The rise of Generative AI models has sparked a transformative shift across industries, offering unprecedented capabilities in automating tasks, enhancing decision-making, and fostering innovation. As publicly available tools, these models promise a democratization of technology, suggesting that any company can leverage them to boost efficiency and creativity. However, the reality of integrating and optimizing these AI models for specific corporate needs is far from plug-and-play.

Amazon Bedrock and Elastic’s Attack Discovery automate security analyst workflows As cyber threats grow increasingly sophisticated, the need for highly effective security measures becomes imperative. Traditional SIEMs aren’t equipped to address threats fast enough because they rely on too many manual and labor-intensive tasks. AI-driven security analytics from Elastic’s Search AI platform solves these challenges.

With the widespread use of AI technology, numerous AI models gather and process vast amounts of data, much of which comprises personal information utilized to offer personalized experiences. However, this abundance of data poses inherent risks, particularly in terms of privacy and security.

The Sysdig Threat Research Team (TRT) recently observed a new attack that leveraged stolen cloud credentials in order to target ten cloud-hosted large language model (LLM) services, known as LLMjacking. The credentials were obtained from a popular target, a system running a vulnerable version of Laravel (CVE-2021-3129). Attacks against LLM-based Artificial Intelligence (AI) systems have been discussed often, but mostly around prompt abuse and altering training data.

Language models have revolutionized natural language processing, enabling machines to generate human-like text with remarkable fluency and coherence. However, despite their impressive capabilities, traditional language models often need help with knowledge-intensive tasks that require factual accuracy, external knowledge integration, and contextual awareness.

In the era of big data and information overload, efficiently retrieving and generating relevant knowledge has become increasingly crucial across various domains. While traditional language models have made significant strides in natural language processing tasks, they often need help with factual accuracy, context awareness, and integrating external knowledge sources.

In the realm of modern enterprise productivity suites, Copilot for Microsoft 365 stands as a huge driver for efficiency, offering business users the ability to aggregate, summarize, and process data within the M365 suite of tools. However, for organizations with diverse infrastructure and applications, and the need for real-time data interactions, the out-of-the-box functionality requires augmentation to reach its full potential, not to mention secure controls for Copilot for M365.

SaaS applications have fundamentally transformed business operations by enabling on-demand user access to services and data via the internet from anywhere. Yet, despite countless benefits, SaaS in the enterprise is fraught with cybersecurity challenges.