Data is a company’s most valuable asset. So, doing everything in your power to protect that asset is a given. But what if the threat you’re guarding your data against is known to cripple operations, tarnish reputations, and drain finances? And even worse, what if that threat is only getting more dangerous, thanks to a little thing called artificial intelligence (AI)?
Unfortunately, for many businesses, there’s nothing “what if” about this scenario. As many as 72% of businesses worldwide have experienced a ransomware attack at some point and know just how devastating the aftermath can be.
That’s why we tapped two cybersecurity experts to share their insights on ransomware, its evolution, and how businesses can protect themselves. Read on to hear what Tom Vazdar, the chair of the Enterprise Cybersecurity Master’s program at the Open Institute of Technology (OPIT), and Venicia Solomons, a seasoned cybersecurity architect, have to say on this topic in their “Cyber Threat Landscape 2024: Navigating New Risks” master class.
Ransomware: The Basics
Ransomware is nothing new. However, there are always new business owners who (luckily) haven’t encountered it yet. So, let’s cover the basics first.
Ransomware is a natural product of phishing, a human-centric cyber threat that relies on social engineering to deceive individuals into providing sensitive information or downloading malicious attachments. The latter is what ultimately triggers a ransomware infection. Tom describes the process like this:
You click on a malicious link.
Your device downloads the malware.
Your system is now infected, and somebody else is essentially in charge.
They encrypt your data and demand you pay ransom for the encryption key to get it back.
As mentioned, dealing with ransomware attacks and cyber criminals has become a daily reality for companies worldwide. What certainly doesn’t help companies is the fact that ransomware is now also offered as a service.
Ransomware as a Service
Just a few short years ago, cybercriminals needed sophisticated technical skills and tools to develop and deploy ransomware. Now, all they need is access to the dark web.
As Tom explains it, numerous cyber criminals on the dark web offer ransomware as a service, a malicious adaptation of the software as a service (SaaS) business model. So, you essentially pay them to deploy their ransomware on your behalf.
The most famous, or should we say infamous, among these threats is the LockBit model, which has wreaked havoc on thousands of companies worldwide. The issue is that LockBit ransomware attacks vary in tactics, techniques, and procedures. In other words, an organization must be prepared for virtually anything.
How Has AI Affected Ransomware?
Ransomware is dangerous on its own. But throw artificial intelligence into the mix, and you’ve got a massive threat on your hands.
AI has undoubtedly revolutionized the cybersecurity industry, for better or for worse. The “worse” part is that AI is making cyber threats smarter. Unfortunately, for organizations, this particularly applies to ransomware. According to a 2024 report by the U.K.’s top intelligence agency, ransomware stands to gain the most from AI.
How so?
Well, AI has the potential to create malware that circumvents current cybersecurity detection measures. After all, AI is trained using data. Give it malware data to analyze, and it will learn how to evade detection by traditional cybersecurity tools.
AI will also likely generate a surge of new cybercriminals as the barrier to entering into cybercrime decreases with AI-powered tools.
Of course, the more capable and experienced attackers will also benefit from AI. They will use it to identify system vulnerabilities, bypass security defenses, and craft more precise social engineering attacks.
How to Prevent Ransomware Attacks
Given how quickly ransomware is evolving, preventing attacks requires a multi-faceted approach that combines technology, education, and proactive measures. Tom and Venicia break down this approach.
1. Keep Your Systems Updated
When it comes to anything cybersecurity-related, this is the first crucial step. Keep all your systems and programs updated and patched if you want to stand any chance of protecting against known vulnerabilities.
Tom says that there’s a new vulnerability “basically each week,” so having a process in place to update regularly and patch systems is essential.
Venicia adds that something as simple as a basic software update can go a long way toward protecting your data from ransomware. This update will limit its ability to spread through your network, thus reducing the impact of the attack.
2. Invest in Quality Training
Having the most advanced protection systems in place will do you no good if you don’t have well-trained employees.
These employees must learn to recognize potential cyberattacks that could introduce malware into your organization’s system (e.g., phishing emails). Of course, the next step is to respond effectively to the attack. Though each organization has its own set of rules in place, the proper response typically involves disconnecting from the network and contacting IT support.
