The prospect of bringing more of Britain's critical national infrastructure into public ownership has prompted plenty of debate about investment, governance and accountability.

Far less attention has been paid to what it could mean for cybersecurity.

Regardless of where you stand politically, one thing is clear. Public ownership does not make cyber risk disappear.

If anything, it raises expectations that essential services will be more resilient, more coordinated and better prepared to withstand disruption.

That expectation reflects the reality of the threat landscape. Energy providers, water companies, transport operators and healthcare organizations all sit at the center of complex digital ecosystems. Their ability to deliver essential services depends on thousands of suppliers, technology vendors and third parties.

When one organization is compromised, the effects can spread well beyond its own network. Resilience therefore depends on far more than protecting individual organizations. It depends on understanding and managing the relationships between them.

If the government is serious about strengthening national infrastructure, cybersecurity must become part of that conversation from day one. That means moving beyond isolated security programs and towards a model where organizations share intelligence, understand common risks and coordinate their response before disruption spreads.

Critical infrastructure extends beyond organizational boundaries

Some of the defining cyber attacks of recent years have demonstrated that attackers are rarely interested in a single target. They look for opportunities to compromise one organization in order to reach many others.

The SolarWinds attack remains one of the clearest examples. By compromising trusted software updates, attackers gained access to thousands of organizations around the world. More recently, the ransomware attack on Synnovis disrupted pathology services across several NHS trusts, leading to cancelled operations, delayed appointments and widespread disruption to patient care.

Neither incident remained confined to the organization that was initially compromised. Both exposed the reality that critical infrastructure now depends on interconnected supply chains as much as physical assets.

That presents a challenge for every operator of critical national infrastructure. Security can no longer be viewed solely through the lens of protecting your own estate. Organizations also need visibility into the threats affecting suppliers, partners and the wider ecosystem. A vulnerability within a software provider or outsourced service can quickly become a problem for every organization that depends on it.

This is where many existing security programs begin to show their limitations. Organizations have invested heavily in detection technologies, vulnerability management platforms and threat intelligence feeds. They are collecting more information than ever before. Yet many still struggle to translate that information into confident operational decisions.

Better decisions start with better intelligence

The cybersecurity industry has spent years focusing on visibility. The assumption has been that if organizations can discover every vulnerability, identify every asset and collect every threat feed, they will naturally become more secure.

The evidence suggests otherwise.

Filigran's recent State of Threat Management report found that organizations consume an average of fourteen different threat intelligence feeds, yet fewer than half have fully operationalized that intelligence across their security programs.

At the same time, 84% of respondents said the attacks they experience exploit risks that were already known but had not been prioritized. Almost every organization surveyed also reported difficulty determining whether identified exposures were genuinely exploitable.

Those findings illustrate a wider industry problem. The challenge is no longer discovering risk. It is deciding which risks deserve immediate attention.

Security teams are surrounded by alerts, vulnerability reports and intelligence updates. Every tool claims to identify another critical issue demanding urgent action. Without context, everything starts to look important. Analysts spend valuable time investigating vulnerabilities that may never be exploited while genuinely dangerous attack paths remain hidden among the noise.

That has consequences beyond operational efficiency. Every hour spent investigating a low priority issue is an hour that cannot be spent reducing real business risk. Organizations are not simply overwhelmed by the volume of information. They are overwhelmed by the number of decisions they are expected to make every day.

Threat intelligence should shape decisions long before an incident

One reason this happens is that threat intelligence is still too often treated as a function of the Security Operations Centre. Intelligence is gathered, analyzed and used to help detect or investigate malicious activity once attackers have already reached the network.

Yet, threat intelligence has far greater value when it informs decisions much earlier in the security lifecycle.

Used effectively, it should help organizations understand which vulnerabilities are actively being targeted, which attack paths present the greatest business risk and which remediation activities will deliver the greatest reduction in exposure. Rather than treating every vulnerability as equally urgent, security teams can focus on the threats that genuinely matter to their environment.

This is also where Continuous Threat Exposure Management, or CTEM, has an important role to play. CTEM should not be viewed as another technology category or another security acronym. It provides a structured framework for connecting threat intelligence, exposure management, validation and remediation into a continuous process. Instead of relying on assumptions or theoretical risk scores, organizations can validate whether a vulnerability is genuinely exploitable before committing time and resources to fixing it.

Perhaps the biggest obstacle is not technical at all. Many organizations still operate with threat intelligence, vulnerability management, penetration testing and governance teams working independently, each with different priorities, processes and tooling. Breaking down those silos often delivers greater improvements than introducing another security platform.

Building a national capability

If critical infrastructure is expected to become more resilient, collaboration has to become part of everyday operations rather than something that only happens during a major incident. That thinking is already beginning to take shape.

Earlier this month, the National Cyber Security Centre and GCHQ issued a call for industry, academia and critical infrastructure operators to help define Cyber Shield, a proposed national cyber defense capability designed to combine AI, shared intelligence and coordinated defense at national scale.

Significantly, the initiative recognizes that the government cannot build this capability alone. It will depend on close collaboration with the organizations responsible for protecting the UK's essential services.

Additionally, the Cyber Security and Resilience Bill provides an opportunity to strengthen that approach by encouraging greater consistency across essential sectors. Frameworks such as the National Cyber Security Centre's Cyber Assessment Framework already give organizations a common language for measuring resilience.

They become even more valuable when they encourage organizations to learn from one another instead of tackling similar challenges in isolation.

Open standards have an important role to play as well. The Dutch National Cyber Security Centre recently made STIX and TAXII 2.1 the mandatory standard for sharing cyber threat intelligence across government.

While technical on the surface, the decision reflects a broader principle. When organizations exchange intelligence using common standards, they remove friction from collaboration and can respond to threats more quickly.

Technology alone will not deliver that outcome. Artificial intelligence, automation and modern security platforms can help organizations process more information and reduce manual effort, but they still depend on good intelligence, sound governance and trusted relationships.

For the simple reason that fast decisions only become good decisions when they are supported by the right context.

Resilience is a shared responsibility

Whether more of Britain's critical national infrastructure ultimately moves into public ownership is only part of the story. Cyber attackers do not distinguish between public and private organizations. They target weak links, trusted suppliers and interconnected systems wherever they find them.

The organizations that will be best prepared for the years ahead will be those that treat resilience as a collective responsibility. They will operationalize threat intelligence before incidents occur, validate real-world risk rather than relying on assumptions, and collaborate across organizational boundaries as readily as attackers do.

Protecting critical infrastructure has never been solely about defending individual organizations. It is about strengthening the entire ecosystem that keeps essential services running. If the UK wants to build genuinely resilient national infrastructure, that is where the conversation needs to begin.

