News

• Amazon has rolled out tougher DRM to the Kindle Oasis
• The same update has previously landed on most other Kindle models
• The company has also closed an airplane mode loophole for Libby loans

If you like reading Kindle books on other devices or borrowing digital library books using Libby, Amazon has just made some changes that you’re unlikely to be happy about.

First up, as reported by The eBook Reader, a software update has rolled out for the Kindle Oasis that adds a new hardened DRM (Digital Rights Management) to books on it, meaning that most DRM-removal tools will no longer work.

While we don’t condone piracy, there are legitimate reasons to want to remove DRM, such as reading the book on a non-Amazon ereader that doesn’t have access to the Kindle app.

And this isn’t the first or only Kindle to get this undesirable update — now, the only supported model that doesn’t have this new DRM is the Kindle Voyage, and it’s probably only a matter of time before that gets it too.

The other change that Kindle users are unhappy with is that you can seemingly no longer keep a Libby book beyond its due date by turning airplane mode on. This was a handy workaround that gave you more time with a borrowed book, but now Reddit users report being prompted to turn Wi-Fi on to access their loans.

As these are digital loans, this workaround wasn’t really hurting anyone — the books would still be made available to other users after the loan period expired anyway; you’d just also still have a copy on your Kindle.

Unsurprisingly, the online reactions to these changes aren’t positive, with Reddit comments about the new DRM including the likes of “glad I switched to Kobo,” while of the Libby change, one commenter claimed it was “my biggest nightmare.”

If you’re affected by either of these issues, then there aren’t any easy solutions — other than potentially switching to another brand. In the case of the DRM, Kobo books currently use a version that’s easier to remove, but even on Kobo, the airplane mode loophole won’t work.

Motorways don't stay safe simply because drivers follow the Highway Code. They rely on speed limits, lane markings, and crash barriers that keep traffic moving to help prevent mistakes from becoming serious accidents.

AI governance works the same way. Policies define the rules, but operational controls keep organizations on course when technology, users, and risks change.

As organizations accelerate AI adoption, many have focused on developing policies, governance frameworks, and acceptable use guidelines. Those are essential first steps. But policies alone can't prevent AI systems from accessing the wrong data, interacting with the wrong applications, or creating unintended operational risks.

The EU AI Act has made AI governance a board-level issue, requiring organizations that use or develop AI to identify, manage, and document risks, particularly for high-risk systems.

This has introduced new expectations around oversight, documentation, data governance, and transparency. However, regulation alone won't solve the biggest practical challenge: AI adoption is moving faster than governance.

Employees are using public AI tools, developers are relying on coding assistants, and business teams are deploying AI capabilities across existing platforms. In many cases, these technologies are introduced before security, compliance, and risk teams fully understand what they can access, which systems they influence, or how they change the organization's overall risk profile.

That's the gap between regulatory ambition and operational reality. Organizations may have approved AI use cases and governance frameworks, but they're often governing only part of the AI already operating across the business.

Closing that gap starts with treating AI as an operational challenge rather than simply a compliance exercise. In practice, moving beyond policy means understanding how AI is used, applying controls according to business risk, and adapting governance as AI capabilities evolve.

Traditional governance assumes organizations know what technologies are operating across the business. AI has changed that. Shadow AI is no longer limited to employees experimenting with public tools.

It also includes embedded AI capabilities introduced through routine software updates, AI-powered workflows created by business users, and autonomous agents operating across enterprise systems without ever being treated as formal AI projects. These capabilities are often adopted faster than governance processes can identify, assess, and manage them.

Today, AI isn't simply supporting employees - it's acting on their behalf. AI agents can retrieve information, trigger workflows, interact with enterprise systems, and make operational decisions using permissions inherited from the people or systems they represent.

Because of this, the enterprise attack surface has fundamentally changed. Security teams are no longer protecting only people, devices, and applications - they're also securing autonomous systems capable of moving across environments, accessing sensitive information, interacting with critical workflows, and making decisions independently.

