Old habits die hard. Even in 2025, legacy systems like fax machines, PBX phone systems and filing cabinets remain in use in many offices. Another surprising holdover is the traditional VPN (Virtual Private Network.) VPN technology had a nice run, serving as the default choice for remote access for decades. But many organizations continue to rely on legacy VPN solutions, despite significant advancements in secure remote access technologies.

Indeed, in the modern threat landscape, VPN services bring more risk than benefit. Let’s look at five reasons why it is time to say goodbye to VPN infrastructure and what to use instead.

Workloads have moved to the cloud

In recent years, businesses have steadily migrated their operations to cloud computing environments. Consider how many Teams or Zoom meetings you’ve attended this week and how many of your daily applications now operate in the cloud.

This shift has made VPN solutions less and less necessary. As cloud usage increases, the need to remotely access on-premises resources diminishes. Nevertheless, many companies still maintain VPN solutions to provide access to a dwindling number of legacy resources, much like individuals holding onto Blu-ray players solely to watch a couple of dusty discs tucked in an old cabinet.

But is it worth maintaining a complex, outdated VPN infrastructure for a few remaining items, such as a designated machine or a legacy file storage repository? Today, businesses have a multitude of alternative solutions to choose from. These cloud-centric solutions improve efficiency and productivity because they eliminate the need for constant maintenance, patching and monitoring. They can also easily scale up or down to accommodate fluctuating numbers of remote users. But perhaps the most important benefit is stronger security.

Blocking lateral movement and privilege escalation is critical

Traditional VPNs provide broad network access to everything — which can expose sensitive data and critical systems unnecessarily. Indeed, this expansion of your attack surface is what enables threat actors to steadily escalate their privileges and compromise multiple systems across the network. What's more, VPN-enabled accounts sit dormant when not in use, ready for takeover by attackers at their convenience. Compounding this risk, organizations have limited control over the off-premises devices running VPN clients, which further increases the potential for malware infiltration and unauthorized access.

Secure access solutions, on the other hand, provide access to the specific resources that the user currently needs, nothing more. Strictly enforcing the principle of least privilege restricts lateral movement, which in turn significantly reduces the potential impact of a breach. These modern solutions also dynamically create access privileges that last only for the duration of a session and are then removed; this just-in-time approach dramatically reduces the risk of dormant accounts being exploited while maintaining productivity for remote users.

You need better visibility and control

Traditional VPN configurations are static which means they need to grant broad access to each user. This lack of granular control exposes organizations to significant security risks and potential data breaches – once connected, those users can engage in risky activities such as launching RDP sessions or accessing malicious websites without oversight.

In contrast, today’s advanced secure access systems can dynamically modify users’ access rights based on real-time factors such as user behavior, device status and threat intelligence. For example, if an account begins attempting to delete or encrypt sensitive files, its access rights can be cut off immediately.

In addition, live monitoring and session recording capabilities empower security teams to carefully observe and analyze user activity. This comprehensive visibility enables prompt threat detection and rapid incident response. Moreover, enabling deep visibility and accountability discourages users from engaging in risky behavior in the first place.

VPNs are a gateway for multiple threats

An active VPN connection is one more open door to your network. If a user is surfing the web while connected, they can inadvertently download malicious code onto their system. As a result, threat actors can gain remote control of the device, enabling them to compromise MFA tokens, redirect text messages, implement man-in-the-middle (MITM) attacks and more.

A modern secure access solution significantly reduces the risk of these threats because it creates isolated, application-specific connections rather than broad network-wide access. In addition, it enforces least privilege access and applies real-time security controls to detect and block suspicious activity. This proactive approach helps safeguard sensitive data and systems and, therefore, core business operations.

Compliance regulations are becoming stricter

There is no legislative mandate today that dictates that companies cannot use a VPN. However, the goal of compliance isn’t to check boxes; it is to strengthen your security posture. Ask yourself: are you truly meeting your due diligence obligations if you continue to rely on VPN solutions?

Moreover, compliance standards are rapidly changing in response to advances in both technology and the threat landscape. In particular, regulations increasingly emphasize the importance of proactive risk management and adaptive security solutions. Soon enough, regulatory bodies are likely to recommend more robust security measures than VPNs — just as they already discourage the use of old encryption and authentication protocols.