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The reality for many organizations is that their employees are already being deceived. So, probably are their customers, their investors, and their board.

For years, the warnings have focused on the impact of deepfakes, such as fake CEOs on video calls, cloned voices authorizing payments, and fraudulent emails.

The tactics themselves are not new, but over the past few years, AI has made them cheaper to produce, harder to spot, and far more convincing within the ordinary flow of business.

It’s this trend that means that the principle of Zero Trust is becoming as relevant to how information is treated as it has been to access.

In cybersecurity, Zero Trust starts from a simple assumption: no user, device, application or request should be trusted by default.

In the age of AI-generated misinformation, businesses need to apply that same mindset to the information that moves throughout their organization.

The new trust crisis

Employees, customers, investors and partners are all making decisions based on what they see, read and hear. If that information is false, manipulated or stripped of context, the consequences can move quickly from confusion to commercial damage. Which is why misinformation, disinformation, and malinformation need to be treated as business risks.

Misinformation – the false content that spreads without deliberate intent – has always been an issue. Disinformation, constructed specifically to deceive, is now easier to manufacture at scale than ever before. And malinformation – true information, often deliberately stripped of context and weaponized – might be the most insidious of the three. A competitor, a criminal group, or an activist campaign no longer needs to breach a network to cause serious damage. They can influence those associated and concerned with an organization simply by shaping what those people see and believe.

What makes this particularly difficult for businesses is that it mirrors something individuals are already struggling with. In an environment saturated with AI-generated content, the habits that people must employ to protect themselves – pause before reacting, questioning the source, verifying before acting – are the same habits that organizations must build into how they operate.

Essentially, the instinct to trust has become a vulnerability. And addressing that requires something closer to a structural response than an awareness campaign.

The importance of verified trust

This is where Zero Trust becomes the strategy. Traditionally, organizations have thought about Zero Trust through the lens of least privilege, ensuring that the right users have access to the right applications, and nothing more. But in an AI-driven information environment, that principle needs to evolve. Businesses can no longer focus just on who is requesting access. They also need to interrogate what information is being used, what action is being taken, and whether the intent behind the action can be trusted.

The next stage is going beyond authentication and investigating authenticity, and asking questions such as “Is this information verified?”, “Is this image real or AI-generated?”, and “Has this content been edited?”. Zero Trust gives businesses a framework for answering those questions. It forces organizations to verify before they act, limit exposure where they can, and reduce the risk of false, manipulated, or decontextualised information moving unchecked through the business.

Standards bodies such as the C2PA (Coalition for Content Provenance and Authenticity) show the direction that this is heading: a future where provenance and integrity are embedded in digital content itself, the same way a padlock in a browser indicates that the connection is secure. Essentially trust won’t be something that businesses need to check for, rather it will be something that travels with the information as provenance feeds verifications. Every piece of content therefore becomes a signal in a continuous trust decision.

Developing Trust in the agentic era

The need to trust intent has become even more pressing as AI agents enter the workplace. These agents will increasingly operate like another person working alongside us, mirroring our behaviors, such as reading documents, interpreting data, making decisions, and acting. The difference, however, is that these non-human identities are moving at machine speed, where human-speed verification has no hope of keeping up.

That means AI agents must be governed through a Zero Trust model from the outset. An agent should not be trusted just because it sits inside the enterprise, has been approved by a user, or is connected to corporate systems. Its identity, permissions, behavior and outputs all need to be continuously validated. Just as importantly, agents should be governed by least privilege, the principle of least information, and least function, granting only the minimum access, data, and capability required for a specific task.

However, these agents create a trust challenge that identity management alone can’t solve. Businesses will need to know whether they are dealing with a human or a machine, whether an agent is behaving responsibly, and whether its actions reflect an organization’s values and boundaries. Effectively, businesses will need to adopt an operating constitution that agents are continuously measured against.

And in this AI era, enterprises must interrogate information, content, intent, behavior, and action in realtime and continuously as identity is generally just checked as front door access. However, given the scale of the task at hand, it will take AI to audit, flag, and govern as needed and keep the chain of trust intact.

Engineering trust into the business

The organizations that succeed will be those that treat trust as something to be engineered, rather than assumed. Misinformation, disinformation, malinformation are security, resilience, and leadership challenges, and AI is making them harder to ignore.

As technology continues to shape how information is created, shared, and acted upon, businesses need to build the same discipline around authenticity that they’ve always applied to access. That means verifying content, questioning intent, and limiting what AI systems can do to what they actually need to do.

Trust can no longer be the default setting. Instead, in today’s operating environment, it must be a decision that’s made continuously, at machine speed, across information, intent, behavior, and action. The good news is that the framework already exists in Zero Trust. What needs to change however is how organizations apply it, broadening the scope to include information, intent, behavior, and action.

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Artificial intelligence is entering a new phase, one defined not by experimentation, but by operational deployment in environments where the stakes are high and the margin for error is narrow.

Nowhere is this shift more visible than in critical services such as healthcare, where organizations are beginning to rely on AI not just for efficiency gains, but for decisions that directly affect lives, outcomes and public trust.

As a result, the conversation around AI capability is expanding, and there’s a real need for AI systems to be sovereign, trusted and aligned to the legal, ethical and operational frameworks of the jurisdictions they serve.

Sovereign AI is emerging as a response to this need.

It is not a marketing term or a technical preference; it is a structural requirement for organizations that operate under strict regulatory oversight and handle sensitive citizen data.

For these sectors, sovereignty is the mechanism that ensures AI systems remain under the control of the people and institutions accountable for their outcomes.

Data residency

The distinction between data residency and true sovereignty is central to this shift. Data residency simply describes where data is stored or processed. It is a geographical statement, not a legal one. Data sovereignty, by contrast, defines who controls the data, who can access it and which laws apply. It is a statement of legal authority and operational control.

Sovereign AI goes further still. A sovereign by design AI system ensures that every stage of the AI lifecycle, from training and fine tuning to inference, deployment and monitoring, sits entirely within the sovereign perimeter. This includes the IT infrastructure, the data pipelines, the model governance processes and the personnel who operate and maintain the system. Nothing crosses borders, and nothing falls under the jurisdiction of external authorities.

For critical services such as national healthcare systems, this level of assurance is not optional. These organizations must protect patient confidentiality, maintain public trust and comply with regulatory frameworks that are among the most stringent in the world. They cannot rely on AI systems whose training data is opaque, whose operational footprint spans multiple jurisdictions or whose governance structures are not aligned to local laws.

They need systems that are transparent, explainable and auditable, systems that can demonstrate not only what they do, but how and why they do it.

Regulated sectors

This is one of the reasons why organizations in regulated sectors are increasingly looking beyond general purpose AI models. These models have driven much of the recent excitement around AI, but they are not always suitable for environments where accuracy, safety and accountability are paramount.