3. Implement Defensive Systems
Humans are undoubtedly the first line of defense against cyber threats. However, they can’t do it alone. That’s why implementing advanced Endpoint Detection and Response (EDR) solutions is crucial. Tom explains that these systems will help you identify and, more importantly, mitigate a threat on time.
However, he also adds that you must restrict user permissions within the system. This way, even if a single component is compromised, the ransomware won’t take down the entire network.
4. Implement Network Segmentation
As you can see, a huge part of mitigating ransomware attacks is ensuring they don’t affect the entire network. That’s where network segmentation can also help.
As Tom explains, with network segmentation, the malicious actor in control of your network won’t be able to do “lateral movements.” In other words, even if they do manage to penetrate your network, they won’t be able to spread within it.
So, network segmentation is a critical part of the multi-layer approach every organization should adopt when it comes to cybersecurity.
5. Collaborate With Others
Remember – you aren’t the only one experiencing cyberattacks. In Venicia’s words, “ransomware has a global impact.”
That’s why organizations in the private sector are constantly encouraged to “talk to each other,” as Tom puts it. Of course, there’s always the issue of confidentiality, but Tom explains that this, too, can be resolved with a “closed circle of trust.”
Also, organizations in the private and public sectors are encouraged to share relevant information with institutions such as the Financial Services Information Sharing and Analysis Center (FS-ISAC).
In Europe, there’s also something called The No More Ransom Project. This Europol initiative has existed for years, hosting decryption keys for different types of ransomware. It has helped numerous individuals and organizations decrypt their systems and avoid paying the ransom.
Of course, this won’t always be possible, as the attackers typically keep changing the encryption keys. However, anything that helps organizations avoid paying the ransom is worth trying.
Why?
Because paying the ransom often won’t solve any problems.
As Tom explains it, you’re dealing with criminals, after all. So, they will often double the ransom after you pay the initial amount, having realized that you have the money. Or, they’ll simply take the money and run without giving you the decryption keys.
So, ongoing threat intelligence sharing should be among the top priorities for an organization, as it allows them to evade the last-resort scenario of paying the ransom.
6. Invest in Backups and Disaster Recovery
According to Venicia, backups and disaster recovery have a massive role to play in combating ransomware. She says that the primary reason organizations choose to pay the ransom is because they don’t have any backups in place. In other words, they don’t have an alternative way to get their data back.
That’s precisely what Tom has experienced working with many small and medium-sized businesses.
He says that these businesses usually don’t have disaster recovery procedures and data backups because they find them to be too expensive. Other times, they’ll say they didn’t have the time to deal with these measures. But whatever the excuse may be, one thing’s for sure – having no backups leaves you vulnerable to losing your data permanently in a ransomware attack.
According to Tom and Venicia, here’s what an ideal proactive approach to cybersecurity would look like.
Step No. 1 – Have regularly scheduled backups and ensure they’re stored in different environments, including offline ones. Tom suggests the 3-2-1 data backup strategy – have three copies of your data on two different mediums (e.g., hard drives and DVDs) with one copy off-site (a different physical location).
Step No. 2 – Regularly test your backups to see whether they’re able to handle different scenarios.
Step No. 3 – Implement a disaster recovery plan that outlines the steps for different types of incidents. Of course, these incidents shouldn’t only cover ransomware. Earthquakes, floods, and even meteor strikes should be considered in your plan. The last part might seem silly to you. In fact, it also sounded silly to Tom and his colleagues. That is, at least, until a meteor struck Russia in 2013. So, you never know!
The Importance of Cybersecurity Specialists
Most of the strategies for combating ransomware require one thing – a skilled cybersecurity specialist to execute them. This is also what most companies lack, which is why they easily fall victim to cyberattacks.
That’s why programs like the Enterprise Cybersecurity Master’s program at OPIT are essential for the future of cybersecurity. This program helps train the next generation of cybersecurity professionals to defend organizations against the so-called “Ransomware Armageddon” and any other cyber threat that might emerge.
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Source:
- Agenda Digitale, published on November 25th, 2025
In recent years, the word ” sustainability ” has become a firm fixture in the corporate lexicon. However, simply “doing no harm” is no longer enough: the climate crisis , social inequalities , and the erosion of natural resources require a change of pace. This is where the net-positive paradigm comes in , a model that isn’t content to simply reduce negative impacts, but aims to generate more social and environmental value than is consumed.