Conclusion

As cyber threats evolve, businesses must rethink their approach to remote access. It’s time to retire VPNs and embrace modern, identity management security solutions that provide far better protection for critical assets while improving efficiency and scalability. Although saying goodbye can be tough, the benefits of contemporary access solutions far outweigh the comfort of maintaining outdated systems.

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This article was produced as part of TechRadarPro's Expert Insights channel where we 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/news/submit-your-story-to-techradar-pro

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As artificial intelligence (AI) technology continues to advance its way further into our daily lives, new, and often controversial, players are entering the space, intensifying the competition. While we see the immediate benefits, these innovations also give rise to a complex suite of legal challenges, particularly in respect of how AI models are trained.

The latest competitor to enter the industry, DeepSeek AI, came under fire, not only, for potential sharing of user data, but also met with claims that its model is trained on outputs from existing AI models – specifically Open AI’s ChatGPT. These allegations raise critical questions for the industry regarding AI copyright, data usage rights, and just how enforceable platform policies really are.

Copyright protection

Competing players aside, one of the most pressing issues in this debate is whether AI-generated content is itself eligible for copyright protection. There’s no immediate and clear answer here as there has not yet been a case of this nature between two AI companies, and it also varies depending on jurisdiction. Many countries still require human authorship as a fundamental condition for copyright ownership, meaning that purely AI-created works often fall into a legal “grey area.

Let’s look into the copyright issue first. AI models operate by processing and generating responses based on vast data pools, making it exceedingly difficult to establish clear-cut cases of direct copying. We can’t use the same methods for identifying traditional plagiarism here, where we’d usually see identical passages of text or near-verbatim reproductions, as AI outputs are inherently non-deterministic—meaning they produce varied results even when given the same prompt.

A competitor AI system might also be trained by repeatedly querying an existing AI model, collecting the responses, and using them to improve its own algorithms. This creates another challenge for enforcement: unless an AI model produces identical or highly similar outputs to another, trying to prove substantial copying remains a significant hurdle.

Infringement claims

One possible approach for AI providers seeking to establish infringement claims is to embed unique, detectable markers within their AI-generated responses. If such markers consistently appear in a competing AI’s output, it could serve as stronger evidence of unauthorized training. However, such methods are not foolproof, as AI models that are trained on large datasets may generate similar responses simply due to the nature of large-scale language modelling.

So, even if an AI company had a compelling case of direct copying, the issue of jurisdiction then comes into play. In regions where AI-generated content does not qualify for copyright, AI companies may struggle to assert ownership over their models' outputs. This raises a further dilemma: if an AI system produces content that isn’t legally protected, can another company legally train its own models using those outputs? And if there’s no copyright to infringe upon, is there even a case for intellectual property theft? Jurisdictions that allow for some level of protection over AI-generated work may provide AI firms with a stronger legal footing.

For example, the US copyright office recently determined that copyright vests in an image that was created by an artist selectively modifying or regenerating parts of an AI generated image through multiple prompts. However, whether an AI provider like OpenAI retains rights over user-generated content would then depend on its terms of service and the licensing agreements accepted by users when utilizing the platform.

Contractual restrictions

Finally, there’s the matter of contractual restrictions. OpenAI, like many AI service providers, has strict terms that prohibit ChatGPT users from employing its AI-generated content to train competing models. If DeepSeek AI, or any other company, violated such an agreement, the issue at hand then shifts from copyright infringement to breach of contract.

If an AI company believes it has a strong case against another, it is highly likely that they would opt to mediate or settle such a dispute privately in order to avoid the potential downsides associated with litigation. A confidential settlement would allow both parties to protect their proprietary training methodologies, reduce costs, and avoid the risks of an unfavorable legal precedent.

However, should a landmark AI training practices case emerge, far-reaching implications would be introduced. If a court can definitively rule that AI-generated materials are protected under copyright law, or that training on another model’s output is an infringement, then this would set a precedent for global AI development and the legal frameworks governing it.

Ultimately, the allegations surrounding DeepSeek AI and the lack of a clear-cut route to protect AI companies highlights that legal frameworks and contractual agreements are struggling to keep up with the pace of a rapidly evolving AI industry. While we await a case that could settle the issues raised around copyright and user agreements, it is important that businesses relying on AI remain vigilant as to how they use third-party services and are cautious to safeguard their own proprietary technology.