Their training data is broad and often scraped from the open internet. Their provenance is difficult to verify. Their operational controls vary widely. And their governance frameworks are not always designed with regulatory compliance in mind. In contrast, domain specific AI models built on trusted, curated datasets offer a level of precision and contextual understanding that general purpose models struggle to match.

They can be aligned to clinical workflows, diagnostic pathways and sector specific terminology. They can be governed with the level of transparency and auditability that regulators increasingly expect. And when built within a sovereign architecture, they can operate entirely within the legal and ethical boundaries required by critical services.

The rise of sovereign AI signals a broader transformation in how regulated sectors will adopt and govern AI over the next decade. AI architectures will become more localized, with sovereign cloud regions, isolated compute environments and jurisdiction specific MLOps pipelines becoming the norm. Governance will become as important as model performance, with explainability, auditability and lifecycle control treated as first class requirements.

Regulators will demand greater transparency around model provenance, training data lineage and operational controls. And AI supply chains, from data ingestion to model deployment, will be scrutinized with the same rigor applied to other critical infrastructure.

What this future looks like

Healthcare offers a clear illustration of what this future looks like. When deployed responsibly, sovereign AI can automate clinical workflows while maintaining strict data protection, support diagnostic decision making with transparent and explainable models, improve patient flow through predictive analytics and optimize resource allocation across hospitals and care pathways.

By reducing administrative burden and helping ensure patients are directed to the most appropriate care pathway more efficiently, it also has the potential to improve productivity and support better use of constrained healthcare resources.

It can also enable population level insights without compromising privacy, allowing healthcare systems to plan more effectively and respond more rapidly to emerging challenges. These benefits are only achievable when the underlying AI systems are trusted, transparent and sovereign.

Sovereign AI represents a turning point in how critical services approach digital transformation. It acknowledges that trust, governance and domain expertise are just as important as model capability.

It recognizes that AI must be built to serve the needs, values and legal frameworks of the communities it supports. And it reflects a broader truth: as AI becomes more deeply embedded in essential services, sovereignty will not be a niche requirement. It will be the standard.

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The legal sector’s use of AI is maturing, and its use is going beyond simply being an external chatbot or standalone tool. The focus is shifting to embedding AI into everyday legal work, giving it access to the documents, matters, knowledge and systems lawyers use every day.

As firms increase their investment in AI technology, they need to look beyond what AI models can generate and focus on a more practical question: how do those models connect to the information lawyers rely on, where does that information sit, and how is access governed?

That connection challenge is why Model Context Protocol (MCP) has started to attract so much attention.

Separating the standard from the assumptions

MCP is an open standard that gives AI tools a more consistent way to connect to external systems, data sources and applications. For all the excitement around MCP, firms need to be clear about what it does, and just as importantly, what it does not do. Otherwise, there is a risk that firms either overestimate what MCP can solve on its own or dismiss it as just another technical acronym.

The reality sits somewhere in the middle. MCP can help AI tools connect to the systems and data sources firms already use, but it does not automatically solve challenges around governance, integration, permissions or legal context. With misconceptions starting to spread across the legal sector, here are five common myths to clear up.

Myth 1: MCP is only for Claude

Although MCP was created by Anthropic, it is not just a Claude feature. It is an open-source framework and is now becoming part of the broader conversation around how AI agents connect to external tools, data sources and systems.

That distinction matters for law firms and legal organizations generally. If MCP is treated as a single-vendor feature, it can be dismissed as something tied to one model or product roadmap. But as a broader connectivity standard, firms need to think about how it fits into their wider AI strategy, integration architecture and governance model.

Myth 2: MCP replaces APIs

APIs still matter. They remain the backbone of platform-to-platform connections, allowing software systems to exchange data and trigger actions. MCP does something different: it acts as a protocol layer on top of APIs, giving AI agents a more standard way to discover and interact with approved legal systems, tools and data sources.

Put simply, MCP is closer to a universal adapter for AI, than an alternative to APIs. Just as USB gives different devices a common way to connect, MCP gives AI tools a more consistent way to understand what systems and functions are available to them, and how they can interact with those systems.

But it does not remove the need for APIs, authentication, system owners or clear rules about what AI tools can and cannot access.

Myth 3: MCP means moving documents into AI tools

There is a common misconception that connecting AI to legal systems means copying a large volume of documents into external AI platforms. In practice, AI should only be given controlled access to governed systems. Documents, precedents and matter files can remain within the firm’s trusted environment, with AI tools using MCP to retrieve only the information they are authorized to access.

Rather than creating another uncontrolled copy of sensitive material, firms can let AI work with the right information while existing permissions, security policies and governance controls remain intact. To hit the right balance, legal IT leaders should be asking “Where does the data stay, what is exposed, and how is access controlled?

Myth 4: All MCP integrations are the same

As MCP becomes more common, there will be a temptation to treat any MCP-compatible integration as broadly equivalent. That would be a mistake. MCP standardizes the connection, rather than the value of what comes through it. One integration may provide a basic route to retrieve files.

Another may provide richer information about permissions, matter relationships, document history, metadata and audit trails. Both may be MCP-compatible, but they will not deliver the same outcome.

Law firms need to focus on what the AI receives, whether access rights are enforced and whether activity can be audited.

Myth 5: MCP automatically gives AI legal context

MCP creates the route into systems, but it does not decide what the AI receives or understands. Connection does not mean context. In legal, this is more than a simple retrieval problem. An AI tool does not just need access to a document in a DMS. It needs to understand the matter, client, permissions, version history, related work and institutional knowledge around it.

For example, an AI tool may be able to find a precedent agreement, but does it know whether that precedent is current, whether it belongs to a similar matter, whether it reflects the firm’s preferred position, or whether the lawyer has permission to access the related material? Without that context, AI may generate an answer, but the answer may not be reliable enough for legal work.

That is why MCP should be seen as the access layer, not the intelligence layer. It can help AI tools connect to legal systems, but the value comes from what those systems expose through MCP: governed, matter-aware and permission-sensitive context.

Connection is only half the story

MCP gives law firms a more standard way to connect AI tools to the systems they already use. But legal AI depends on more than connection. The firms that benefit most will be those that treat MCP as the starting point, not the destination. The quality of the information, the controls around it and the context that gives it meaning will determine whether connected AI makes a genuine impact in legal work.

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Artificial intelligence (AI) is rapidly becoming embedded in the modern workplace, with employees are increasingly turning to AI tools to work more efficiently and boost productivity.

This growing demand for faster, more effective ways of working is driving the rise of shadow AI - the use of AI tools outside approved organizational controls and governance frameworks – which results in organizations quickly losing visibility into data usage and potential risks.

