Homegrown jewellery brand State Property is now sold beside luxury giants worldwide
Singapore is not short of jewellery stores.
From the gleaming counters of Cartier and Tiffany at Ion Orchard to the indie labels that have quietly multiplied over the past decade, the options are vast.
But in 2015, when Afzal Imram and his wife Lin Ruiyin launched State Property, there was a specific gap they felt no one was filling—fine jewellery that led with design rather than gemstones.
10 years on, that conviction has taken them from a home workshop to retail shelves in the US, the Middle East, the UK, Japan and Hong Kong. Their pieces have even been worn by Michelle Obama, Taylor Swift and Robert Downey Jr.—a roster that would be implausible for most independent jewellers anywhere in the world, let alone one founded by two Singaporeans straight out of university with no industry connections.
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“We’re as good as anyone else from any other part of the world,” Afzal, 37, told Vulcan Post. “I don’t think we should see ourselves as being held back in some way.”
We spoke with Afzal to find out how the couple built State Property into an internationally recognised indie fine jewellery brand.
A shared obsession with design
State Property’s husband and wife duo, Afzal and Ruiyin./ Image Credit: State Property
Afzal and Ruiyin, 36, met around 2010 through mutual friends. He was studying industrial design at NUS, while she was at Central Saint Martins in London, completing a degree in jewellery design.
The uncanny geographic distance closed when Afzal went on an exchange to Paris, where the pair spent the time collaborating on each other’s projects, each covering what the other lacked.
“What she’s good at, I’m not good at, and what I’m good at, she’s not so good at,” Afzal said. “We managed to kind of help each other out.”
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Both were drawn to architecture and geometry—a design sensibility that would later define State Property. Fine jewellery felt like a natural medium for the couple, who wanted to create pieces that could endure for years, even generations.
“With fine jewellery, it’s not as throwaway as fashion accessories. The trends don’t move so fast, and people keep the pieces for much longer. For generations, sometimes,” Afzal explained
After graduating from university in 2014, the duo began taking on commissions and design consulting work from home through a studio they called Proper People—which still operates today and handles State Property’s branding—to fund State Property’s first collection.
A year later, they launched State Property.
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Crafting jewellery across continents
(Left): The Holmes Earrings from State Property’s first collection, Substate; (Right): The Edessa Epaulette Enchantress Bracelet from its fifth collection, Arcane. /Image Credit: State Property
Where traditional jewellers start with a beautiful stone and design around it, State Property does the opposite and prioritises the concept of the piece, then sees what material best fits.
Their vision, as the founders put it, is to strike a balance between minimalism and maximalism—inspired by the 20th-century design style Art Deco, which is characterised by modern materials in symmetry.
Every step of the process for each product is drawn and made by hand./ Image Credit: State Property
A new collection can take anywhere from six months to a year to develop, with each treated as a living body of work. New pieces tied to the same theme are introduced over subsequent seasons, while designs that no longer fit are gradually retired.
Today, State Property has seven collections spanning fine jewellery for both men and women.
Prices start at S$750 for a single 14-karat gold and diamond earring, while pieces from the brand’s anniversary Story of Everything collection range from S$4,980 for a toadstool pendant to S$11,650 for the Railroad Diamond Bracelet. Bespoke commissions and special edition pieces can cost significantly more.
The creative process involves collaborative brainstorming by the duo and prototyping, which ranges from Ruiyin’s sketches to creating wax models worn for scale and comfort, before anything goes up for final production.
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Eventually, all final pieces are made in 18-karat gold, with production split across a trusted network of workshops in Hong Kong, Sri Lanka, Italy, and elsewhere, each chosen for specific techniques.
Diamonds are cut in India; gemstones are sourced from Sri Lanka and Brazil. For bespoke commissions involving a client’s own stone, the casting, enamelling, and stone-setting may each happen in different locations—with the final setting done by local artisans in Singapore, so the founders maintain direct oversight of the gemstone at every stage.
Every collection balances accessible everyday pieces with more experimental designs that Afzal knows may be slower to sell—but those, he’s found, generate the most excitement and allow the duo to push their creative boundaries.
“That’s what keeps the brand moving the needle.”
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Building credibility from scratch
Designing is done in-house, while other parts of production take place all over the world, including Singapore./ Image Credit: State Property
For the first couple of years, growth was slow and almost entirely organic. The two knew how to design, and they knew how to make a brand look good. Everything else, from pricing to strategy and managing a team, had to be learned on the job.
Pricing, in particular, was a sticking point early on. The most expensive pieces from that first 2015 collection went for around S$3,000. Even that felt bold at the time.
“Asking someone to pay S$3,000 for our work? Who wants to spend that here?” Afzal recalled thinking.
The brand’s first real turning point came in 2017, when State Property won the Emerging Accessories Designer of the Year at the Singapore Fashion Awards.
One of the judges was Tina Tan-Leo, a veteran of Singapore’s fashion retail industry, who offered some perspective that neither founder had considered. “She basically said, ‘Guys, your stuff is good to go international. Why aren’t you there?’”
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Tina went on to mentor the State Property for six months, reworking pricing, laying out processes for export, and crucially introducing them to the wider world of independent designer jewellers.
“She was the key for us to put State Property on this international trajectory,” Afzal said.
Ruiyin had described the moment as “surreal”: seeing something that started as an idea in a sketchbook worn by one of the world’s most recognisable women on a red carpet.
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From there, the list grew to a degree that still seems improbable for a Singapore business: Rihanna, Gigi Hadid and Florence Pugh, among others.
For a label with no family connections in the industry and no established country reputation to lean on, the cultural legitimacy this provided was transformative.
“If you say you’re a jewellery brand from Singapore, people internationally aren’t sure what that means,” Afzal said. “We had to build that trust ourselves.”
In 2021, that trust was further cemented when State Property became the first Singaporean fine jewellery label on global luxury e-commerce Net-A-Porter.