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This article was produced as part of TechRadarPro's Expert Insights channel where we 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/news/submit-your-story-to-techradar-pro

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While AI Agents, or agentic AIs, are being touted as the next leap in human productivity, the unadvertised reality is that, like other AI technologies, they are only as good as the humans that design and use them.

They still require a human to know what the problem is and give the right command. But what if you were to combine the horsepower of an agent with an AI that can think like a human? How much more could be achieved?

This question is why many in the AI industry are already looking past agents, and are solely focused on AGI, artificial general intelligence. There are some leaders in the tech industry who believe AGI is as close as two years away. Google co-founder Sergey Brin, recently sent a memo to employees recommending they work 60 hours a week, saying “Competition has accelerated immensely and the final race to A.G.I. is afoot.”

But let’s get real, are we really that close to AGI? Even if we are, will it be able to truly replicate the adaptability and complexity of the human mind? And even if so, should we look past agents now and wait for the promise of AGI?

True AGI is Still a Long Ways Away

Much of the conversation around AGI has started with AI reasoning models. Reasoning models like OpenAI’s o1 and X’s Grok 3, are designed to “think through” problems before they respond. The human mind, however, is much more sophisticated than a reasoning machine. Reasoning models don’t solve the three biggest problems with achieving AGI.

The first problem is AI models, even reasoning models, need to be continually updated and trained. That requires continual human oversight to reinforce the AI’s logic. Without a human telling an AI that it is thinking about things in the right way or the wrong way, it will remain in a perpetual embryonic state.

The second problem is that current AI models cannot assimilate information or adapt to novel situations. If an AI model can think through a math problem, that does not mean it can think through a legal problem.

Finally, the biggest problem is that AI’s are unable to form unique ideas. AI “thought” is confined to the data it was trained on. To put it another way, AI can only restate concepts that already existed. We are struggling to find a way for AIs to come up with anything original.

Dr. Robert Ambrose, NASA’s former Chief of its Software, Robotics and Simulation Division, has posited that a possible solution is to induce AI’s to dream. The idea being that if you can make it possible for an AI to think abstractly and spontaneously by suspending its normal reasoning for extended periods of time, and then having the AI incorporate those abstract thoughts into its training data, you could artificially create original thought.

But scientists don’t even know why humans dream or how, making the prospect of having AI’s dream of electric sheep that much more remote.

Don’t Overlook Agents – They Can Help You Right Now

Believe it or not, you don’t need to wait for AGI to control your agents, general intelligence still works. If you are intentional with how you set up your AI agent, you can use it to substantially multiply your productivity. Instead of performing tasks manually or prompting discrete AIs and automations to do work, you can rely on an AI agent to project manage for you.

Don’t mistake AI agents as some sort of magic “Easy Button,” though. No matter what tech companies are promising you, there is still work that needs to be done on the front end to really make AI agents effective.

Think of it this way, if you hire a person, presumably with general intelligence, and hand them a work manual, would you expect they could do your job on day one?

Like a new employee, your agent needs to be trained on your processes to know how to get the job done. It needs to be given tools and trained on how to use them to accomplish each task in the process chain. That means you need to create underlying automations for your AI to execute in pursuance of its objective. It also needs to be trained on how to handle the different exceptions, roadblocks, and variations that may interrupt the nominal process flow.

The difference with an agent is, once built, you are not at risk of losing an employee anymore. Recruiting and training people is not only expensive but continuous no matter the role. The investment of time and money in an agent on the other hand is a mostly a one time cost, you don’t need to recruit or train a new one ever again.

It may seem daunting but we’re doing it today, and guess what, even AGI wouldn’t be able to work without that groundwork being laid.

The payoff is once that initial work is done, you have an agent that cannot only work at a speed a human never could but can also perform far more task simultaneously.

Don’t Pin Your Hopes on Sci-Fi Timelines

According to Back to the Future we’d have flying cars and time travel by 2015. Blade Runner said we’d have fully sentient androids by 2017. In Terminator 2, Skynet’s artificial super intelligence comes online in 1997. AGI is still in the realm of science fiction, if we wait around for it, we’re missing out on the progress available now.