The scale of this challenge is significant. While 90% of executives are confident in their organizations' visibility into AI tools, just 52% of employees admit to using AI tools without approval, often through personal accounts.

As a result, organizations are left grappling with a widening gap between AI adoption and AI governance.

The next frontier of AI risk

When AI is used without formal oversight, it can bypass governance controls, increasing the risk of errors, regulatory breaches and sensitive data leakages. Organizations are most exposed when AI is already influencing business-critical activities, from customer service and operational decision-making to software development and content creation.

The challenge will intensify as businesses move beyond large language models, which generate information, to large action models and agentic systems that can take action. These systems can diagnose issues, recommend actions and execute workflows with minimum human input, increasing both the speed and scale at which mistakes occur.

A shadow agent operating outside approved governance frameworks could trigger harmful actions before organizations have the visibility and governance capabilities needed to intervene.

There is also a longer-term risk that future AI systems will be trained on synthetic or lower-quality data, weakening performance and decision-making over time. Transparency and traceability will be critical to maintaining accountability, protecting ethical standards and preserving the effectiveness of AI systems as adoption continues to accelerate.

AI governance as an enabler

What works is AI governance that enables innovation while putting clear guardrails in place that are integrated, transparent, auditable, and aligned with existing risk and compliance frameworks. If AI is to be used safely, firms must be able to successfully identify exactly what went wrong and why when issues arise.

In practice, mature governance starts with an approved AI tool stack that provides safe and trusted options for common use cases. This should be supported by risk-based policies that make clear the data being handled, what can and cannot be shared, which tools are permitted, and where human approval is required.

Low-risk tasks such as drafting or summarizing content should not be governed in the same way as high-risk uses involving customer data, regulated information or business-critical decisions.

Training is equally important. The challenge, beyond only enforcing controls, involves helping employees understand why those controls exist and how to use AI responsibly. As agents increasingly diagnose issues, recommend actions, and execute workflows with minimum input, human oversight and approval processes must scale alongside them.

Interoperability will be critical to making this workable at scale, allowing organizations to operate across jurisdictions and multiple AI models without repeatedly rebuilding governance processes and systems from scratch.

Making responsible adoption the easy choice

For security and compliance leaders, the goal should be to make responsible AI adoption the path of least resistance. Employees turn to shadow AI when approved tools are unavailable, difficult to access or fail to meet their needs. Companies that focus solely on restricting usage risk driving activity further underground and losing out on the efficiency and innovation gains that AI can deliver.

Organizations that successfully balance AI productivity and control over their systems recognize that shadow AI use is often a symptom of unmet demand. Employees typically turn to unauthorized tools because they are easier to access, faster to use or better suited to the task at hand. Rather than focusing on restrictions alone, leaders should understand where AI is already being used across the business and ensure approved alternatives are available for the most common use cases.

With three-quarters of office professionals saying they would be likely to look for a new job that offered better AI skills development, firms that combine governance with opportunities to build AI skills are likely to see stronger adoption of approved tools and, as a result, less reliance on shadow AI.

Building an AI-enabled culture means giving employees the tools, knowledge and confidence to innovate within clear boundaries. By doing so, shadow AI can be reduced while the speed and agility that workers increasingly expect is maintained.

The organizations best positioned to succeed

The businesses that strike the right balance for AI success will be those that view governance as a foundation for AI adoption and not a barrier to it. By making the secure, approved path the easiest path, shadow AI risk is reduced without sacrificing productivity.

Embedding strong governance, supported by trusted and well-managed data foundations, avoids costly mistakes and allows AI to be deployed and scaled with greater safety and confidence.

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Lanterns has finally premiered on HBO Max — and, in a move reminiscent of Invincible's first-ever episode on Prime Video, the new DC Universe (DCU) TV show wastes no time in delivering an almighty shock in its first chapter.

Indeed, the sci-fi show's premiere, titled 'Pilot', ends with a moment that'll completely stun viewers and likely upset DC comic book fanatics.

I'm about to dig into the biggest talking point from Lanterns' debut episode, so consider this your one and only warning: full spoilers immediately follow after the poll below. Turn back now if you haven't seen 'Pilot' yet, or else.

Is Kyle Chandler's Hal Jordan really dead in Lanterns?

Green Lantern fans and DC purists are going to have a lot to say about this... (Image credit: John Johnson/HBO Max)

It certainly seems that way. Of course, this could be a massive fake out — after all, there are all manner of shapeshifting alien species that can impersonate humans in DC Comics, so it's possible that this is the case here. Nonetheless, I believe that Chandler's Jordan has taken his last breath in the DCU.

Okay, but how did we get here? For starters, this season's premiere wasn't shy about teasing Jordan's demise. Indeed, minutes before his death, which takes place during the show's 2026 storyline, is shown, Jordan, Aaron Pierre's John Stewart, and Kelly Macdonald's Sheriff Kerry Kane survive an attack from a suicidal bomber at Rushville's police station.

This incident, which happens as part of Lanterns' 2016 storyline, is caused by an alien masquerading as a human truck driver called Waylon Sanders. As the unidentified extraterrestrial reveals, their skeleton is retrofitted with a biometric neutron device that they can activate with a single thought.

Hal Jordan interrogates Waylon Sanders, who the former correctly identifies as an alien (Image credit: John Johnson/HBO Max)

Prior to detonating the explosive gadget, Sanders goads Jordan over the latter's fearless nature, which Sanders interprets as Jordan wanting someone or something to kill him. In that respect, then, the DCU Chapter One series telegraphs his death before it happens.

Rather than bump off Jordan in 2016, though, Lanterns withholds his passing until a decade later. Indeed, Jordan uses his power ring to form a protective bubble around Sanders just before the assailant detonates his skeletal device, thereby restricting the blast radius and saving the lives of everyone nearby.

But, that's not the end of the story. Five minutes before 'Pilot' ends, we skip ahead to 2026, which reteams us with Stewart as he heads to the same college football field that he and Jordan first met Sheriff Kane, and where their 2016 investigation into the deaths of four football fans began.

There, Stewart reunites with Kane and, after a bit of small talk about Kane's now-teenage son Noah, they head to a section of the bleacher seating where Jordan's snow-covered corpse is eventually revealed to be sitting.

Who killed Hal Jordan in Lanterns episode 1? Assessing the most likely candidates

Anybody else react like this when they saw Hal Jordan's dead body? (Image credit: John Johnson/HBO Max)

Alright, so who murdered Earth's first-ever Green Lantern? We don't know, but we can speculate on who pulled the trigger.

The first two — and arguably most likely candidates — are Sheriff Kane and the stadium's groundskeeper. Kane tells Stewart that the latter is the only other individual who currently knows that Jordan is dead. However, it's incredibly unlikely that the groundskeeper will be a prominent character moving forward, so we can rule them out.