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Getting onto the main street
(Left): Jenna Ortega wearing State Property’s Markeli Twin Link Necklace and Darro Diamond Necklace; (Right): Catherine O’hara wearing the Bering Ring./ Image Credit: State Property
For most of State Property’s early years, discovery came through word-of-mouth, Instagram, and appointments only.
The brand participated in Boutique Fairs starting in 2018, and had a consignment presence at Tangs Plaza from 2019 to 2021—both useful for visibility, but limited in reach.
However, the audiences at these places were accustomed to affordably priced goods, not 18-karat gold and diamond fine jewellery.
That changed with brick-and-mortar retail. State Property opened its Armenian Street atelier in early 2021, followed by a boutique at Takashimaya Shopping Centre in Sep 2022.
The foot traffic proved what no amount of digital marketing could replicate for the luxury jewellery brand. Back then, local and international sales were roughly even. Today, Singapore has become the stronger half.
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Internationally, State Property now sits in approximately 20 retailers across Hong Kong, Japan, North America, the UK, and the Middle East, including Dover Street Market London, Moda Operandi, Goop, and most recently a trunk show at Bergdorf Goodman in New York.
Cracking those markets wasn’t just about getting stocked, it required genuinely understanding how people in different climates and cultures dress.
“In Singapore, we don’t have seasons that affect how we dress,” Afzal said. “But in temperate climates, there are considerations that don’t come to us naturally—what earrings work when you’re wearing a scarf, whether you’d even wear a bracelet if you need gloves.”
The research meant travelling to each market, observing what people were actually buying and wearing, and only partnering with retailers whose values aligned with the brand’s. “We’re fortunate to have caught the eyes of key retailers like Saks Fifth Avenue and Goop,” Afzal said.
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The team today numbers about 10, and has grown to become a family affair: Afzal’s brother handles the finances, his sister-in-law manages retail partnerships, and a childhood friend of Ruiyin’s oversees manufacturing.
Building a premium brand in Singapore
Some bespoke rings designed by Afzal and Ruiyin./ Image Credit: State Property
State Property’s clientele is made up of people typically in their late 30s to 40s, who tend to be culturally driven and already well-versed in luxury jewellery. These are customers who own pieces from Cartier, Van Cleef, and Tiffany, and are looking for something that sits alongside that collection rather than duplicating it.
Most of its business also comes from returning clients. The brand does not chase volume. “We celebrate every sale,” Afzal has said, “because each one means so much to us and goes a long way in helping us build the brand we envision.” It is built, deliberately, on relationships.
State Property’s pearl necklace, pearl earrings and rabbit chair auctioned for charity in 2019./ Image Credit: Indesign Live, State Property
In Jul, State Property will take part in the Singapore International Jewellery Expo, along with Singapore Diamond and Jewellery Week and the World Diamond Congress. The brand will mark the event with new designs and an art collaboration with local mushroom growers, using mycelium to create jewellery displays.
Afzal and Ruiyin are also interested in exploring adjacent categories.
In 2019, State Property was invited to customise a Qeeboo Rabbit Chair for a group installation at design fair Saturday Indesign. They styled it with a pearl necklace and earring, keeping true to their jewellery aesthetic even on a piece of furniture. The chairs were later auctioned to raise funds for the Children’s Cancer Foundation.
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Their measure of success has shifted over time.
“When we started, success looked like recognition—getting into the right stores, having press coverage, being seen,” Afzal said. “But now success is when a piece we made becomes part of someone’s life, when we become the go-to brand for a client’s jewellery needs, or when clients come to us to commemorate deeply personal moments.”
For founders looking to build something premium from Singapore, Afzal advised not to shortchange any part of it.
“The brand has to be built holistically—from how it’s presented online all the way to your after-sales service,” he said. “There is nowhere within that where you can drop the ball.”
Adobe is bringing AI assistants to some of its biggest creative apps.
New chatbot-style tools are now rolling out in public beta for Photoshop, Premiere Pro, Illustrator, InDesign and Frame.io. The new assistants are designed to handle repetitive tasks and help users make edits using natural language prompts.
Rather than digging through menus or learning complex workflows, users can simply describe what they want to do. Then, the software does much of the heavy lifting.
For Photoshop users, that means being able to reorganise layers, swap backgrounds, resize assets for different platforms and make other edits by describing the desired result. It’s a broader version of the AI-powered editing tools Adobe has already introduced through Firefly. Furthermore, these tools are also in the web version of Photoshop.
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Premiere Pro is getting what could be one of the most useful implementations. The assistant can organise footage into bins, rename clips based on what’s happening in a scene and even analyse spoken dialogue to automatically place markers on a timeline. In addition, Adobe says it can also help create an initial video structure. This reduces some of the setup work that often comes before editing can begin.
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Illustrator’s assistant focuses on production tasks, helping users spot missing fonts, fix colour mode issues and reorganise layers. It can also generate multiple design variations from spreadsheets and documents.
Meanwhile, InDesign’s version is aimed at publishing workflows, allowing users to apply styling updates and print-readiness checks across entire layouts. Frame.io users can use the assistant to organise assets, surface revision notes and even suggest B-roll footage during the editing process.
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While each assistant is tailored to its respective app, they’re all powered by Adobe’s underlying “conversational creative agent” technology. Adobe says the goal is to give every creative professional an AI assistant that understands the tools they’re already using. Rather than offering a one-size-fits-all chatbot, Adobe aims for a more personalised experience.
The rollout marks one of Adobe’s biggest AI expansions yet. After introducing AI assistants to Express, Acrobat and Firefly, the company is now bringing the same prompt-driven approach directly into the Creative Cloud apps. These are the apps that many designers, photographers and video editors use every day.
