Ahead of the EU’s Digital Operational Resilience Act (DORA) coming into force on 17th January 2025, and on the back of the updated Network and Information Security Directive (NIS2) coming into effect from 17th October of this year, organisations across Europe are scrambling to understand what these regulations mean for them. The initial reaction from many businesses is one of concern, and understandably so, non-compliance can lead to significant penalties and reputational damage.

The EU AI Act (the “AI Act”) is the world’s first comprehensive AI law. The Act lays down a harmonised legal framework for the development, supply, and use of AI products and services in the EU.

In the rapidly evolving landscape of IT infrastructure, businesses are constantly faced with the critical decision of choosing between on-premises and cloud solutions. The allure of cloud computing, with its promises of scalability, flexibility, and cost efficiency, often leads organizations to assess the financial implications of their choices meticulously. In this blog post, we’ll delve into the complexities of assessing on-premises vs.

In an era dominated by technological advancements and interconnected digital landscapes, the need for robust cybersecurity measures has never been more critical. Cyber threats, attacks, and ransomware incidents continue to rise, targeting organizations of all sizes and industries. In this landscape, network penetration testing has emerged as a vital component of a comprehensive cybersecurity strategy.

In the rapidly evolving landscape of technology and data-driven decision-making, machine learning has emerged as a powerful tool to gain insights, optimize processes, and drive innovation. Machine learning, a subset of artificial intelligence, involves building models that can analyze data and make predictions. These models can unlock valuable insights and opportunities, making them a potent growth lever for organizations across various industries.

In the world of technology, few concepts have captured our collective imagination like Artificial Intelligence (AI). It’s the promise of machines that can think, learn, and perform tasks with a level of sophistication that mimics human intelligence. Yet, the allure of AI has also given rise to a web of confusion, myths, and misunderstandings.

Security risks within the IT infrastructure of global businesses are increasingly prevalent – and damaging. When swathes of data are separated in the hybrid or multi cloud, it can leave big open doorways for malware to walk right in. The message I want businesses to hear is that cloud and data are not separate. IT only exists to service the needs of a business’ data. Securing cloud services – and therefore your data – is a business-critical issue.

In this latest podcast in our ‘Beyond Data’ series, Tessa Jones (Calligo’s Chief Data Scientist) and Peter Matson (Data Science Practice Lead) talk with Oxford University’s Professor Philip Howard about the threats posed to democracy by technology, specifically in the shape of Lie Machines.

With growing volumes of personal data being collected, analyzed, shared and stored, there is more expectation than ever on businesses to ensure privacy for their employees, clients and wider supply chain. The digital age has streamlined ways of working, improved the targeting and personalization of services and communications and made detailed information available at the touch of a screen. But personal data is exactly that – personal.

In Calligo’s latest Beyond Data podcast, co-hosts Sophie Chase Borthwick and Tessa Jones are joined by Alexander Visheratin, Artificial Intelligence Engineer at Beehive AI. Here we explore some of the episode’s highlights; the importance of Natural Learning Processing (NLP) and the pros and cons of output produced by examples like OpenAI’s ChatGPT-3.