We can do likewise with Kane. Sure, she's got a firearm to hand at all times, but I just don't see her wanting to draw heat over Jordan's death, and the potential ire of Stewart, the Green Lantern Corps, and/or the Guardians of the Universe.

Did Jordan's murderer steal his ring, too? (Image credit: HBO Max)

So, who else could it be? Of the other characters we've met, I wouldn't be shocked if Garret Dillahunt's Will Macon is behind it, regardless of whether he actually committed the deed or not. Right now, though, he's formed something of an uneasy alliance with Jordan and Stewart, so a massive, relationship-breaking event would need to happen for Macon to be involved in Jordan's demise.

There are bound to be other would-be murderers who could've taken Jordan's life who we've yet to encounter, so we might be adding more names to our shortlist in the weeks ahead.

That said — and hear me out on this before you pass judgement — what if Jordan killed himself? He's a former special forces pilot, so he knows how to handle a gun. For all we know, Waylon was telling the truth about Jordan wanting is life to be over, too — especially if, as an alien, the former has some form of perceptive superpower that allows his species to read someone else.

Who do you think killed Hal Jordan? Let me know in the comment section below. And, for more on the HBO Max show, read my Lanterns review.

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Despite mammoth advanced in artificial intelligence and computer vision, and a slew of humanoid robots either being piloted or hitting parts of the market, there’s still one major challenge that machines need to overcome.

It’s tactility, sensation, knowing how to hold an object. Picking up a strawberry, for example, a robot might not know how much pressure to apply, whether it’s secure, slipping, or about to be crushed. It’s something that we humans can feel through our skin, but it’s much harder to replicate on a machine level.

But it’s a work in progress, because there are companies out there like Touchlab who are working to give these skin-like characteristics to robots in a bid to make them more useful around the home, in care or in manufacturing. And it’s a lot more complex than we could ever imagine.

In Touchlab’s instance, it actually relies on quantum tunnelling technology to measure pressure, force and direction through a material that’s thinner than human skin, and with response times of less than a millisecond.

E-skin technology exists, but deployment is a challenge

Although the solution is laid out on the table, moving from pilot to production is a different ball game. Durability, reliability, affordability, adaptability… they’re all considerations that stand between theory and real-world use cases.

Touchlab is addressing some of these concerns with off-the-shelf components that already exist for established platforms, using bespoke engineering only where necessary.

It’s also seeking to reduce latency by bringing processing to the edge so that the robot only transmits the data necessary for the task, being that the e-skin can detect and collect huge amounts of micro-data.

With robots now rolling out across workplaces, care environments and, likely soon, homes, the ability to sense contact is becoming as important as the ability to see.

I speak with Touchlab’s team to understand how we can go from pilot to production in a realistic way.

• Why do robots need the sense of touch, and how is Touchlab trying to achieve this?

Computer vision allows a robot to predict, but touch is what confirms. Without tactile feedback, robotics remains trapped in a classic paradox: a machine can calculate complex equations in milliseconds, yet struggle to pick up a strawberry, handle soft medical instruments, or turn a valve without either crushing the object or dropping it. Without tactile sensing, a robot is functionally numb.

Touchlab solves this by developing biomimetic electronic skin (e-skin) that is actually thinner than human skin. Powered by quantum tunneling technology, our e-skin measures 3D forces, direction, and pressure in real-time. Crucially, it detects incipient slip—meaning the robot senses that an object is beginning to slip before it actually drops, enabling instantaneous, sub-millisecond grip adjustments just like the human nervous system.

• I tried the technology at Future Lab... I tried to control a robotic arm with haptic feedback and it felt a bit clunky with cables and restrictions. Why is it so hard to make it smoother?

Teleoperation is a classic engineering balancing act! First, there is a natural learning curve—controlling a high-degree-of-freedom robotic arm through a haptic glove isn't always intuitive on the very first attempt. It usually takes a bit of practice time and muscle memory to get the hang of the subtle force dynamics.

Second, running live demonstrations at high-traffic public events like Future Lab introduces specific hardware constraints. To ensure hundreds of visitors with completely different hand sizes can safely participate, setup requires universal-fit, heavy-duty tethered rigs and robust cabling.

Longer-term, our roadmap moves away from bulky, tethered operator suits entirely. By embedding our e-skin directly onto the robot and pairing it with onboard AI, the robot handles slip detection and dexterity autonomously—removing the need for an operator to wear restrictive physical gear.

• Right now, the focus is on your proprietary robot fingertip tech. What's the plan going forward? Do you plan to license it or sell it as a finished product? How will you deal with various robotic architectures?

Every robot platform is unique—whether it’s a humanoid deployed into hazardous industrial environments or a cobot assisting nurses in hospital wards. Touchlab is uniquely positioned to design, develop, and manufacture bespoke tactile devices tailored to specific OEM architectures.

At the same time, we offer scalable, off-the-shelf tactile components that are pre-integrated (both electronically and mechanically) with a wide variety of popular robot end-effectors. We provide volume-based discounts alongside strategic hardware loan and collaboration programs for robotics companies aligned with our vision. Over the past seven years, we have become extremely adept at rapidly building reliable integrations to suit diverse customer requirements.

• What are currently the biggest technological barriers Touchlab is encountering? What does the end game look like, and will we ever digitize our largest organ, the skin?

Our ultimate end game is for Touchlab’s tactile technology to become as ubiquitous as MEMS sensors— integrated into every commercial robot interacting with objects and humans. We envision full "finger-to-toe" tactile coverage powered by specialized edge computing and tactile orchestration software that mirrors the human somatosensory system.

Regarding barriers: the challenge isn't just technical; it's navigating western market adoption timelines for deeply transformative hardware. While digitizing human skin at scale presents manufacturing and durability hurdles, Touchlab can already deliver sensors matching or exceeding human-level spatial resolution, readout frequency, and sensitivity. The real task is packaging this capability into rugged, cost-effective formats ready for mass market deployment.

• How is Touchlab designing the user interface to reduce operator fatigue during long shifts?

We focus on shared autonomy. Rather than forcing a human operator to manually sense and adjust every micro-force vector, our e-skin handles low-level reflexes (like slip prevention) locally on the robot. The human operator directs higher-level tasks, while the robot's tactile system autonomously maintains optimal grip— dramatically reducing both physical strain and cognitive fatigue.

• High-density e-skin generates massive amounts of data. How do you handle bandwidth and network transfer without overloading systems?

High-density sensing does not automatically equal richer functional data, and often comes at the cost of hardware robustness. For our commercial products, we optimize spatial and temporal sensor density specifically for manipulation tasks, keeping data payloads lightweight.