Tech has taken over our lives. We have smartphones, smartwatches and smart TVs. There are even smart fridges, smart toilets and smart sex dolls. (Or, uh, so I hear.) And with the rise of AI, Big Tech is now jumping on the smartglasses bandwagon… again.
An analog rebellion is brewing. I recently went to a Barnes & Noble for the first time in well over a decade. I was surprised at how many young, hip people were there, scouring the print books and vinyl records. Then there’s the resurgence of digital cameras, film cameras and cassette tapes.
When smartwatches started popping up in the mid-2010s, they promised quick info at a glance without having to grab your phone. In theory, that meant freeing you up to engage with the world around you. But in practice? Well, over a decade later, not everyone finds that to be the case.
To be clear, nobody’s arguing that people are ditching smartwatches left and right. In fact, the market is steadily growing, not shrinking. But not everyone wants to keep marching in that direction.
“My smartwatch kept me attached to b******t I wanted it to get me away from,” born-again analog watch user RadioAdam posted. But not everyone needs to go back to the days of Casio and Timex. Minimal wearable tech products can track your fitness, just without feeling like you have a second phone on your wrist.
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Notification overload
The persistent nag of the online world can feel even more intrusive when it’s on your wrist. It’s one thing to hear your phone chirp in a pocket or bag. It’s another to have a wearable device poking you every time something comes in.
“I don’t want my wrist to communicate with me at all” u/NeoMoose wrote in the Whoop subreddit. “My phone is already too much distraction.” Of course, you can silence notifications. But at that point, you (like these smartwatch expats) might question how much you need one in the first place.
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Big Tech sold us the always-online lifestyle as a utopia. But the reality has too often resembled a dopamine-addiction hellscape. And if you’re looking to cut down on devices, smartwatches are an obvious candidate for the first item on your list.
Feature (and tracking) fatigue
Will Shanklin for Engadget
Smartwatches can suffer from feature creep. While an Apple Watch has potentially lifesaving ones like fall detection and the ability to call emergency services from your wrist, it (and its competitors) also have… lots of other stuff.
For instance, Redditor u/Adventurous_Rice_731 briefly switched from a minimal Whoop to a Garmin smartwatch and quickly regretted the decision. “Went to my first [workout] and realized how many times I was actively checking the screen, looking to see if all my reps were recording,” they posted. “Overall, I just found myself glued to it even during TV time.” Simpler devices could keep you focused on not just the task or activity at hand, but also help you stay present in moments.
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For some, health tracking can (ironically) increase stress. On top of that, when smartwatches and other fitness trackers measure things like sleep, stress and recovery, they’re merely estimates. Those things can’t be measured directly with a wrist-worn device, only approximated via advanced algorithms. Some people don’t see much point in using data that’s little more than an informed guess, as opposed to paying closer attention to their body.
In this economy?
Smartwatches, at least the most useful ones, can be expensive. For example, the Apple Watch Series 11 starts at $399. Samsung’s and Google’s alternatives are in the same ballpark. And while the Apple Watch SE is a more affordable $249 and up, it lacks several key health features (ECG, blood oxygen and hypertension monitoring).
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With inflation running rampant, it’s easy to cast a more critical eye on the value of a smartwatch. Sure, it’s nice not to have to whip out your phone to check messages or the forecast. But is it $400 nice? If all you want is health tracking, wearables like Google’s Fitbit Air and Nothing’s CMF Watch 3 Pro offer it for a small fraction of the price.
Road safety
Smartwatches may also make driving less safe. One study found that drivers were more distracted by smartwatch notifications than phone alerts. Glancing down at a watch seems more likely to take your eyes off the road than glancing at a phone, often mounted on a dashboard. (For the record, voice-based responses on either device were the least distracting.)
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Arguably, this one is more about applying common sense and self-control than it is about the device itself. But it’s another factor to weigh when questioning whether you need a screen on your body.
Style and substance
Cherlynn Low for Engadget
Tech companies do their best to make smartwatches look good. I’m in the camp that doesn’t mind the aesthetic of the Apple Watch and some of its rivals. But if I were basing my decision on style alone, above all else? I’d go with a sleek analog watch without hesitation.
The advantage of screenless tracking bands is that they’re typically subtle enough to wear alongside more stylish watches. They could also be easier to dress up or wear to events where smartwatches are frowned upon. And if you’re looking for something that still tells the time and tracks your steps while looking like a classic timepiece, there are hybrid smartwatches from companies like Withings and Garmin that could meet those needs.
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Opting for something simpler
Cherlynn Low for Engadget
If a smartwatch seems like a bit much, there are simpler and cheaper alternatives.
Screen-free fitness bands are having a moment with the recent launch of Google’s $100 Fitbit Air. The device, which impressed us in our review, is currently sold out everywhere. Whoop, the apparent inspiration for Google’s product, is another screenless contender with robust health tracking. However, it requires a subscription that ranges from $149 for the first year (then $199) to $359 annually, which can put some people off.
Then there are smart rings. Although they’re more expensive (the new Oura Ring 5 starts at $399), they excel at sleep tracking and recovery metrics. Of course, they also lack a screen and haptics, so it’s one less thing bugging you. There’s also the Samsung Galaxy Ring, a $400 competitor that’s often on sale for $300 at big-box retailers.
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As a bonus, these free up space on your wrist for an analog watch. “I can wear a mechanical watch and be more in the moment,” u/Th3p4l4d1n posted. “The Whoop allows me to do that more since it has auto workout tracking.” Plus, you don’t need to worry about charging classic timepieces. And they won’t become obsolete in a few years.
There’s no shortage of variety (in style and price) in that space. For example, Casio has a plethora of options, starting at $30. Or, for that matter, head to any jewelry or department store and have at it. And while old-school timepieces don’t promise the moon, they also won’t lower your attention span or raise your blood pressure.
