Articles about Data

A new study finds 26 percent of surveyed organizations in the UK and US have fallen victim to AI data poisoning in the past year. This is where hackers corrupt the data that trains AI systems by planting hidden backdoors, sabotaging performance, or manipulating outcomes to their advantage.

The research from information security platform IO (formerly ISMS.online) surveyed over 3,000 cybersecurity and information security managers in the UK and US, and finds that that 20 percent of organizations have also reported experiencing deepfake or cloning incidents in the last year.

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Organizations are sitting on troves of information yet struggle to leverage this data for quick decision-making. The challenge isn't just about having data, but working with it in its natural state -- which often includes ‘dirty data' not cleaned of typos or errors.

We spoke to CEO of analytics company WisdomAI, Soham Mazumdar, to find out more about this challenge and how businesses can deal with it.

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The UK is no longer seeking a back door that would have allowed it to access encrypted data of Apple users. Controversial from the start, the UK has been pushing for technology firms such as Apple, Meta and others to provide it with access to encrypted users data.

Billed as a crime-fighting measure, the requests outraged not only the companies involved, but also privacy advocates and concerned individuals. But now the US Director of National Intelligence, Tulsi Gabbard, says that the UK has had a change of heart.

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A disconnect between cybersecurity confidence and data reality is leaving organizations exposed, according to a new report released today by Axonius.

The study, based on a survey of 500 US director-level and above cybersecurity and IT leaders, reveals that while 90 percent of cybersecurity leaders say their organization is prepared to take immediate action on a vulnerability, only 25 percent trust all the data in their own security tools.

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Electronic data interchange (EDI) is the lifeblood of modern business, but even a small error -- be it a connection failure, data quality issue, transformation failure, or data transmission issue for example -- can rapidly cascade, generating hundreds or even thousands of issues.

This can become a domino effect tipping over into longer root cause identification, inefficiency in managing a raft of open tickets, and a prolonged time to resolution. These factors can increase operational risk, leading to downstream supply chain issues that can jeopardize valuable business relationships.

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Every application, database, filesystem and SaaS service inevitably creates another data silo. From Hadoop-based data lakes to modern data warehouses and lakehouses, enterprises have invested millions in the promise of a single source of truth. But these grand visions invariably fall short.

We talked to Saket Saurabh, CEO and co-founder of Nexla, to discuss a more practical approach that embraces the existence of data silos while ensuring seamless access and usability.

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New threat intelligence from Zimperium reveals over five million unsecured public Wi-Fi networks have been detected globally since the beginning of 2025, with a staggering 33 percent of users still connecting to these open networks, putting enterprise data at risk in the process.

“Mobile devices are now a primary gateway to corporate data, but during travel, they’re also the most vulnerable,” says Kern Smith, VP of global solutions  at Zimperium. “Unsecured Wi-Fi, phishing disguised as travel alerts, and risky sideloaded apps are creating an ideal attack surface for cybercriminals -- especially in peak travel months.”

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IT and business leaders from UK mid-market organizations have conflicting views on the role of AI in enabling growth and driving productivity, according to new research.

The report from Node4, based on responses from over 600 IT and business leaders across multiple sectors, shows IT leaders rank investment in AI in their top two strategies for improving productivity and efficiency, but it only just makes business leaders' top five.

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Data is a window into understanding not just how your business operates, but how consumers engage with, and select, your solutions. But according to this annual analyst report from Precisely and Drexel University, 67 percent of organizations don’t trust the data they are using for decision making. That means that your organization could also be dealing with unreliable data. When poor data leads to incorrect decisions, it can mean lost opportunities and wasted resources -- which costs your organization money. In uncertain times, a business's livelihood could even be on the line.

Data quality -- especially when it comes to Marketing, Advertising and Sales data -- impacts how teams assess and move forward in the marketplace. These teams lean on datasets to adjust outreach activities and awareness campaigns. The insights used from data are often the underpinning foundation for how a business not just orients itself for short-term quarterly goals, but also how an organization pivots to gain an edge on competition in the long term.

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For many years, the IT community has consistently emphasized the inherent value and significance of data. Data is one of the greatest resources within a business, even referred to as an organization’s crown jewels, and as a result, has become a vital part of business’ security strategies.

However, as the global interconnectivity of technology continues to grow, securing data and its integrity has become one of the most complex parts of cybersecurity. The driving factor behind this increasing complexity is the broadening use of generative AI (GenAI) and large language models (LLMs), for which training data has largely become the world’s publicly available data.

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2024 ushered in one of the biggest shifts in data security, as cyber threats continued to increase in sophistication by leveraging advancements in AI to outpace traditional defenses. High-profile breaches across all industries continued, uncovering vulnerabilities in even the most robust systems. Meanwhile, the ongoing hybrid work models and migration to cloud-based technologies expanded the attack surface, creating new challenges for protecting sensitive data.

As 2025 rolls on, organizations need to follow best practices that represent a proactive, forward-thinking framework to stay ahead of emerging threats, protect critical data, and maintain the trust of their stakeholders. Here are ten best practices that organizations should consider.

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A new study finds that 92 percent of those surveyed in the manufacturing industry say AI is a top C-Suite priority and the same percentage agree it provides a competitive advantage.

But the report from Riverbed also shows that 69 percent are concerned about the suitability of their organization's data for AI usage, and only 42 percent rated their data as excellent for completeness and accuracy. It's notable too that 42 percent say their data quality is a barrier to further AI investment.

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Both AI agents and agentic AI are becoming increasingly powerful and prevalent. With AI agents, we can automate simple tasks and save time in our everyday lives. With agentic AI, businesses can automate complex enterprise processes. Widespread AI use is an inevitability, and the question going forward is not if we’ll use the technology but how well.

In a world where AI takes on more responsibility, we need to know how to measure its effectiveness. Metrics like the number of human hours saved or the costs reduced are, of course, important. But we also need to consider things like how ethically and securely our AI solutions operate. This is true when adopting third-party solutions and when training AI in house. 

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Data-driven organizations are increasingly struggling with the limitations of passive metadata practices. These traditional approaches quickly become outdated, leading to inaccurate insights and poor decision-making. Passive metadata often remains siloed, making it challenging to integrate and understand relationships between datasets. As a result, organizations face significant hurdles in achieving data agility -- the ability to adapt how information is interpreted and rapidly acted upon.

Active metadata management solves these challenges by providing a dynamic, intelligent layer that enables businesses to improve their decision-making processes and maintain a competitive edge in an increasingly data-centric environment.

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Becoming a data scientist is hard. The job focuses on mathematical tools, but also demands fluency with software engineering, understanding of a business situation, and deep understanding of the data itself. This book provides a crash course in data science, combining all the necessary skills into a unified discipline.

The focus of The Data Science Handbook is on practical applications and the ability to solve real problems, rather than theoretical formalisms that are rarely needed in practice.

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