Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Online fraud remains a significant and rapidly expanding threat in today's cyber threat landscape. According to the FTC, consumers in the U.S. lost a staggering $12.5 billion to fraud in 2024, a 25% surge from the previous year. The most commonly reported category? Imposter scams. This difficult-to-intercept form of fraud alone accounted for $2.95 billion of those losses.

Today, the average phishing email that lands in your CEO's inbox is flawless. It uses perfect grammar, contains an intimate understanding of your organization’s current business landscape, and ends with an urgent, contextually relevant request. This isn't the work of a typical cybercriminal; it's the hallmark of generative AI being weaponized, transforming social engineering from a numbers game into a targeted strike.

Picture this: your vendor’s latest security audit just landed in your inbox, and you spot multiple failure points. What’s your immediate action plan? Failed vendor audits are an uncomfortable but increasingly common reality as reliance on third-party vendors grows, and handling them poorly can lead to data breaches, costly compliance violations, and serious operational disruptions. Knowing how to respond effectively isn't just good practice—it's essential risk management.

When you hear the word “ransomware,” many people think of a lone hacker launching a complex cyberattack. However, ransomware attacks that paralyze businesses worldwide have evolved into the product of a highly organized, industrialized criminal ecosystem. Their secret weapon? Ransomware-as-a-Service, or RaaS.

Is your team's favorite new productivity tool also your biggest data leak waiting to happen? Generative AI (GenAI) assistants like ChatGPT, Microsoft Copilot, and Google Gemini have quickly moved from novelty to necessity in many workplaces. These tools use machine learning and advanced algorithms to help employees draft content, analyze data, and even write code faster than ever before.

One of the confidentiality concerns associated with AI is that third parties will use your data inputs to train their models. When companies use inputs for training, the inputs may later become or inform outputs, potentially exposing confidential information to other people.

Artificial intelligence isn’t just a buzzword in cybersecurity—it’s rapidly becoming the backbone of both offense and defense in the digital battlefield. From hyper-realistic deepfakes to machine learning-powered threat detection, AI is fundamentally changing how we manage cyber exposure.

The proliferation of AI has rapidly introduced many new software technologies, each with its own potential misconfigurations that can compromise information security. Thus the mission of UpGuard Research: discover the vectors particular to a new technology and measure its cyber risk. This investigation looks at llama.cpp, an open-source framework for using large language models (LLMs).

On April 4, 2025, The Australian Financial Review reported on a set of credential abuse attacks targeting multiple Austrian superannuation funds. These attacks were not breaches of the infrastructure of those companies, but compromises of individual customer accounts via stolen credentials. How were those customer credentials stolen?

Staying ahead of the game is a top concern for security teams as the cyber threat landscape continues to evolve rapidly. Every year seems to bring new technological advances, which also introduce new cybersecurity trends and significant risks. As organizations face these challenges, it’s essential to build proactive defenses, not reactive responses. The next big cyber threat is already here—ready or not.