Siri AI is turning out to be absolutely brilliant, except when it isn’t, plus there are now Snap Spectacles, and rumors about the iPhone Fold, on the AppleInsider Podcast.
Of course you haven’t been so foolish and reckless as to install the developer betas of iOS 27 and the rest. These do seem to be remarkably stable, but your two hosts have both had problems, and totally different ones.
They’re not calamitous problems, but these are the same betas, on similar devices, being used in the same way, yet giving completely different difficulties. So, seriously, stay away for now.
Although when Siri AI is at its best, it is superb and you will want to use it. Just be reassured that Siri AI is far from always at its best, and both hosts are hoping for some marked improvements before this is all released publicly.
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But speaking of releasing publicly, this week also saw the launch of another set of AR glasses. Snap has released its Snap Specs and from just the right angle, in just the right light, they still look poor.
Lastly, it wouldn’t be a week of Apple news without iPhone rumors, and there have been so many this time. From conflicting reports of delays with the iPhone Fold, to perhaps wishful thinking about an iPhone Air 2, we’ve got it all.
BONUS: Subscribe via Patreon or Apple Podcasts to hear AppleInsider+, the extended edition. This time, it’s about those different beta problems and just how it’s affecting our work.
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Waymo had to recall a similar number last month after it discovered a bug that allowed AVs to drive onto flooded roads.
A new recall notice shows that Waymo is pulling nearly 3,900 robotaxis from US streets over a software issue that lets autonomous vehicles (AVs) enter and drive in closed freeway construction zones.
“Under certain circumstances”, Waymo’s fifth-generation automated driving system (ADS) software could allow AVs to enter and drive “at speed” in freeway construction zones, according to the safety recall report filed with the US National Highway Traffic Safety Administration (NHTSA) on 17 June.
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The ADS in question is unable to recognise construction zones, or “inappropriately” prioritises avoiding other freeway hazards, the document noted. Waymo said it owns all of the 3,871 robotaxis it is recalling.
Mounting safety concerns alongside political roadblocks hindering its rollout plans in the US are bringing into question whether Waymo – or its competitors – might succeed in enabling wider robotaxi adoption.
Waymo said it began monitoring the latest issue after six separate incidents in April where its robotaxis failed to recognise, and drove past, ramp closure signs into pre-planned freeway construction zones in Arizona.
Seven similar incidents in mid-May saw Waymo AVs drive between traffic cones to enter freeway lanes with active construction in the San Francisco Bay Area. The company decided to recall the cars on 8 June.
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“We identified an area of improvement regarding performance around freeway construction zones,” the company said in a statement to news publications. “We voluntarily restricted freeway operations last month while making improvements, proactively notified state and federal regulators, and decided to file a voluntary software recall with NHTSA.”
This is the sixth recall Waymo has had to issue for its robotaxis, TechCrunch reported. In December, the company issued a software recall after its AVs drove dangerously around school buses. Other recalls involved low-speed collisions with gates and telephone poles.
Waymo is currently being investigated by the US vehicle safety authority after one of its AVs struck a child near a school in California.
‘If this pilot delivers what we expect, it adds real momentum to Ireland’s decarbonisation story,’ said Equinix’s Irish head Peter Lantry.
Global data centre giant Equinix is testing its first hydrogen-powered back-up units in Ireland.
The 12-week pilot programme will test two hydrogen power generators developed by UK clean energy company GeoPura situated at Equinix’s DB3 data centre in Dublin’s Blanchardstown. The units are currently being used to support cooling systems within the facility.
The three partners believe that the project could provide solutions for Ireland’s grid constraints, which faces mounting pressure from data centres that consumed 22pc of the country’s total metred electricity in 2024. That figure is only set to rise as more companies situate these massive energy users in Ireland.
Equinix and ESB said they will gain valuable data insights into carbon reduction potential as a result of the project, which could be beneficial to policymakers and universities as they assess Ireland’s renewable needs.
Currently, Ireland has 72 data centre buildings that created more than 850,000 jobs and added more than €100bn in annual gross value to the economy, according to a March report from KPMG. The Government says data centres directly employ only 21,000.
Meanwhile, climate activists say that the rapid expansion of data centres cost the Irish economy €715m between 2015 and 2023. Climate group Friends of the Earth, in a recent report, said that households could face an additional €1.43bn in electricity costs linked to data centre growth between 2026 and 2034.
“As data demand continues to grow, solutions like hydrogen power units offer a reliable, clean alternative to traditional backup generation,” said Paul Lennon, the head of asset development at ESB generation trading.
Peter Lantry, the managing director of Equinix Ireland said: “If this pilot delivers what we expect, it adds real momentum to Ireland’s decarbonisation story.”
The new hydrogen generators are a first for Equinix’s 280-plus data centre footprint worldwide. The two deployed generators have helped Equinix bring its power use effectiveness (PEU) – a metric used to measure the efficiency of power usage by data centres – to below 1.3, the company said.
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A lower PEU means data centres are using a majority of the energy consumed for computing. An ideal PEU is 1, which would mean that all the energy consumed by the facility is used for IT, with no overhead for cooling, lighting or other support.
The units, housed in shipping containers, are powered by green hydrogen and use advanced fuel cell technology that allows the system to produce “clean, silent” energy, Equinix said.
They make “zero” direct onsite emissions, and only produce water and heat as byproducts at the point of use. The back-up generators can also respond in real-time to changes in grid capacity and turn on on its own when needed.
“As demand for digital infrastructure continues to grow, operators are facing increasing pressure to secure reliable power, reduce emissions and minimise the impact on local communities,” said GeoPura CEO Andrew Cunningham.
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“This trial shows how hydrogen can help address those challenges today. By combining hydrogen fuel cell technology with battery systems and uninterruptible power capabilities, we’re delivering reliable zero direct onsite-emission power that can respond instantly when required.”