The path forward isn't about choosing between agents and AGI, but rather about leveraging the concrete benefits of current AI technology while maintaining a pragmatic perspective on future developments. By focusing on thoughtful implementation of AI agents today – establishing clear processes, creating necessary automations, and providing proper training – organizations can realize significant productivity gains without waiting for the uncertain timeline of AGI.

Rather than fixating on when machines might think exactly like humans, we should concentrate on how they can help us think and work better right now.

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This article was produced as part of TechRadarPro's Expert Insights channel where we 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/news/submit-your-story-to-techradar-pro

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Despite AI tools being designed to improve business efficiency through automation, it is being exploited by cybercriminals to threaten organizations at scale, leading to increasingly sophisticated cyberattacks that are outpacing traditional security measures.

As the volume of AI-driven cyber threats rises, over half (54%) of security teams stated they are struggling to keep up. They must now adopt a robust cyber resilience posture in order to protect against these ever-evolving threats.

AI-Driven Cyber Attacks

The National Cyber Security Centre (NCSC) has warned that AI will "almost certainly" make cyberattacks against the UK more impactful as AI enables threat actors to train models on stolen data, refine their methods and amplify the scale and severity of attacks.

The NCSC Report states that AI's rapid data analysis and model training capabilities will make cyberattacks more damaging, leading to higher data breach costs. Additionally, AI's ability to quickly summarize data will enable attackers to target high-value information more easily, resulting in more severe breaches.

AI’s speed is undoubtedly leading to more dangerous threats, allowing cyber criminals to scale up their attacks. For instance, social engineering attacks are increasingly being fine-tuned with generative AI, enabling attackers to tailor their techniques to specific targets faster than ever.

In the long run, we may also see AI being used to automate more mechanical attacks aimed at penetrating systems, although it's uncertain if AI will significantly outperform existing tools fine-tuned by experts.

The growing volume, complexity, and impact of AI-driven cyber operations will intensify challenges for organizations. A recent study from Absolute Security highlights that 46 per cent of organizations view AI as more of a threat to their cyber resilience than a help, 39% of CISOs having personally stopped using AI due to fears of a cyber breach. This highlights the critical need for businesses to adopt a robust cyber resilience strategy to counter the increasing complexity and speed of AI-driven attacks.

The Double-Edged Sword of AI

While AI has certainly heightened the complexity of cyber threats, it has also become a valuable tool for businesses, leading to increased investment. Given that AI is here to stay and will continue to grow in adoption, companies must adapt and integrate it into their strategies as part of cyber resilience.

One way to embrace AI is by using it for data analysis to help businesses identify and mitigate cyber threats more effectively. AI-powered tools can analyze large amounts of data quickly, making it easier to detect potential security issues and anomalies.

The Cost of a Data Breach Report 2024 states that using AI and automation in cybersecurity can help protect organizations by reducing the risk of attacks. As companies use generative AI, IoT devices, and SaaS applications more, their vulnerability to attacks increases. To combat this, AI and automation can be applied to strengthen security strategies.

By continuously scanning for vulnerabilities, analysing data threats, and spotting unusual behavior that might indicate a breach, AI can help manage attack surfaces better. AI also automates patch management and adapts security measures in real time to address new threats. This reduces potential entry points for attackers and strengthens overall security.

AI, especially Generative AI, plays a crucial role in closing the security gap. These systems can process and understand human language, making them invaluable for interpreting vast amounts of data and generating easily digestible insights. AI tools can streamline processes and enhance efficiency across the board.

The same neural network structures can also be used to process language can be applied to security information from devices like laptops and servers, predicting what should happen next and identifying any deviations that may indicate a security breach.

Another way to adapt to the challenges brought by AI is by investing in cybersecurity skills. Upskilling employees in cybersecurity, as well as AI, can help protect organizations from AI-powered cyber threats. For example, 85 per cent of CISOs say their C-Suite has been sent on AI training courses over the past year.

Additionally, 83 per cent of organizations have prioritized hiring AI experts over the past year, recognising the importance of having specialized knowledge to navigate the evolving landscape of AI-driven threats.