Every new AI agent represents another potential pathway for accidental misuse or malicious activity if it isn't governed appropriately.

The EU AI Act places significant emphasis on accountability, oversight, and risk management throughout the AI lifecycle. But meeting those expectations requires far more than documentation.

Regulation defines the outcomes organizations must achieve; the challenge is translating those requirements into operational governance across environments where AI is constantly evolving.

That starts with understanding AI in context. Risk can't be assessed by looking at an AI system in isolation. An AI assistant summarizing internal documents may appear relatively low risk until it gains access to commercially sensitive information.

Likewise, an AI agent automating procurement may operate safely under normal circumstances, but its risk profile changes significantly if it can approve financial transactions, inherit privileged identities, or initiate actions across critical business systems.

Risk is defined not only by the AI itself, but by the identities, applications, infrastructure, data, and business processes it connects to. As it becomes more deeply embedded across these interconnected systems, AI governance increasingly overlaps with cybersecurity.

Every AI capability introduced into the enterprise can create new attack paths, expand access to sensitive information, and increase the number of non-human identities operating across the environment.

Recent incidents, such as PocketOS’s database being wiped by an AI coding agent in seconds, have shown how quickly AI risk can become operational risk when AI systems are granted more authority than intended.

Organizations should therefore govern AI identities and permissions with the same rigor as human users, regularly reviewing inherited access and removing unnecessary privileges before they create new attack paths.

Governance also needs to extend beyond individual AI systems. Organizations should understand how AI interacts with identities, enterprise assets, business processes, and critical data so they can prioritize controls where they'll have the greatest impact.

Bringing security, risk, compliance, and technology teams together around this shared understanding turns governance into an enabler of resilience rather than simply a compliance exercise.

As AI regulation continues to evolve, organizations will be expected to demonstrate that governance exists not only on paper, but in practice. Policies establish the rules, but operational governance builds the rails that keep AI operating safely as technologies evolve, permissions change, and new capabilities emerge.

Moving beyond policy means embedding governance into day-to-day operations. Organizations should continuously identify new AI capabilities, understand how they relate to enterprise assets, review the permissions they inherit, assess business impact, and prioritize the exposures that present the greatest risk.

This allows teams to apply controls where they'll have the greatest impact while supporting responsible AI adoption across the business.

Governance also needs to keep pace with change. As AI agents evolve and environments shift, organizations should be able to adjust permissions, restrict access, isolate affected agents, or prevent actions that exceed an AI system's intended role before they become wider operational issues.

The objective isn't to slow AI adoption, but to create the confidence to innovate securely as AI becomes part of everyday business.

AI will continue to transform how organizations operate, innovate, and compete. But organizations that realize AI's full potential won't be those with the longest policy documents.

They'll be the ones that embed governance into everyday operations, govern AI identities and permissions with the same discipline as any other critical asset, and continuously adapt controls as technology evolves.

The EU AI Act has raised expectations around accountability, but organizations that move beyond policy and compliance will be best placed to unlock AI’s full potential.

In the age of autonomous AI, governance isn't just about setting the rules - it's about creating the guardrails that let organizations innovate with confidence without compromising security, trust, or resilience.

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Every year, marketing teams are expected to deliver more. Marketing leaders are under growing pressure to prove the return on investment they deliver and demonstrate the value they bring to the business.

At the same time, budgets remain tight, sales cycles are getting longer, and more people are becoming involved in buying decisions.

Against that backdrop, many organizations have turned to AI to automate routine tasks, analyze market and audience data and support content creation.

But as adoption matures, the conversation is now moving from experimentation to execution.

According to recent UK government research, whilst 41% of businesses are now using AI technology, only 21% have integrated AI into their existing business systems, showing that while there’s been real progress, we're still in the early stages.

The next challenge is not over-use, but shallow use. That means moving beyond isolated use cases and embedding AI into everyday marketing workflows, equipping people with the skills to use it effectively and focusing on the commercial outcomes it helps deliver.