The partners also believe that hydrogen power in this context could offer a viable lower-carbon alternative for construction sites and other temporary power needs traditionally reliant on diesel generation. Hydrogen fuel units such as these are scalable up to 50 MW to support both backup and prime power applications.
According to the trio, the waste heat could also make potential uses for future district heating projects and the water can be recycled into the on-site cooling systems.
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The Hidden Cost of Cyber Risk report, found that often the challenges being faced by companies are as a result of everyday cyber disruption, rather than large scale isolated issues.
The eir business Hidden Cost of Cyber Risk report, which is supported by Microsoft and the Kemmy Business School of the University of Limerick, has found that on average cyber attacks are costing Irish small and medium-sized enterprises (SMEs) up to €3.4bn annually.
However, the greatest impact is not from large-scale, one-off breaches, but rather frequent, day-to-day cybersecurity-related disruptions, that are in turn, driving losses for many Irish companies.
Reportedly, SMEs lose more than 7.2m working days every year due to cyber incidents, with affected businesses experiencing multiple incidents annually. For individual firms, this equates to nearly three working weeks lost annually.
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Susan Brady, the managing director at eir business, said: “This report shows that cyber risk is not just about rare, large-scale attacks.
“For most SMEs, it is the cumulative impact of everyday incidents, from phishing emails and ransomware attempts to service disruptions, that drives significant loss of time and productivity. These risks affect not just individual businesses, but supply chains, customers and the wider business ecosystem.
Challenges big and small
The report noted that, while single events can have significant financial implications, research suggests that the cumulative effect of repeated disruption, downtime, lost productivity and operational interruption creates the greatest economic cost per SME annually. The report also found that “much of this impact is avoidable”, for organisations exhibiting higher ‘cyber preparedness’.
The report stated that the companies with more cyber preparedness tend to experience fewer incidents, lower overall losses and significantly less disruption. Moreover, the organisations with higher levels of preparedness can reduce annual downtime from more than 30 days to around five days, while structured data management significantly lowers the likelihood of experiencing an attack.
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Commenting on the report, the Minister of State at the Department of Enterprise, Tourism and Employment Alan Dillon, TD said, “Small and medium-sized enterprises are central to the Irish economy and ensuring they are resilient in an increasingly digital environment is critical.
“This research highlights the real and growing impact that cyber risk is having on businesses across the country, not just in financial terms, but in disrupted operations and lost productivity. However, with the right support, guidance and focus on practical measures, businesses can strengthen their resilience and reduce their exposure. “
Dr Mauricio Perez-Alaniz, an assistant prof in the Department of Economics, for the Kemmy Business School welcomed the attention to the issue. He said, “While SMEs are increasingly being reminded about the potential productivity and sustainability gains that can arise from the adoption of digital technologies, the issue of cyber risk, and the associated costs of cyberattacks, require more attention.
“This report seeks to do just that. It provides an intuitive approach to quantify the costs of cyber-attacks in terms of direct economic costs, and more importantly, potential costs associated with downtime. It is important to keep in mind that fully quantifying such costs is difficult. While the estimates presented by the report are necessarily high-level and resting on a set of assumptions, they offer important insights into the scale and nature of the issue.”
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In early June, ESET published a similar report, the SMB Cyber Readiness Index 2026, which also indicated that some organisations are neglecting to pay attention to everyday threats, amid a sharper focus on large-scale, one-off cyber incidents. The report found that businesses are risking harm and loss of profits by allowing threats perceived to be smaller, to ‘pass through’.
Previously commenting on the report, Michal Jankech, the vice-president of enterprise, SMB and MSP at ESET, said: “While 78pc of SMBs recognise cybersecurity’s strategic importance, inconsistent understanding of key threats, technology and terminology, including MDR and security posture, suggests there is still room for improvement. Any improvement will have to start with a reality check.
“We’ve found SMBs’ concerns are often shaped by headlines on emerging threats like AI-driven attacks, while more routine risks, phishing, unpatched vulnerabilities and lack of monitoring, are underestimated. This hints that many respondents misperceive their security posture and resilience.”
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Integrity360’s Richard Ford discusses the unease caused by Anthropic’s advanced cybersecurity AI model, and how cyber teams can prepare for such technology.
In the time since Anthropic first revealed Claude Mythos in April, discourse around the cybersecurity AI model has been unceasing.
Anthropic’s claims that Mythos has seemingly advanced capabilities in finding and exploiting software security vulnerabilities caused a frenzy in public and private sectors around the world – including in Ireland.
“The issue is not that Anthropic has created this. The issue is that Anthropic has demonstrated that this is possible,” said Richard Browne, director of the National Cyber Security Centre, when speaking to the Oireachtas Joint Committee on Artificial Intelligence shortly after the Mythos reveal.
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Mythos has not been released to the general public yet, though Anthropic had been granting access to a pool of companies, banks and authorities – that is, before a recent US government order resulted in the company disabling the model for all of its users.
But while institutions and governments panic over the capabilities of this new AI model, Integrity360 CTO Richard Ford says Mythos should be approached with “measured scrutiny rather than hype”.
“Based on the information available so far, the model appears capable as an autonomous attack tool, but there is no clear evidence that it materially outperforms existing large language models in this area,” he tells SiliconRepublic.com.
“The more important point is how it could be used. In the hands of threat actors, Mythos does not need to be revolutionary to be dangerous.
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“It would still be highly effective when targeting organisations with weak security postures, particularly those lacking strong access controls, patching discipline and visibility across their environments.”
Hype and disruption
Ford says that much of what is driving both the hype and the concern around Mythos comes from self-reported results, with limited independent validation.
This makes it difficult to separate genuine technical advancement from narrative, he says.
“There is a legitimate question around whether the capabilities are being overstated or simply presented without enough context.
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“Early claims of large-scale vulnerability discovery sound significant, but without external benchmarking or reproducibility, it is hard to assess how meaningful those findings are in practice.”
