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AI has been a real gamechanger for productivity and automation, but as the technology evolves, the expectations being placed on it are growing fast – particularly in the field of cybersecurity.From boardrooms to SOC teams, the promise is compelling: AI will reduce false positives, accelerate detection, automate response, and unburden fatigued analysts. But while these capabilities certainly aren’t out of reach, AI is not the “plug-and-play” solution that some might hope. Without the right data, context, and oversight, the lens offered by AI is blurry at best.According to a 2024 report by IBM, roughly two-thirds of organizations say they’re now deploying AI tools and automation across their SOC environments. However, a 2025 survey by Darktrace reveals that less than half (42%) of CISOs have confidence in their AI deployment and fully understand how AI fits into their security stack. This gap between AI deployment and understanding how to extract value from it isn’t sustainable long-term.Sprawling webs of interconnectivityNetworks used to be small, contained and relatively easy to protect – often confined to an office or single cloud computing environment. Today, they’re sprawling webs of interconnectivity spanning multiple clouds and endpoint devices. In other words, cybersecurity has gotten more complex.There’s a growing assumption that AI can shed light on this complexity – that if you throw enough data at a model, it will separate the signal from the noise, even without deep integration into your environment. But threats don’t exist in a vacuum. They move through systems, exploit blind spots, and adapt to patterns. And unless an AI system understands the operational baseline – what’s normal, what’s sanctioned, what’s truly anomalous – it’s probably just best-guessing. Sometimes it guesses right, but when it doesn’t, the consequences can be costly.None of this is to say that AI isn’t a force for good. It’s an incredibly powerful tool when wielded in the right way, but businesses need to pace themselves and create the right environment before it can truly deliver on its promises.Are Businesses Prepared for AI?The excitement around AI isn’t new or exclusive to cybersecurity. According to Gartner’s most recent Hype Cycle, both generative AI and cloud-based AI services are currently in the “peak of inflated expectations” phase. What comes next, with any new technology, is the “trough of disillusionment” – this is where the hype meets reality and industries realize that some lessons need to be learned before the technology can ascend to the last part of the cycle, “the plateau of productivity”.This is the very pattern that security teams now find themselves in with AI. Early deployments have revealed just how brittle AI can be when removed from the controlled conditions of lab testing. Sophisticated models that looked flawless in demos can falter in the complex, unpredictable context of a live enterprise environment.False positives are one problem. Analysts know the fatigue of chasing alerts that lead nowhere – and AI, when misapplied, can actually amplify that noise rather than reduce it. But the bigger risk is what AI misses. Algorithms trained on generalized threat data might completely overlook subtle, organization-specific anomalies, such as a lateral movement that piggybacks on a rarely used internal tool, or data exfiltration masked by a legitimate third-party integration. These are the types of threats that slip through when detection efforts lack specific environmental context.Another cause for hesitation is that many AI-powered solutions operate as black boxes, which goes against the grain of the open source, community-driven threat response the industry is now rightly moving toward. Their logic isn’t exposed, their training data isn’t transparent, and their outputs are often unverifiable. For CISOs, that’s a risky proposition.It’s hard enough to explain cybersecurity risks to the board; try explaining why an opaque model flagged – or failed to flag – a critical incident. AI effectiveness is one thing, but trust in AI and its processes is something that must be planned for and cultivated over time.Putting Things into ContextIn cybersecurity, context is everything. AI might detect an anomaly, but can it tell whether that anomaly is benign, malicious, or even expected? That requires more than pattern recognition. It requires a deep understanding of system baselines, user behavior, network topology, and operational rhythms.Without this foundation, AI tools are inevitably prone to misinterpretation: flagging routine administrative scripts as threats, or worse, overlooking subtle indicators of compromise that don’t conform to known attack patterns. That creates more trivial work for security teams as it’s down to them to figure out what’s real and what’s not.This is where network visibility comes into play. AI needs telemetry from every layer of the environment: endpoints, servers, cloud workloads, authentication flows, network traffic, and more. And it needs that data to be correlated, not siloed. An alert from an endpoint only makes sense when viewed alongside what’s happening across the system.A login from an unusual location might be suspicious, unless it’s coming from a known travel route for a senior executive or a new remote hire based in another time zone. AI can’t make those judgments on its own. Without unified context, even the most advanced algorithms are guessing. And in cybersecurity, guessing is always a liability.The Case for UnificationIf AI is going to play a meaningful role in cybersecurity, it first needs a foundation it can trust, and so do the people relying on it. That begins with visibility, but it extends to architecture. Fragmented tools with partial views and proprietary, closed-source alert logic only hinder cybersecurity efforts.What CISOs need is a cohesive layer of detection and response where telemetry is unified, logic is transparent, and automation is tightly aligned with operational context. This is where architectural convergence – for example, the merging of SIEM-level visibility with the orchestration capabilities of extended detection and response (XDR) – becomes critical. This baseline will turn AI into a force multiplier for security teams when correctly deployed.Equally important is explainability. If an AI system flags a potential threat, security teams need to understand why. Not only to validate the alert, but to learn from it, adapt processes, and communicate risk to leaders and stakeholders. Black-box models might seem impressive, but in security, opacity is a threat vector in itself.CISOs don’t need magic; they need clarity. And the best AI implementations are those that put humans in the loop – enhancing decision-making, accelerating triage, and surfacing the insights that matter most without drowning teams in noise.We've compiled a list of the best identity management software.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