Investing in AI-Powered Defense Technology

Although AI introduces significant challenges to cybersecurity, it also provides powerful tools to counteract these threats. In order for companies to remain cyber resilient, there needs to be a balanced investment in AI-powered defense technologies, as well as human expertise. Embracing AI as a vital part of a robust cyber resilience strategy, rather than just a threat, is essential as cyber threats evolve and become more complex.

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This article was produced as part of TechRadarPro's Expert Insights channel where we 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/news/submit-your-story-to-techradar-pro

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The recent AI Action Summit has placed artificial intelligence at the forefront of business transformation, but there is a growing gap between AI implementation and workforce readiness emerging. While companies invest heavily in cutting-edge AI tools, many lack structured approaches to building essential AI competencies within their teams – a disconnect that threatens to undermine the very benefits these technologies promise to deliver.

As organizations grapple with ongoing challenges, from the macroeconomy to changing data policies to the competition for AI talent, the need to develop AI literacy has become a practical necessity rather than a future consideration. Add to that, the rising workforce expectations around flexible work and career development opportunities, and it’s clear that organizations must reimagine how they prepare their teams and also how their employees benefit from the adoption of AI.

The current state of AI adoption

Organizations are implementing AI across their business, from content creation to machine to data analysis. A McKinsey research suggests that up to 30% of hours worked across economies could be automated by 2030, with millions of job changes required in the same timeframe.

This skills gap extends beyond technical expertise; it also includes understanding AI's potential and pinpointing the right applications across workstreams. Rather than replacing workers, AI should be viewed as their “sous chef,” enhancing productivity and efficiency in the workplace.

Research from the IBM Institute for Business Value found that organizations deploying AI at an operational level, rather than relying solely on skills-based initiatives, have outperformed their peers by 44% in key metrics such as employee retention and revenue growth. This suggests that successful AI integration depends not on deploying more tools, but on creating seamless, user-friendly employee experiences that enhance rather than complicate daily work.

Reimagining workforce development

Traditional training methods don't apply to the dynamic nature of AI. Organizations must create comprehensive training that accommodates different levels of AI literacy and varying roles within the company.

Doing this successfully requires nuanced thinking. Companies should implement programs that go beyond technical knowledge and include practical application and ethical considerations. Whether it is developing clear frameworks for AI use or establishing guidelines for use cases, it is important to create a safe space for experimentation and learning.

Above all, any framework created must be flexible and easy to adapt as the technology evolves. Organizations need trust-based management that ensures teams understand not just how to use AI tools, but when and why to leverage them effectively.

Executives and managers need to champion AI adoption, demonstrating their own commitment to learning and development. This top-down approach helps create a culture where AI skill development is valued across the organization. Leaders also need to be prepared to combat digital exhaustion by integrating AI tools into intuitive, unified platforms that support productivity without creating employee frustration.

The human element remains central

Many professionals navigating an AI-enabled workplace are concerned about how their skills and expertise fit in this new landscape. While anxiety is natural as employees wonder how AI will alter their roles, leaders need to help their teams see their place in this transformation.

For instance, some organizations have found success through practical demonstrations, such as having human experts fact-check AI-generated responses. This exercise helps professionals recognize AI as a useful starting point while spotting its shortcomings, validating employees' unique human expertise.

On the other hand, individuals need to identify the skills that make them valuable in their field and lean into them. Soft skills like leadership, compassion, and critical thinking will become even more valuable as routine tasks become automated.

Leaders are the catalysts for AI transformation, pioneering a new era of work by blending technological innovation with human ingenuity. By modelling AI adoption and setting clear roadmaps for AI adoption, they’ll be one step closer to creating a culture that balances technological advancement with employee wellbeing. As teams acquire new skills around AI and critical thinking, leadership's vision and commitment become the foundation for workplace evolution.

The time to act is now. Organizations cannot afford to wait until the AI skills gap becomes a crisis. By taking proactive steps to develop their workforce's capabilities, businesses can ensure they're prepared to thrive in an increasingly AI-enabled future. Taking decisive action now to develop your workforce's AI literacy can help your team prepare for change and also position your business to lead it.

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This article was produced as part of TechRadarPro's Expert Insights channel where we 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/news/submit-your-story-to-techradar-pro

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Right now, AI is the dumbest it will ever be. I hear that line all the time.

