As AI integrates into every stage of the SDLC, the area of software testing is undergoing transformative and unprecedented changes. In this article, we will discuss the ethical considerations for AI-powered software testing, examining the advantages and potential hurdles generative AI presents as a new technology being applied across the SDLC.

Large language models (LLMs) have revolutionized various fields by providing advanced natural language processing, understanding, and generation capabilities—these models power applications from virtual assistants and chatbots to automated content creation and translation services. Their proficiency in comprehending and generating human-like text has made them vital resources for businesses and individuals, driving efficiency and innovation across industries.

The impact of the 2024 RSA Conference on security in San Francisco was beyond expectations. It was really a fantastic opportunity to meet an amazing group of individuals from all stages of the software supply chain from CISOs to researchers to development and security teams.

Large language models (LLMs) have experienced a meteoric rise in recent years, revolutionizing natural language processing (NLP) and various applications within artificial intelligence (AI). These models, such as OpenAI's GPT-4 and Google's BERT, are built on deep learning architectures that can process and generate human-like text with remarkable accuracy and coherence.

As part of our ongoing partnership and investment in Microsoft, we have deepened our integration with Microsoft's Copilot within Microsoft Teams. This integration expands Copilot's capabilities by incorporating Egnyte as an additional data source. Microsoft 365 Copilot users can now derive valuable insights from documents stored in Egnyte, enhancing the usefulness of both Egnyte and the Microsoft Copilot solution for joint customers.

Since CrowdStrike Charlotte AI became generally available, we’ve seen firsthand how genAI can transform security operations, enabling teams to save hours across time-sensitive tasks and accelerate response to match the speed of modern adversaries.

As artificial intelligence (AI) continues to advance, its impact on cybersecurity grows more significant. AI is an incredibly powerful tool in the hands of both cyber attackers and defenders, playing a pivotal role in the evolving landscape of digital threats and security defense mechanisms. In this blog, let’s explore the ways AI is employed by attackers to conduct cyber attacks, and how defenders are using AI to deter and counter threats.

In the oft-obscure world of Architecture, Engineering, and Construction (AEC), the structures we see reaching for the skyline are not just feats of design and engineering but archives of data, each rivet and beam a data point in a colossal network of information. Yet, with these digital monoliths comes an invisible vulnerability – data control, a challenge that’s upending the AEC industry.

Analysis of malicious large language model (LLM) offerings on the dark web uncovers wide variation in service quality, methodology and value – with some being downright scams. We’ve seen the use of this technology grow to the point where an expansion of the cybercrime economy occurred to include GenAI-based services like FraudGPT and PoisonGPT, with many others joining their ranks.

The definition of Artificial Intelligence (AI) has been thrown around whilst it has risen to the top of the tech agenda over the past couple of years. Security professionals have determined AI to be a risk to businesses, and also an opportunity. But could it also be a way to better defend your network against attacks? For many years, AI and Machine Learning have gone hand in hand; with AI used to better determine defensive decisions and cut down on the human element in more basic functions.