For advanced R&D, we utilize distributed edge computing and are pioneering event-based asynchronous sampling in tactile sensing (similar to event-based vision cameras). Because our e-skin does not rely on heavy image-based data streams, it operates with extremely low latency and modest network bandwidth— making long-distance remote teleoperation surprisingly efficient.

• Because e-skin can identify objects and textures, what protocols protect tactile data security and privacy?

Tactile data on its own does not contain personally identifiable information. However, when integrated with video, audio, and LiDAR streams, multi-modal data security becomes critical.

For example, during our deployment with the Valkky medical robot in a Finnish hospital, all data transferred across hospital networks was strictly encrypted. Identifiable multi-modal data remained entirely on-premises on air-gapped storage devices and was fully anonymized before being processed for research or system analytics.

• E-skin is often seen as a high-end luxury. What is your roadmap for driving down production costs for commercial robotics?

Much like the early days of automobiles, groundbreaking technology often enters the market at a premium before scaling. While Touchlab is capable of producing full human-spec density today, deploying that level of complexity everywhere would make commercial robots cost-prohibitive.

Our strategy focuses on pragmatic engineering: delivering touch sensing tuned precisely to real-world task requirements at an accessible price point. We are actively collaborating with industry bodies to establish standardized performance metrics for tactile data, driving down manufacturing costs and making e-skin a standard baseline for all commercial robotics.

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Last month, the UK government announced that plug-in solar panels would become legal to buy from August 27. Now, new planning rules have passed into law that specify exactly where you can — and can't — install them.

Before we get into the details, it's important to understand that these rules apply to "permitted development" – changes you can make to your home without needing to apply planning permission.

If your solar panels don't meet the permitted development rules, you might still be able to install them, but you'll need to apply for planning permission first and pay the relevant fee. A planning officer will then do a survey and determine whether or not you can go ahead.

If you install solar panels without the required permission, you could be fined and forced to take them down. Your local council's website will explain how to apply for permission, while the company supplying your plug-in panels should also be able to offer advice on the process.

"Plug-in solar has the potential to make renewable energy accessible to many more households, but it’s important that consumers have clear information about how and where these systems can be installed," explained a spokesperson for EcoFlow, which specialises in home power stations, solar generators, and panels.

"As a manufacturer, EcoFlow recognises its responsibility to provide customers with the information they need to install and use its products in line with relevant UK requirements, and we will ensure our guidance reflects the regulations in place."

With that in mind, here are three locations where plug-in solar panels aren't allowed without planning permission.

1. On anything wooden

(Image credit: Generated by Google Gemini)

Before you start planning to install solar panels on your balcony, check what it's made from. If you live in a block of flats, plug-in solar systems don't qualify as "permitted development" if the panels or any other part of the system would be installed on a wooden balcony, wall, or any part of the building's exterior that's clad in timber.

If you live in a house, you can't install plug-in solar panels if any part of the system would be mounted on a wooden gate, fence, wall, or other timber surface. This is to avoid the risk of fire.

2. Too high above your roof

Generated with Google Gemini AI

Generated with Google Gemini AI

There are also rules about where you can position solar panels on your roof, similar to those that apply to skylights. If you have a pitched roof (one that slopes downwards), the panels and equipment must not project more than 20cm beyond the roof slope, measured at right angles to it.

If you have a flat roof, the highest part of your solar panels and equipment shouldn't sit any more than 60cm above the highest part of the roof — although this doesn't include your chimney.

There are plenty of practical reasons for limiting how far out your panels project. They could alter the roofline, cast shadows over neighbours' houses, or catch more wind, which would put your roof under extra stress.

3. Sticking out too far from your wall

Generated with Google Gemini AI

Generated with Google Gemini AI

If you're mounting solar equipment on the wall of your house, it shouldn't project more than 40cm when measured perpendicular to the outside of the wall.

The rules are stricter if your house is located right next to a highway. If your home is right beside a highway, your solar equipment must not protrude more than 20cm from the wall. In planning terms, the word "highway" means any route that people can use to pass through, whether on foot, by bike, on horseback or in a vehicle. This includes roads, footpaths, bridleways and cycle paths.

If you live an a conservation area, or a World Heritage Site, you shouldn't have solar panels on walls next to highways at all without planning permission.

Where to get advice

There are also other factors to consider before installing a plug-in solar setup. For example, if you rent your home, you'll need permission from your landlord. And if you live in a flat, you might also need permission from the building's management company.

"Planning requirements can also depend on the individual property and how and where panels are installed," says EcoFlow's spokesperson. "We would therefore encourage customers to follow the guidance provided with their system, check the latest government and local planning requirements, and contact their local planning authority where they are unsure whether planning permission is required."

At EcoFlow, we believe accessibility and safety need to go hand in hand. Making solar available to more households is an important step forward, but it needs to be supported by clear guidance, appropriate regulation, and responsible installation so consumers can adopt the technology with confidence."

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Want to make delicious lattes and cappuccinos at home without using a steam wand? You need an electric milk frother — and as TechRadar's resident barista, I've put together the top three I recommend, whether you enjoy plant milk or dairy.

Before we get started, I should note that electric milk frothers and steam wands both have their pros and cons. A manual steam wand works much more quickly than an electric milk frother, and once you've got your eye in, it allows you greater control over the texture of the milk (more creamy for lattes, and 'drier' for a cappuccino, for example).

Electric frothers, however, can usually whip up cold milk foam as well as hot, and make it easy to prepare other drinks including hot chocolate and protein shakes. The inside of an electric milk frother usually has a non-stick coating, making it a breeze to clean. An electric frother is also a great option if you have limited mobility, often featuring just one or two physical buttons that can be operated with one hand. Sounds good? On with the recommendations...

Dreo Baristamaker

Future

Future

Future

Future

Future

Future

Future

If you enjoy plant-based milk, the Dreo Baristamaker is the electric milk frother for you. While the other two in this guide are optimized for dairy, the Baristamaker also has settings for soya, almond, coconut, and oat — as well as half-and-half if you want something richer in your coffee.

The base of the unit contains the heating element and motor, and the two whisking attachments are held in position (and spun) using magnets. One attachment looks like your standard milk whisk, while the other has large blades to pull the milk in towards the center, where a mesh screen breaks up large bubbles and turns them into fine foam.

During the foaming process, the Baristamaker will alternate between stirring and foaming, which is intended to create an even texture. It's also the only electric milk frother here whose pitcher has a fine spout, which is essential for creating latte art, though in my tests I found that the foam was less smooth than milk steamed the old-fashioned way, and I couldn't achieve the same results when pouring it.

Nevertheless, for sheer versatility, the Baristamaker is my number one pick if you're looking for an electric milk frother, provided your budget can take the hit. If you're looking for something a little more affordable (or just smaller), read on for two more options.