Our favorite adjectives of “unprecedented” or “exponential growth” can’t quite capture the pace at which AI is learning, iterating, and advancing. The models that wow us today might be rudimentary tomorrow.

It’s both a scary and thrilling thought. Scary, when we think of regulations playing catch up, the computational bandwidth it might require, and ensuring tech stack agility. But thrilling when we think of the innovation, scale, and productivity just on the horizon. For now, let’s focus on the latter.

One of the most notable “up-and-coming” sectors of AI is in conversational voice and chat. Right now its market valuation is at $5.8 billion. In three years it’s expected to hit $31.9 billion – a 450% increase.

AI-powered agents offer a level of precision in customer engagement we haven’t seen before. They’re simultaneously analyzing historical and behavioral data while keeping the natural flow of conversation – even understanding the nuance of a well-timed pause on a phone call.

These capabilities seem incredible today, but as we know, the bar keeps getting higher. What advancements can we expect from conversational AI agents in the near future?

Adapting with human sentiment and responding to emotional cues

Sentiment is tricky to pick up on. Something as simple as receiving a one-word text can spark a moment of uncertainty – is this person upset? Is it just their style of communication? Usually, we navigate this by relying on our lived experiences and the context of the conversation.

Can AI do the same?

In the past, sentiment analysis was usually done by categorizing words as either positive, negative or neutral (i.e., a polarity score), which created a framework for AI to learn within. Businesses could then analyze social media comments or product reviews at scale to gauge brand sentiment.

Now, advanced machine learning algorithms are starting to go beyond these preset categories. It can understand how sarcasm changes the meaning of a normally positive word – e.g., the difference between an enthusiastic or jaded “wow” – or sense someone’s urgency in their rapid responses. In text, AI can look at punctuation or even historical data to glean emotional subtext; like the difference between “that’s great!” versus “that’s just great…”

By picking up on these subtle emotional cues, AI could start to adjust its responses in real time. This could range from simply acknowledging a person’s frustration to recognizing replies are becoming gradually more delayed, hinting at a waning interest. By weaving in predictive analytics, AI agents could even start to connect sentiment to things like churn or lifetime values.

Emotionally intelligent AI isn’t a net-new concept – in 1995 Rosalind Picard, an MIT lab professor, published “Affective Computing” on the subject. But this once-futuristic sounding idea is now within reach. It’ll be interesting to see customers' reaction. Emotionally intelligent AI can create better customer experiences, but will an AI agent that’s too perceptive come across as eerie and off-putting? I imagine, much like personalization, its effectiveness will ultimately come down to transparency and customer trust.

AI representatives in the metaverse

When generative AI catapulted into the spotlight it pulled focus from another burgeoning technology and channel: the metaverse. Described as a digital playground and a virtual marketplace, the metaverse is reimagining everything from how we purchase products, to education, and collaboration.

Just look to Roblox, the virtual 3D gaming platform worth ~$49 billion, whose growth has predominantly been organic. Its success has cemented immersive, digital experiences as a channel worth investing in. The NFL, Walmart, and Paramount all saw the potential in Roblox to reach new audiences and blend entertainment and commerce.

Even luxury brands are following suit. Gucci has created several metaverse experiences, like their 2023 Gucci Cosmos Land hosted in The Sandbox. As part of the experience, visitors could explore themed rooms, complete quests, and even purchase digital Gucci clothing. (It was reported that Gucci made over 1 million in revenue by selling digital items through Cosmos Land.)

I expect we’ll continue to see brands build in the metaverse, whether it’s a digital equivalent of a physical storefront or a standalone virtual experience. And AI agents will play a pivotal role. They could be personal shoppers, tutors, or run a 24/7 virtual concierge desk. With advanced language models, AI agents could switch between languages and incorporate accessibility features to make experiences more accessible and inclusive.

Even more meta, consumers could have their own AI agents acting on their behalf, like evaluating the best product or offer from multiple brands at once. It’s a completely new dynamic.

However, AI representatives will only be as effective as the data (and the data architecture) behind them. Real-time personalization hinges on the ability of an AI agent to process multiple data streams in milliseconds – analyzing everything from purchase history to user intent.