Ford adds that in the light of Anthropic’s previous difficulties with the US government, sceptics could reasonably question whether the Mythos announcement was “partly about shaping perception as much as demonstrating capability”.
But what if the purported sophistication of Mythos is as significant as Anthropic claims?
“If the claims hold true, there is a clear view that models like Mythos could begin to disrupt areas such as bug bounty programmes and the wider ethical hacking market,” says Ford. “The concern is not that human researchers become obsolete overnight, but that AI can significantly accelerate vulnerability discovery, shifting the balance in terms of speed, scale and cost.
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“We are already seeing early indicators of this trend. AI-driven platforms are performing strongly in competitive CTF environments, where rapid analysis, pattern recognition and automation provide a clear advantage.
“That raises questions about how traditional bug bounty ecosystems evolve, especially if AI can identify issues faster than human researchers or commoditise parts of the process.”
How can organisations prepare?
Though Mythos has not been fully released to the public yet – and is currently disabled as of last week – Ford has some advice for cybersecurity teams regarding the eventual widespread availability of AI models such as Mythos.
“Cybersecurity teams should treat models like Mythos as an acceleration of existing threats rather than something entirely new,” he says. “The priority is getting the fundamentals right, because AI will exploit weaknesses faster, not differently.
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“Strong identity controls, consistent patching and full visibility of assets remain critical. Organisations that lack these basics will be the easiest targets for AI-assisted attacks. In short, the better your fundamentals, the more resilient you will be as AI-driven threats become mainstream.”
Ford says organisations should avoid reacting to Mythos with panic, but should also take its implications seriously.
“The direction of travel is clear: AI is becoming embedded in both attack and defence,” he says.
He believes any organisation that is not building an AI-driven cyber defence will fall behind and “move directly into the crosshairs of attackers”.
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“That does not mean chasing hype, but it does mean investing in capabilities that improve speed, scale and decision-making across detection and response,” he explains.
“At the same time, this only works if the fundamentals are in place. The organisations that will succeed will be those that combine solid core controls with intelligent automation, allowing them to keep pace as the threat landscape continues to accelerate.”
The reveal of Mythos has undoubtedly rocked the boat in relation to AI and its place in cybersecurity.
But while many worry about the impact of Mythos’s capacity for cyber exploitation, Ford believes the most significant long-term effect of such AI technology will be “a structural shift” in how quickly and cheaply cyberattacks can be executed – rather than a single breakthrough capability.
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“If models like Mythos mature as suggested, they will compress the time between identifying an exposure and exploiting it,” he says. “Tasks that once required skilled researchers and time investment, such as reconnaissance, vulnerability discovery, and initial exploitation, will become increasingly automated and scalable.
“That changes the economics of cyberattacks, allowing threat actors to operate at higher volume and with greater efficiency. All of this depends of course on whether Mythos is indeed just hype or the real deal.”
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Netgear Orbi 970 (2-Pack) for $1,300: There’s no denying that the tri-band Wi-Fi 7 Netgear Orbi 970 is an impressive quad-band mesh. This mesh system is incredibly fast, reliable, and provides expansive coverage with plenty of high-speed Ethernet ports. However, the astronomical price makes it hard to recommend. You can get similar performance for less, and full parental controls now require a separate subscription from the security software. Ultimately, this system is only worth considering if you have a large home, a multi-gig connection, and a generous budget.
More Wi-Fi 6 or 6E Mesh Systems I Liked
TP-Link Deco XE70 Pro
Photograph: Simon Hill
TP-Link Deco XE70 Pro (3-Pack) for $250: Support for Wi-Fi 6E, which operates on the 6-GHz band, is common, but with Wi-Fi 7 rolling out, 6E routers and mesh systems like this are falling in price. A two-pack of this tri-band mesh system is relatively affordable and enough to cover most homes, making this perhaps the best Wi-Fi 6E mesh for most people. I also tested the XE75 ($270 for a three-pack), which is almost identical, but has three Gigabit ports and no multi-Gig. There is also the XE75 Pro ($400 for a three-pack), which features the 2.5-Gbps port and theoretically offers slightly more bandwidth but is far more expensive. Since TP-Link frequently discounts its products, the standard model is the best choice for most people—though multi-gig users should opt for the Pro.
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TP-Link Deco X50 Outdoor for $150: This was our previous outdoor pick, and it’s still a good dual-band Wi-Fi 6 router that will form a mesh with any Deco system (I tested with the Deco X50 4G). It’s a solid performer, but with the Wi-Fi 7 BE25 Outdoor coming in around the same price, I’d pick that instead.
TP-Link Deco X55 (3-Pack) for $150: This affordable Wi-Fi 6 mesh delivers decent coverage and performance, with optional parental controls and antivirus protection, making it ideal for a modest family home. This is a dual-band system (2.4 GHz and 5 GHz). There are two gigabit Ethernet ports on each router. Coverage and speeds are solid, falling short of the Asus XT8 but beating systems like the entry-level Eero 6.
Google Nest Wifi Pro
Photograph: Simon Hill
Google Nest Wifi Pro (3-Pack) for $400: Mesh systems don’t come much simpler than this. Google’s Nest Wifi Pro is a tri-band (2.4, 5, and 6 GHz) Wi-Fi 6E system that works via Google Home, and each router sports two 1-gigabit ports. The setup is super simple, coverage and performance were solid and consistent, and my testing was refreshingly free from glitches and buffering, though WIRED editor Julian Chokkattu had issues that Google’s customer support could not fix. The Nest Wifi Pro came mid-table in raw speed at short, mid, and long range, and settings in the Home app are very bare-bones. Disappointingly, it is not backward compatible with older Nest routers.
