As a small kid, I remember watching flying monkeys, talking lions, and houses landing on evil witches in the film The Wizard of Oz and thinking how amazing it was. Once the curtain pulled back, exposing the wizard as a smart but ordinary person, I felt slightly let down. The recent explosion of AI, and more specifically, large language models (LLMs), feels similar. On the surface, they look like magic, but behind the curtain, LLMs are just complex systems created by humans.

Cloud storage is a versatile and competitive market, so making a decision about choosing the best cloud storage services can seem intimidating. Fortunately, best cloud storage for personal use offers many features and services to securely store personal files, backups, sync, and protect your privacy online.

Artificial intelligence has taken over almost every aspect of our everyday lives. In cybersecurity, generative AI models with natural language processing are commonly being used to predict, detect, and respond to threats. But AI security assistants, although an upgrade from traditional machine learning, only provide very basic queries and summarization, which is insufficient to fully comprehend modern cloud attacks. As part of an ongoing effort to improve the cloud detection and response (CDR) experience,

The introduction, adoption, and quick evolution of generative AI has raised multiple questions about implementing effective security architecture and the specific requirements for protecting all aspects of an AI environment as more and more organizations begin using this technology. Recent security reports on vulnerabilities that expose Large Language Model (LLM) components and jailbreaks for bypassing prompting restrictions have further shown the need for AI defenses.

As global network infrastructure expands to include devices without traditional compute power, every organization’s attack surface becomes increasingly complex. Parallel to the increased complexity in the threat landscape is the increased scale and complexity of the signals and data necessary to produce meaningful cybersecurity insights. At its core, cybersecurity is a big data problem, requiring centralization of disparate data sources in uniform structure to enable continuous analytics.

Organizations increasingly rely on third parties for business operations, and as a result are working with more digital suppliers than ever. According to Gartner, 60% of organizations work with more than 1,000 third parties and this number will grow. High-profile vulnerabilities such as Log4Shell are a constant reminder of the risks posed by a breakdown in the software supply chain. This has spurred enterprises to increase the rigor of software risk assessments to ensure supply chain security.

In today’s rapidly evolving cloud landscape, organizations are grappling with the intricate challenge of striking a delicate balance between ensuring robust cybersecurity measures and facilitating seamless operational efficiency. As cloud adoption continues to surge, the traditional approach of granting standing privileges to users has become an increasingly significant security vulnerability.