Read our full Dreo Baristamaker review

Smeg Mini Milk Frother

Future

Future

Future

Future

Future

Future

Future

The Smeg Mini Milk Frother is an ideal companion to the Lavazza A Modo Mio Smeg capsule coffee machine, but its clean, minimalist design would look right at home anywhere (particularly since it comes in a wide array of colors, including black, white, and pastels).

Its controls are extremely simple: just one button on the front, which you press once, twice, or three times to choose your preferred setting (hot milk foam, hot milk, or cold milk foam). There are no options for different milk types, and according to Smeg, it's intended to be used with chilled full-fat dairy milk, which it turns into quite a thick foam. I also tried it with barista-style oat and almond milk to see how it compared with the Baristamaker; oat milk produced similar results to dairy, while almond was 'drier' and less creamy.

Whether I used the hot milk or hot milk foam option, the milk was always heated to exactly 140F / 60C, which is optimal for coffee. However, it's a shame the milk vessel doesn't have a fine spout like the Baristamaker; the milk pours cleanly without drips, but the lack of fine control combined with the thick texture means it's impossible to create latte art.

Read our full Smeg Mini Milk Frother review

Future / Max Langridge

Future / Max Langridge

Future / Max Langridge

Future / Max Langridge

Future / Max Langridge

Future / Max Langridge

Philips Baristina Milk Frother

This smart-looking electric milk frother is available on its own, or bundled with the Philips Baristina coffee machine — one of my all-time favorite espresso machines. When my colleague Max Langridge tested this frother, he found it "consistently produces thick milk foam, whether hot or cold", but as with all the devices we've tried, the foam it produces tends to be on the stiffer side and not ideal for pouring latte art.

Unlike the other frothers here, the Baristina doesn't use a heating element underneath the pitcher to heat your milk. Instead, it has a heated coil, which needs to be removed first if you want your milk cold. There's no audible beep to tell you that your milk is ready, but there is an LED indicator, and at just 40dB the while heating and whisking process is very quiet.

There are no options for tweaking the texture of your finished froth, but unlike the Smeg Mini Milk Frother above, Philips says that the Baristina frother is designed to be used with all types of milk — plant and dairy. However, Max noticed that whatever type of milk he used, the foam was always on the thicker side, and like the Smeg frother, there's no spout for pouring patterns onto your coffee.

If you're considering the Baristina coffee maker and milk frother bundle, you should also be aware that Philips has recently launched the Baristina Latte, which bundles both into one compact package. I've not had the opportunity to test it yet, but I have high expectations.

Read our full Philips Baristina Milk Frother review

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• Cincinnati Open 2026: Thursday, August 13 to Sunday, August 23
• Daily start time: 11am ET / 8am PT / 4pm BST / 1am AEST
• US streams: Tennis Channel
• Access your usual streaming services with Proton VPN

Watch Cincinnati Open 2026 live streams as the world's best players gather at the 127-year-old tournament to continue their preparation for the year's fourth and final Grand Slam – the US Open – which is only a few weeks away. The Lindner Family Tennis Center in Mason, Ohio, again hosts.

Both draws are wide open. Carlos Alcaraz and Iga Świątek may be the reigning men's and women's singles champions respectively, but the Spaniard remains injured and the Pole's has failed to make a grand slam semi-final this year – Świątek, though, did win her first title of the season last week in Toronto, a WTA 1000 event, no less.

Elsewhere on the men's side, world No.1 Jannik Sinner will also miss Cincinnati through injury, meaning French Open champ and 2021 winner Alexander Zverev is promoted to top seed. Although in his 40th year, three-time champion Novak Djokovic will be one of the biggest threats, with Americans Taylor Fritz, Ben Shelton – who won last week's Canadian Open – and Learner Tien all hoping to do well. Keep an eye out for fast-rising teen Rafael Jódar, too, and Wimbledon wild card semi-finalist Arthur Fery.

There's also a strong US contingent on the women's side, with Jessica Pegula, Coco Gauff and Amanda Anisimova all among the top 10 seeds. Naturally, world No.1 Aryna Sabalenka, reigning Australian Open champion Elena Rybakina and freshly crowned Wimbledon winner Linda Nosková will be their biggest rivals here.

Here's how to watch Cincinnati Open 2026 tennis from anywhere, including worldwide TV channels, broadcasters and a full list of the tournament's seeds, schedule and daily start times.

Can I watch Cincinnati Open 2026 for FREE

In the United States, the Tennis Channel is the exclusive home of Cincinnati Open 2026, which can be accessed directly or via 'over the top' streaming providers that offer free trials, for example YouTube TV (5 days free) or Fubo (7 days).

It has also been confirmed that there will be select matches shown for free on Tennis Channel 2.

Australians can get a free trial too, thanks to beIN Sports (7 days).

Traveling outside your home country for the tournament? Use Proton VPN to get past geo-blockers and tune in to your regular tennis live streams.

Use a VPN to watch any Cincinnati Open 2026 stream

The Cincinnati Open 2026 is being streamed all over the world, but what if you are outside your usual country and can't watch your home stream?

Don't worry – this is where a VPN comes in very handy. The best VPNs allow you to appear as though you're still at home regardless of where you actually are in the world, meaning you don't have to miss out because of geo-restrictions. Proton VPN is one of the providers we most recommend:

🟩 Proton VPN from only $2.99 a month (normally $3.59

TechRadar regularly reviews all the biggest and best VPN providers and Proton VPN is among the top options for streaming.

✅ Up to 70% Off Today
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Get Proton VPN and stream the Cincinnati Open online from anywhere.View Deal

It's really straightforward to use a VPN to watch the Cincinnati Open 2026.

1. Install the VPN of your choice. As we've said, Proton VPN is one of our favorites.

2. Choose the location you wish to connect to in the VPN app. For example, if you want to watch a US stream, select 'United States' from the listed countries.

3. Sit back and enjoy the action. Head to your usual streaming service and tune into Cincinnati Open 2026 as normal.

How to watch Cincinnati Open 2026 live streams in the US

In the US, comprehensive coverage of the 2026 Cincinnati Open is being shown on the Tennis Channel, which appears on a wide range of cable plans.

It has also been confirmed that some matches will also be shown absolutely free on Tennis Channel 2.

Cord cutters can get a dedicated online Tennis Channel subscription that costs $109.99 per year or $11.99 per month. New subscribers can get their first year for $77 for a limited time with code HOT77. And the free Tennis Channel 2 streams can be watched without even needing to log in.

Looking for an OTT cable replacement option that carries hundreds of other channels? The Tennis Channel is available on Sling TV (with its Sport Extra add-on), YouTube TV (with another sport add-on) or Fubo, the latter two of which come with free trials for new users.

