and classifying sensitive information and understanding where it resides, who or what can access it, how it is being used and whether its protection is appropriate.Once an organization identifies unnecessarily exposed sensitive data, it can decide whether access should be removed, restricted or protected using controls such as masking, tokenization or encryption.AI data: trick or treat?This becomes particularly important as businesses progress from experimenting with generative AI towards embedding AI into everyday operations.AI applications need data. Agents may need considerably more of it because they are designed not simply to generate content but to retrieve information, make decisions and potentially take actions across different systems.The security implications are already becoming visible. The same research found 61% of organizations reported their AI applications were being targeted by attackers, with sensitive data the leading target. Rapid change within the AI ecosystem was also the most commonly cited AI security risk. The important question, however, isn't simply whether AI can be secured. Organizations also need to ask a more fundamental question:What data are we allowing AI to see in the first place?Giving an AI application access to a database shouldn't automatically mean giving it access to every raw sensitive value contained within it. The same least-privilege principle used for human identities can be applied to data: expose what is necessary for the task and protect what isn't.That could mean masking personal information before it reaches development and testing environments. It could mean tokenizing particularly sensitive values used by an application. It could mean encrypting information and tightly controlling which identities can decrypt it. And in some circumstances, it should mean preventing an AI system from accessing a dataset altogether.Don't make AI security harder than it needs to beThere is a tendency to treat AI security as an entirely new discipline requiring entirely new answers.Some risks certainly are new. But one of the most useful things organizations can do is apply established data-security principles to this new environment.Know where sensitive data resides. Understand its context. Minimize unnecessary access. Protect the underlying value according to how it needs to be used. And continually reassess those decisions as applications and AI systems change.AI may dramatically increase how much value organizations can extract from their data. It doesn't follow that every system extracting that value needs to see everything underneath.Halloween only comes around once a year. For sensitive data, however, having the option to wear a convincing mask year-round is becoming a non-negotiable.We've reviewed and ranked the best data removal services.This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit

Virgin Media O2 currently employs around 16,000 workersHowever reports claim 25% could be set for redundancyNon-customer-facing roles will likely be heaviest hitSky News has reported that Virgin Media O2 (VMO2) plans to cut an unconfirmed number of jobs – understood to be in the thousands – amid ongoing cost reduction efforts and a bid to get a grip on company debt.Though the number of workers said to be impacted is not yet known, the company currently employs around 16,000 workers, so 'thousands' would represent a significant chunk of its workforce.VMO2 looks to be one of the latest companies in a long list to be flattening its hierarchy, with changes to management reportedly underway.VMO2 job cuts look to be around the cornerCrucially, Sky News reports that non-customer-facing roles will likely be heaviest hit. The firm's customer services typically perform well, it seems, with Virgin Media reporting the fewest number of complaints compared with competitors (via Ofcom) on the broadband front. Though O2 sits somewhere toward the top on the mobile front, beating only Sky Mobile (which also uses the O2 network).An unnamed insider believes as many as 4,000 jobs could be at risk, representing 25% of VMO2's entire workforce.It's believed that the company could share precise numbers and details as part of its Q3 earning call on November 3. Overall company revenue dropped 7.9% last quarter.But VMO2's biggest issue is likely debt. Last quarter, it reported a total net debt equivalent to 5.89x annualized adjusted EBITDA.But VMO2 isn't the only telecoms giant adjusting its workforce in response to financial pressures – VodafoneThree also confirmed plans to cut 1,200 jobs in July 2026, though these were Europe-wide.TechRadar Pro has contacted VMO2 to confirm plans to lay off thousands, but we did not receive an immediate response.