This isn’t about philanthropy, nor is it about reputational makeovers: net-positive is a strategic approach that intertwines economics, technology, and corporate culture. Within this framework, digitalization becomes an essential lever, capable of enabling regenerative models through circular platforms and exponential technologies.
Blockchain, AI, and IoT: The Technological Triad of Regeneration
Blockchain, Artificial Intelligence, and the Internet of Things represent the technological triad that makes this paradigm shift possible. Each addresses a critical point in regeneration.
Blockchain guarantees the traceability of material flows and product life cycles, allowing a regenerated dress or a bottle collected at sea to tell their story in a transparent and verifiable way.
Artificial Intelligence optimizes recovery and redistribution chains, predicting supply and demand, reducing waste and improving the efficiency of circular processes .
Finally, IoT enables real-time monitoring, from sensors installed at recycling plants to sharing mobility platforms, returning granular data for quick, informed decisions.
These integrated technologies allow us to move beyond linear vision and enable systems in which value is continuously regenerated.
New business models: from product-as-a-service to incentive tokens
Digital regeneration is n’t limited to the technological dimension; it’s redefining business models. More and more companies are adopting product-as-a-service approaches , transforming goods into services: from technical clothing rentals to pay-per-use for industrial machinery. This approach reduces resource consumption and encourages modular design, designed for reuse.
At the same time, circular marketplaces create ecosystems where materials, components, and products find new life. No longer waste, but input for other production processes. The logic of scarcity is overturned in an economy of regenerated abundance.
To complete the picture, incentive tokens — digital tools that reward virtuous behavior, from collecting plastic from the sea to reusing used clothing — activate global communities and catalyze private capital for regeneration.
Measuring Impact: Integrated Metrics for Net-Positiveness
One of the main obstacles to the widespread adoption of net-positive models is the difficulty of measuring their impact. Traditional profit-focused accounting systems are not enough. They need to be combined with integrated metrics that combine ESG and ROI, such as impact-weighted accounting or innovative indicators like lifetime carbon savings.
In this way, companies can validate the scalability of their models and attract investors who are increasingly attentive to financial returns that go hand in hand with social and environmental returns.
Case studies: RePlanet Energy, RIFO, and Ogyre
Concrete examples demonstrate how the combination of circular platforms and exponential technologies can generate real value. RePlanet Energy has defined its Massive Transformative Purpose as “Enabling Regeneration” and is now providing sustainable energy to Nigerian schools and hospitals, thanks in part to transparent blockchain-based supply chains and the active contribution of employees. RIFO, a Tuscan circular fashion brand, regenerates textile waste into new clothing, supporting local artisans and promoting workplace inclusion, with transparency in the production process as a distinctive feature and driver of loyalty. Ogyre incentivizes fishermen to collect plastic during their fishing trips; the recovered material is digitally tracked and transformed into new products, while the global community participates through tokens and environmental compensation programs.
These cases demonstrate how regeneration and profitability are not contradictory, but can actually feed off each other, strengthening the competitiveness of businesses.
From Net Zero to Net Positive: The Role of Massive Transformative Purpose
The crucial point lies in the distinction between sustainability and regeneration. The former aims for net zero, that is, reducing the impact until it is completely neutralized. The latter goes further, aiming for a net positive, capable of giving back more than it consumes.
This shift in perspective requires a strong Massive Transformative Purpose: an inspiring and shared goal that guides strategic choices, preventing technology from becoming a sterile end. Without this level of intentionality, even the most advanced tools risk turning into gadgets with no impact.
Regenerating business also means regenerating skills to train a new generation of professionals capable not only of using technologies but also of directing them towards regenerative business models. From this perspective, training becomes the first step in a transformation that is simultaneously cultural, economic, and social.
The Regenerative Future: Technology, Skills, and Shared Value
Digital regeneration is not an abstract concept, but a concrete practice already being tested by companies in Europe and around the world. It’s an opportunity for businesses to redefine their role, moving from mere economic operators to drivers of net-positive value for society and the environment.
The combination of blockchain, AI, and IoT with circular product-as-a-service models, marketplaces, and incentive tokens can enable scalable and sustainable regenerative ecosystems. The future of business isn’t just measured in terms of margins, but in the ability to leave the world better than we found it.