An important part of this is ensuring humans remain at the core. Ultimately, AI should enhance human capability, not the other way around. This is particularly important when it comes to the creativity, storytelling and decision-making at the heart of marketing.

The shift from AI experimentation to execution requires organizations to look beyond familiar use cases and think more strategically about where AI can create commercial value.

Rather than treating AI as a standalone capability to simply improve efficiency, marketers should be looking at where it can support existing workflows across the entire marketing function - from campaign planning and content creation through to new lead generation, sales enablement, measurement and reporting.

One of the biggest barriers is that people tend to rely on what they already know. That's why ongoing training and development are so important. Helping marketing teams understand where AI genuinely adds value and giving them the confidence to apply it in new ways are what will allow organizations to move beyond isolated use cases and deliver stronger commercial outcomes.

As AI becomes embedded across the marketing function, success will depend less on adopting the technology itself and more on how effectively teams apply it to core business objectives. While use cases continue to evolve, marketing leaders should focus on three priorities to deliver the greatest impact.

First, AI should drive efficiency with a goal to shorten sales cycles. It should be viewed as a tool that removes friction from the customer journey to accelerate the flow of opportunities through the funnel.

Beyond just task automation, marketing teams should be focusing on how their use of AI is delivering faster decision-making, more relevant customer engagement and stronger alignment between marketing and sales - ultimately helping organizations reduce sales cycle length and bring revenue forward.

Second, as AI-powered search experiences increasingly influence how buyers discover information, producing more content is no longer enough. Brands must focus on creating high-quality, differentiated and authoritative content that AI systems are more likely to surface and reference. This means going a step further than using prompts in chatbots and using AI to uncover audience insights, scale ideation and optimize formats.

Finally, marketing teams should leverage AI’s ability to collect actionable insights from large volumes of data and predictive analytics into emerging trends to streamline reporting and measurement processes. Automated dashboards and lead scoring can provide near real-time visibility into performance, enabling leaders to make faster, more informed decisions and keep teams aligned against shared objectives.

In an environment where budgets are under constant scrutiny, robust measurement frameworks will be critical to demonstrating marketing's contribution to business outcomes.

It is important to remember that human creativity remains essential for developing original perspectives, compelling storytelling and genuine brand distinction. Organizations that combine AI efficiency with human expertise will be best positioned to maintain visibility and relevance in an increasingly AI-mediated digital landscape.

This starts with ongoing training and development, and creating an environment where employees feel confident experimenting, sharing knowledge and executing new capabilities. As AI becomes embedded across the entire workflow, strong leadership and collaboration across marketing, sales and the wider business will be just as important as the technology itself, helping teams apply AI consistently, securely and strategically.

The organizations seeing the greatest success will be those that build AI around their people, rather than expecting people to adapt to AI. When organizations combine the right technology with ongoing training, cross-functional collaboration and clear governance, AI becomes much more than an efficiency tool. It becomes something that enables employees to apply greater creativity, judgement and commercial thinking, while ensuring implementation remains responsible and aligned to broader business goals.

The conversation around AI has moved past experimentation and is now about getting the balance right - moving beyond shallow, task-based use of AI and embedding it strategically across marketing in ways that deliver measurable commercial value, without overusing it or losing the human qualities that customers value most.

Looking ahead, UK Government research shows that 59% of organizations planning further AI investment intend to embed AI into their existing tools and systems over the next 12 months, while 42% also plan to invest in workforce reskilling and development.

Ultimately, the organizations that pull ahead won't necessarily be those investing in the most technology, but those that invest in their people. This means helping teams understand where AI genuinely adds value, and ensuring it compliments commercial thinking, continuous learning and human creativity, rather than replaces it.

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This article was produced as part of TechRadar Pro Perspectives, our channel to feature the best and brightest minds in the technology industry today.

The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you are interested in contributing find out more here: https://www.techradar.com/pro/perspectives-how-to-submit