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TP-Link Deco X20 (3-Pack) for $130: The Deco X20 is an affordable Wi-Fi 6 mesh that delivers decent coverage and performance, with optional parental controls and antivirus protection, making it ideal for an average family home. This dual-band (2.4 GHz and 5 GHz) mesh was our budget pick for a long time, and there are two gigabit Ethernet ports on each router. Coverage and speeds are decent, falling short of the Asus XT8 but beating systems like the entry-level Eero 6. The app is straightforward, and it’s easy to set up a guest network. Originally released with the free HomeCare software, this has since changed to a HomeShield system, so it’s not as good a bargain as it once was.
Linksys Velop Pro 6E
Courtesy of Linksys
Linksys Velop Pro 6E (2-Pack) for $280: Once up and running, this tri-band (2.4 GHz, 5 GHz, and 6 GHz) Wi-Fi 6E system offers impressive range and decent speeds. It is competitively priced with quite a few dips in cost (don’t pay full price), comes with basic parental controls, and offers handy features like device prioritization and a guest network. But I had a terrible time with the installation. The app continually failed partway through the process, and I had to factory reset the routers. Even then, it took multiple attempts to add the nodes. It’s also not backward compatible with older Velop “Intelligent Mesh” systems, because this is a “Cognitive Mesh” system.
TP-Link XE200 (2-Pack) for $290: This tri-band Wi-Fi 6E mesh system (2.4 GHz, 5 GHz, and 6 GHz) was fast, offered consistently wide coverage, and blew away the Wi-Fi 6 competition at close range. I downloaded a 50-GB game in 20 minutes and didn’t encounter any issues during testing. As it uses the 6 GHz band for backhaul, you have to think about placement and try to keep routers in sight of each other and within 50 feet (or better, connect them via Ethernet cable). While the XE200 is better than the XE70 Pro above, it’s simply too expensive, though it has seen some deep discounts recently, so keep an eye out for deals.
ESG Exposure Research: What AI Changes and What It Doesn’t
An analyst researching a company’s fossil-fuel involvement can now ask a large language model (LLM) for the exact share of revenue tied to thermal coal and get an answer in seconds—complete with a precise percentage, a specific source citation, and a perfectly confident tone.
However, that source could be a regulatory filing that never actually existed.
This is the dual reality of artificial intelligence (AI) in environmental, social, and governance (ESG) data research. On one hand, AI tools act as an efficiency superpower, parsing thousands of pages of sustainability reports, corporate disclosures, and news feeds in the blink of an eye. On the other hand, the high-stakes world of ESG investing demands absolute accuracy, a trait that generative AI—built on probabilistic word-matching rather than factual truth—fundamentally lacks.
As asset managers, rating agencies, and index constructors face tightening greenwashing regulations and stricter disclosure mandates, the role of the ESG analyst is undergoing a massive shift. AI assists them by changing the speed, scale, and cost of processing unstructured data. What AI doesn’t change, however, is the fundamental requirement for data integrity, human skepticism, and the deep contextual understanding needed to separate corporate spin from genuine impact.
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What is ESG Exposure Data Research?
ESG exposure research measures whether a company earns revenue from sensitive or controversial activities, such as coal mining, tobacco production, gambling, weapons production, and animal testing. Rating agencies and index constructors use this data to build exposure screening platforms, risk scores, and exclusion-based indices.
ESG Exposure Research Is Not Equivalent to Report Summarization
ESG exposure is not just a reading task. It is an attribution task. An AI model may correctly identify a sentence stating that a company is connected to gambling operations, palm oil, or weapons production. But a human researcher still has to answer several questions before that finding becomes usable, activity-based ESG data:
Is the company producing the product, distributing it, financing it, transporting it, or only mentioning it as part of a risk disclosure?
Is the activity carried out by the parent company, a subsidiary, a joint venture, or a minority-owned business?
Is the exposure material enough to cross an index, fund, or screening threshold?
Can the revenue share be tied to a source that will withstand review?
These distinctions matter because the answer to “how much of a company’s business is tied to a sensitive activity” is auditable data (a revenue percentage or a yes/no involvement flag). Deducing that revenue percentage or a yes/no flag requires activity-based analysis. For instance, it involves
Production versus participation identification: A company that mines coal and a company that ships it for a fee both touch coal, but most exclusion methodologies treat direct production and indirect participation very differently.
Revenue attribution: “Involved in gambling” is not a data point; “8% of revenue from gambling operations” is. Getting there means reconciling segment reporting, subsidiaries, joint ventures, and equity stakes into a figure you can defend.
This puts exposure data research closer to forensic accounting than to summarization. It needs controversial activity screening, business involvement screening, source checking, revenue mapping, exclusion principle-based outcome alignment—exactly where large language models are least reliable.
Where AI Helps ESG Analysts: Finding Possible Evidence Faster
AI is useful in the discovery stage of ESG exposure metrics data collection. This is the part where analysts look for possible evidence across large volumes of fragmented information.
AI can scan large volumes of ESG disclosure data (such as complex, multi-page, bundled documents and reports) and flag documents that may contain relevant evidence. For example, it can identify a line in an annual report mentioning thermal power assets, detect a subsidiary involved in defense manufacturing, or surface a foreign-language sustainability filing that references tobacco distribution.
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This helps ESG research teams in three ways:
Faster Document Review
Instead of reading hundreds of pages manually, analysts can start with passages AI has flagged as potentially relevant.
Better Language Coverage
AI can help identify evidence of exposure in local filings, regional websites, and non-English disclosures that may otherwise be missed.
Early Structuring
AI can turn unstructured text into well-formatted research leads, including company name, activity type, source document, page reference, and possible exposure categories.
AI improves the speed of document ingestion and scanning and reduces the manual effort needed to collect candidate evidence from hundreds or thousands of documents. But the output should still be treated as a lead, not a final ESG data point.