Source:
- Raconteur, published on November 06th, 2025
Many firms have conducted successful Artificial Intelligence (AI) pilot projects, but scaling them across departments and workflows remains a challenge. Inference costs, data silos, talent gaps and poor alignment with business strategy are just some of the issues that leave organisations trapped in pilot purgatory. This inability to scale successful experiments means AI’s potential for improving enterprise efficiency, decision-making and innovation isn’t fully realised. So what’s the solution?
Although it’s not a magic bullet, an AI operating model is really the foundation for scaling pilot projects up to enterprise-wide deployments. Essentially it’s a structured framework that defines how the organisation develops, deploys and governs AI. By bringing together infrastructure, data, people, and governance in a flexible and secure way, it ensures that AI delivers value at scale while remaining ethical and compliant.
“A successful AI proof-of-concept is like building a single race car that can go fast,” says Professor Yu Xiong, chair of business analytics at the UK-based Surrey Business School. “An efficient AI technology operations model, however, is the entire system – the processes, tools, and team structures – for continuously manufacturing, maintaining, and safely operating an entire fleet of cars.”
But while the importance of this framework is clear, how should enterprises establish and embed it?
“It begins with a clear strategy that defines objectives, desired outcomes, and measurable success criteria, such as model performance, bias detection, and regulatory compliance metrics,” says Professor Azadeh Haratiannezhadi, co-founder of generative AI company Taktify and professor of generative AI in cybersecurity at OPIT – the Open Institute of Technology.
Platforms, tools and MLOps pipelines that enable models to be deployed, monitored and scaled in a safe and efficient way are also essential in practical terms.
“Tools and infrastructure must also be selected with transparency, cost, and governance in mind,” says Efrain Ruh, continental chief technology officer for Europe at Digitate. “Crucially, organisations need to continuously monitor the evolving AI landscape and adapt their models to new capabilities and market offerings.”
An open approach
The most effective AI operating models are also founded on openness, interoperability and modularity. Open source platforms and tools provide greater control over data, deployment environments and costs, for example. These characteristics can help enterprises to avoid vendor lock-in, successfully align AI to business culture and values, and embed it safely into cross-department workflows.
“Modularity and platformisation…avoids building isolated ‘silos’ for each project,” explains professor Xiong. “Instead, it provides a shared, reusable ‘AI platform’ that integrates toolchains for data preparation, model training, deployment, monitoring, and retraining. This drastically improves efficiency and reduces the cost of redundant work.”
A strong data strategy is equally vital for ensuring high-quality performance and reducing bias. Ideally, the AI operating model should be cloud and LLM agnostic too.
“This allows organisations to coordinate and orchestrate AI agents from various sources, whether that’s internal or 3rd party,” says Babak Hodjat, global chief technology officer of AI at Cognizant. “The interoperability also means businesses can adopt an agile iterative process for AI projects that is guided by measuring efficiency, productivity, and quality gains, while guaranteeing trust and safety are built into all elements of design and implementation.”
A robust AI operating model should feature clear objectives for compliance, security and data privacy, as well as accountability structures. Richard Corbridge, chief information officer of Segro, advises organisations to: “Start small with well-scoped pilots that solve real pain points, then bake in repeatable patterns, data contracts, test harnesses, explainability checks and rollback plans, so learning can be scaled without multiplying risk. If you don’t codify how models are approved, deployed, monitored and retired, you won’t get past pilot purgatory.”
Of course, technology alone can’t drive successful AI adoption at scale: the right skills and culture are also essential for embedding AI across the enterprise.
“Multidisciplinary teams that combine technical expertise in AI, security, and governance with deep business knowledge create a foundation for sustainable adoption,” says Professor Haratiannezhadi. “Ongoing training ensures staff acquire advanced AI skills while understanding associated risks and responsibilities.”
Ultimately, an AI operating model is the playbook that enables an enterprise to use AI responsibly and effectively at scale. By drawing together governance, technological infrastructure, cultural change and open collaboration, it supports the shift from isolated experiments to the kind of sustainable AI capability that can drive competitive advantage.
In other words, it’s the foundation for turning ambition into reality, and finally escaping pilot purgatory for good.
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