Volvo has introduced a new seatbelt technology that can customize the protection it provides in real time. The "multi-adaptive safety belt" system, as the automaker is calling it, uses data input from both interior and exterior sensors to change protection settings based on various factors. It can take a person's height, weight, body shape and seating position into account, as well as the direction and speed of the vehicle. The system can communicate all those information to the seatbelt "in the blink of an eye" so that it can optimize protection for the passenger.

If the passenger is on the larger side, for instance, they will receive a higher belt load setting to reduce the risk of a head injury in the event of a serious crash. For milder crashes, someone with a smaller frame will receive a lower belt load setting to prevent rib injuries. Volvo didn't specifically say if the system also takes the position of a seatbelt on women into account, since it doesn't always fit right over a woman's chest. However, the automaker explained that the system expands the number of load-limiting profiles to 11. Load limiters control how much force a seatbelt applies on the body during a crash. Typically, seatbelts only have three load-limiting profiles, but Volvo expanding them to 11 means the system can better optimize the protection a passenger gets.

Volvo used information from five decades of safety research and from a database of over 80,000 people involved in real-life accidents to design the new safety belt. The system was also created to incorporate improvements rolled out via over the-air software updates, which the company expects to release as it gets more data and insights.

"The world first multi-adaptive safety belt is another milestone for automotive safety and a great example of how we leverage real-time data with the ambition to help save millions of more lives," said Åsa Haglund, head of Volvo Cars Safety Centre. "This marks a major upgrade to the modern three-point safety belt, a Volvo invention introduced in 1959, estimated to have saved over a million lives."

Volvo engineer Nils Bohlin designed the modern three-point seatbelt and made its patent available for use by other automakers. The company didn't say whether it'll be as generous with the multi-adaptive safety belt, but the new system will debut in the all-electric Volvo EX60 midsize SUV sometime next year.This article originally appeared on Engadget at https://www.engadget.com/transportation/volvo-is-introducing-the-first-multi-adaptive-seatbelt-technology-on-the-ex60-ev-070017403.html?src=rss

Now that the Switch 2 is finally here, you may be equally as excited to kit out your new console with the right accessories as you are to dive into Mario Kart World. Not only can the right accessories make it easier and more fun to play all of the games you love, but they can also make your gaming experience better in different environments, be it on the couch, in an airplane or in the car. We're excited to get our hands on some of the newest Switch 2 accessories (more on those below), but plenty of our favorite classics are compatible with the Switch 2 as well as older versions of the console. These are our current favorite Nintendo Switch 2 accessories, and we'll add to this list over time as we test out new gear.

Best Nintendo Switch 2 accessories

More Nintendo Switch 2 accessories

Nintendo announced a bunch of new accessories when it revealed the Switch 2 earlier this year. Key among them are a new Switch 2 Pro controller, Switch 2 camera, an all-in-one carrying case and more. Our staff will be testing out a bunch of these accessories, and we'll keep our favorites list up to date as we do so. If you're interested in picking any of those new Switch 2 accessories up, you can find them at a variety of retailers:

Joy-Con 2 bundle

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Switch 2 Pro Controller

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Switch 2 Camera

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Hori Nintendo Switch 2 Piranha Plant Camera

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Joy-Con 2 Charging Grip

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Joy-Con 2 Wheels (set of 2)

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Switch 2 All-in-One Carrying Case

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Switch 2 Carrying Case and Screen Protector

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This article originally appeared on Engadget at https://www.engadget.com/gaming/nintendo/best-nintendo-switch-2-accessories-070011952.html?src=rss