Outside the US for this tournament? Use Proton VPN to unlock your usual stream of Cincinnati Open 2026.

How to watch Cincinnati Open 2026 live streams in Canada

Tennis fans in Canada can live stream the Cincinnati Open 2026 on the TSN network of channels.

If you don't have cable, the TSN Plus streaming service costs CA$8 a month or $80 each year.

Outside Canada while the Cincinnati Open is on? Simply use a VPN to watch from abroad.

How to watch Cincinnati Open 2026 live streams in the UK

Sky Sports is broadcasting the Cincinnati Open 2026 in the UK.

Prices currently start from £22 a month for existing customers. However, tennis fans can also watch using a NOW Sports Membership, which costs from £14.99 for a one-day pass.

Not in the UK right now? Use Proton VPN to access your usual tennis streams.

How to watch Cincinnati Open 2026 live streams in Australia

In Australia, the Cincinnati Open 2026 is exclusive to beIN Sports, which offers new users a 7-day FREE trial.

You can add beIN Sports to most pre-existing TV packages, or you can sign up as a separate subscription. It costs AU$15.99 month or AU$130 if you pay for a year up front, once that week-long trial ends.

As part of a sub-licensing agreement with beIN, the women's WTA event is also available on Stan Sport. You'll need a Stan Sport add-on for AU$20 in addition to a Basic subscription that costs from AU$9.99.

Not in Australia right now? You can simply use a VPN like Proton VPN to watch all the action on beIN Sports as if you were back home.

Who are the Cincinnati Open 2026 seeds?

Men

1. Alexander Zverev
2. Félix Auger-Aliassime
3. Novak Djokovic
4. Daniil Medvedev
5. Alex de Minaur
6. Taylor Fritz
7. Flavio Cobolli
8. Ben Shelton
9. Jiří Lehečka
10. Lorenzo Musetti
11. Casper Ruud
12. Rafael Jódar
13. Andrey Rublev
14. Jakub Menšík
15. Valentin Vacherot
16. Learner Tien
17. Frances Tiafoe
18. Tommy Paul
19. Luciano Darderi
20. Francisco Cerúndolo
21. Arthur Fils
22. Alejandro Tabilo
23. João Fonseca
24. Ugo Humbert
25. Arthur Rinderknech
26. Tomás Martín Etcheverry
27. Brandon Nakashima
28. Alexander Blockx
29. Ignacio Buse
30. Zizou Bergs
31. Matteo Arnaldi
32. Arthur Fery

Women

1. Aryna Sabalenka
2. Elena Rybakina
3. Jessica Pegula
4. Coco Gauff
5. Mirra Andreeva
6. Linda Nosková
7. Iga Świątek
8. Elina Svitolina
9. Amanda Anisimova
10. Marta Kostyuk
11. Naomi Osaka
12. Belinda Bencic
13. Iva Jovic
14. Diana Shnaider
15. Sorana Cîrstea
16. Ekaterina Alexandrova
17. Alexandra Eala
18. Anna Kalinskaya
19. Maja Chwalińska
20. Madison Keys
21. Elise Mertens
22. Marie Bouzková
23. Barbora Krejčiková
24. Anastasia Potapova
25. Emma Navarro
26. Jeļena Ostapenko
27. Clara Tauson
28. Ann Li
29. Maria Sakkari
30. Leylah Fernandez
31. Donna Vekić
32. Janice Tjen
33. Kateřina Siniaková

What is the Cincinnati Open 2026 schedule?

Thursday, August 13
First Round

Friday, August 14
First Round

Saturday, August 15
Second Round

Sunday, August 16
Second Round

Monday, August 17
Third Round; Doubles First Round

Tuesday, August 18
Third Round; Men's Doubles First Round; Women's Doubles Second Round

Wednesday, August 19
Round of 16; Doubles Second Round

Thursday, August 20
Quarter-finals; Men's Doubles Second Round; Women's Doubles Quarter-finals

Friday, August 21
Quarter-finals; Men's Doubles Quarter-finals; Women's Doubles Semi-finals

Saturday, August 22
Semi-finals; Women's Doubles Final

Sunday, August 23
Men's Doubles Final; Singles Finals

What are the daily Cincinnati Open 2026 start times?

Day Session:
11am ET / 8am PT / 4pm BST / 1am AEST (next day)

Night Session:
6pm ET / 3pm PT / 11pm BST / 10am AEST (next day)

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• Study finds 25% of business leaders struggle to explain AI-generated outputs to stakeholders
• SMEs are trusting AI with complex financial tasks including audits and compliance
• Customers and investors are increasingly avoiding businesses that use unverified AI

Business leaders are being outfoxed by AI, with a quarter of those surveyed apparently unable to explain AI generated outputs, 12% “with a lot of difficulty” and only 22% “easily.”

Even more worryingly, the survey, by Startup.co.uk, appears to reveal a culture of “leave it to AI” regardless of the consequences.

The survey has also uncovered other concerns, from questions over GDPR to how SMEs are trusting AI to carry out sensitive financial work, including audits, expenses, and accounts payable automation.

Would you let your AI do this?

With GDPR responsibilities hovering over startups and dynamic new SMBs, the overuse of AI solutions could prove to be devastating if customer data is found to have been misused. Meanwhile concerns over the use of the technology could be affecting businesses that fail to verify AI work.

While the inability of startup leaders to explain AI generated work might be a surprise, the depth of AI use across financial tasks is of particular concern. The report revealed how 85% of small businesses are using AI to complete financial tasks of some sensitivity.

Among the figures are 37% using AI to automate accounts payable processes, 32% to handle audit and compliance, and 31% to manage spend and expenses. While there is some suitability for AI with fraud detection (26%) and performance insights (27%) with its capacity to analyze data in bulk, the inability of leaders to deal with questions about AI is a matter of concern.

Blind trust in AI

What the figures seem to indicate is an over-reliance on AI.

Zohra Huda, editor of Startups.co.uk, noted the data “highlights the bizarre corporate milestone we’ve reached in 2026. Founders are letting AI manage their fraud detection and accounting, but if a stakeholder asks how the numbers were calculated, a quarter of them can’t answer.”

The solution to that might be a bit of rehearsal, but it doesn’t change how data is being used by AI and what the implications for this are, and the likelihood of breaching Article 15 of the UK GDPR (concerning how data is processed).

“Blindly trusting a tech “black box” with sensitive financial data is a massive legal and compliance gamble," added Huda. The takeaway for business leaders? If you can’t explain your AI’s logic to an investor or a customer, you're potentially risking a very expensive GDPR fine.”

With the ICO able to fine businesses up to £17.5 million or 4% of global turnover for verified GDPR breaches, it is clear that startups relying heavily on AI need to up their game.

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