There might even be certain scenarios where a customer needs to share sensitive information (e.g., payment details, their home address to coordinate shipping, etc.). Success in the metaverse will depend not just on creating engaging experiences, but on maintaining user trust and compliance through responsible and privacy-conscious data handling.

Closing thoughts

Conversational AI is redefining how and where customers interact with brands. In the coming months or years, I expect conversational AI will become increasingly multimodal – able to process and respond to things like spatial awareness, a person’s tone and speech patterns, and even subtle cues like body language and gestures with greater precision.

The use cases we’re already seeing are brimming with potential, from top-tier customer service to integrating AI voice assistants into cars to help with navigation or even monitor driver fatigue.

When we think of what’s possible, there’s often a far-off feeling attached. But AI is rapidly reducing the time between idea and implementation. That horizon, of what AI will be capable of, is much closer than we think.

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This article was produced as part of TechRadarPro's Expert Insights channel where we 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/news/submit-your-story-to-techradar-pro

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Daredevil: Born Again season 1 is almost over already.

The hit Marvel TV show's final episode will be with us sooner than you'd think – but, before we reach its climax, there's the small matter of two other chapters coming to Disney+ first.

So, when will Daredevil: Born Again episode 7 be released on Disney's primary streaming platform? Below, I've outlined when you can watch it in the US, UK, and Australia. You'll also find out when new episodes will launch on one of the world's best streaming services, too. Without further ado, then, here's when you can catch Born Again's seventh installment.

When will Daredevil: Born Again episode 7 launch in the US?

"Vanessa, when will the next episode of our show be released on Disney+?" (Image credit: Marvel Studios/Disney+)

Born Again's next entry is set to arrive on Disney+ in the US at 6PM PT / 9PM ET on Tuesday, April 1.

If you're wondering why we've jumped from Born Again episode 5's release time to episode 7's, it's because two chapters were released on Disney+ last week. Indeed, there was a double helping of Daredevil's standalone Marvel Cinematic Universe (MCU) TV series last week (March 25). So, if you somehow missed that double-header, you'll need to watch its sixth installment ASAP.

When can I watch Daredevil: Born Again episode 7 in the UK?

Jumping into a new episode of Born Again like... (Image credit: Marvel Studios/Disney+)

The Marvel Phase 5 TV show's next outing will make its debut on Disney+ UK at 2AM BST on Wednesday, April 2.

Why the time shift? The clocks went forward one hour in the UK last Sunday (March 30), so those of us on British shores will have to wait an extra hour to watch The Man Without Fear's next law- or vigilante-based story on the streaming giant.

What time is Daredevil: Born Again episode 7 going to debut on Disney+ in Australia?

Things won't get any easier for Matt Murdock from here on out (Image credit: Marvel Studios/Disney+)

Those Down Under can check out Born Again's next chapter on Wednesday, April 2 at 12PM AEDT.

This is the final time you'll be able to watch the show at this time, too. The clocks are due to go back on Sunday (April 6), which makes things better for Australian viewers because you'll be able to stream episodes 8 and 9 at the earlier time of 11PM AEST.

What is the full release schedule for new episodes of Daredevil: Born Again?

We're not a-Muse-d that Born Again's first season has almost ended already (Image credit: Marvel Studios/Disney+)

There are only two more episodes of Daredevil: Born Again to come before its first season ends. Find out when new entries for one of the best Disney+ shows will arrive in the US, UK, and Australia below:

• Episode 1 – out now
• Episode 2 – out now
• Episode 3 – out now
• Episode 4 – out now
• Episode 5 – out now
• Episode 6 – out now
• Episode 7 – April 1 (US); April 2 (UK and Australia)
• Episode 8 – April 8 (US); April 9 (UK and Australia)
• Episode 9 – April 15 (US); April 16 (UK and Australia)

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Marvel Unlimited monthly subscription: was $9.99 per month now $4.99 at Marvel
The ultimate digital comics subscription for Marvel fans is offering new and returning customers 50% off their first month, which is much cheaper than a streaming subscription to watch all the best superhero movies. To gain access to a library of more than 30,000 comics, use the code 'SPIDEY50' at checkout. You're able to use the Marvel Unlimited app on all iOS and Android devices, including the web, too. But, be quick – the deal expires on May 5!View Deal

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