Where AI Fails in ESG Data Research: Verification and Attribution
The weaknesses appear when AI is asked to decide what the evidence proves. ESG exposure work often requires source hierarchy, accounting logic, and judgment specific to the methodology. Current AI models are not reliable enough to own those steps without review.
1.AI Can Produce Unsupported or Misleading Sources
AI can produce answers that sound well-supported but are not. In high-stakes research, this is a serious problem because the source matters as much as the answer.
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Stanford RegLab’s study of legal AI tools found that even specialized tools from LexisNexis and Thomson Reuters hallucinated between 17% and 33% of the time. That matters for ESG because the workflow is similar: a user asks a research question, the model searches a document base, and the answer must be tied to a reliable source.
There is also ESG-specific evidence. The ESGenius benchmark, which tested 50 language models on ESG and sustainability questions, found that state-of-the-art models achieved only moderate zero-shot accuracy, typically around 55% to 70%. The results improved when models were grounded in authoritative sources, which reinforces the same point: AI output in ESG cannot be trusted without source-level grounding.
The same risk appears in financial table work. The FAITH benchmark, built from S&P 500 annual reports, showed that financial LLMs frequently hallucinate on complex financial table tasks. ESG exposure research often depends on the same type of work: extracting segment revenue, calculating percentages, and reconciling figures across notes and subsidiaries.
If the model misreads a table, cites a weak source, or invents a supporting reference, the revenue exposure data becomes unreliable.
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2.AI Blurs Important Classification Boundaries
AI often collapses distinctions that matter in ESG exposure screening. For instance, a model may classify a company as “coal involved” if a report mentions coal logistics, a backup power unit, a discontinued coal asset, or a risk note on coal regulation. But these are not the same as direct coal production. Ultimately, a human would have to fix such boundary mistakes (e.g., confirming that a logistics company that merely transports coal via its rail network does not qualify as a thermal coal producer under the exclusion policy).
The same problem can appear in other categories. A retailer selling lottery tickets is not the same as a casino operator. A company supplying packaging to a tobacco firm is not the same as a tobacco manufacturer. A business with a palm oil sourcing policy is not automatically a palm oil producer.
3.AI Fails to Adapt to Client-Specific Exclusion Methodologies
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Exclusion policies are not universal; a company that passes a screen for one asset manager might fail it for another. AI struggles here because it treats corporate data as a static set of facts rather than a dynamic input that must be filtered through different client-specific lenses.
For example, an asset manager running a strict faith-based mandate might require a zero-tolerance exclusion of any revenue derived from gambling logistics, while an institutional pension fund might only exclude direct casino operators that generate more than 5% of their revenue from gaming. Similarly, one client may view a company’s palm oil sourcing policy as a positive ESG mitigant, while another client’s strict “zero-deforestation” mandate demands an automatic exclusion if palm oil is present anywhere in the supply chain.
Because AI models are typically trained on generalized compliance definitions, they routinely fail to pivot their logic based on who the data is being collected for. Without highly customized prompting or manual intervention, AI will apply a uniform blanket standard—either over-excluding viable companies or letting flagrant violations slip through because it doesn’t understand the specific client’s shifting threshold for “involvement.”
4.AI Inherits the Bias of Corporate Disclosure
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AI can only work with the evidence available to it. If a company discloses little, uses vague language, or buries information in subsidiaries, the model may produce a cleaner answer than the evidence allows.
This is already a known issue in ESG. MIT Sloan’s Aggregate Confusion Project found that ESG ratings from prominent agencies had an average correlation of 0.54, compared with 0.92 for credit ratings from Moody’s and S&P. That gap shows how differently ESG evidence can be interpreted even before AI is introduced.
AI does not remove that uncertainty. If implemented poorly, it can hide uncertainty by turning fragmented ESG risk exposure data into a single confident output.
ESG Exposure Metrics Research Needs More than Just AI in 2026
Incorrect ESG exposure data does not stay inside a spreadsheet. It can affect index inclusion, fund screening, rating decisions, and client reporting. The cost of weak ESG exposure research is rising for two reasons.
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●First, ESG rating activity is becoming more regulated. The EU (European Union) ESG Ratings Regulation applies from 2 July 2026, with ESMA (European Securities and Markets Authority) becoming the direct supervisor of ESG rating providers operating in the EU. This increases pressure on providers to show how ratings, methodologies, and data sources are built.
●Second, sustainability reporting rules are changing. The EU’s CSRD (Corporate Sustainability Reporting Directive) simplification raises the reporting threshold to companies with more than 1,000 employees and more than €450 million in net annual turnover. That means fewer companies will be covered by standardized sustainability reporting than under the earlier scope.
For ESG exposure teams, this creates a difficult combination. More scrutiny is being placed on ESG data, while parts of the research may depend more on fragmented, non-standardized sources. Ethical AI can simplify ESG data research by helping teams process disclosures faster, organize evidence, and identify missing data points. But in ESG exposure research, that value holds only when AI outputs are traceable, reviewed by analysts, and supported by source-level documentation.
The Operating Model that Works: AI for Discovery, Humans for Attribution
AI should not be removed from ESG exposure research. It should be placed in the right part of the workflow. A reliable ESG Exposure Research model works like this:
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1.AI scans filings, websites, reports, and news sources to identify evidence of possible exposure.
2.Each AI-generated lead is checked against the original source and verified before use.
3.Analysts confirm whether the activity is direct, indirect, current, discontinued, subsidiary-level, or group-level.
4.Revenue exposure is calculated from verified financial data, with assumptions clearly documented.
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5.Each final ESG data point includes source details, date, confidence level, and review status.
6.Unverified AI leads are logged, so teams can track tool performance over time.
The strongest ESG research workflows will not be the ones that use AI to replace analysts. They will use it to reduce search time while keeping humans responsible for verification, attribution, and auditability. As scrutiny of both ESG data and AI tightens through 2026 and beyond, this boundary will decide which datasets can withstand review and which ones cannot.
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