Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

We’re excited to share new updates and enhancements for August, including: For more information on these updates and others, please read the complete list below and follow the links for more detailed articles.

Generative AI can deliver great improvements in work productivity and quality. But business users must be able to rely on the dependability of the responses their AI tools generate for them. That’s only possible with sophisticated, often complex prompts. In addition, companies want AI solutions that ensure a high level of consistent results across teams. With gen AI, when 10 users ask the same questions using their own prompts, they get 10 different responses.

Artificial Intelligence (AI) is super-charging customer service, amplifying personalized product recommendations, and accelerating workflows that enable humans to focus on higher-value tasks. However, AI cannot deliver desired productivity improvements to financial organizations without foundational security protection in place. In this blog, I recap several best practices that empower financial institutions to leverage AI safely and responsibly.

Egnyte’s core values have always included trust, security, and enablement of business agility for customers who work in data-intensive and highly regulated industries. In turn, our cybersecurity program has always been aligned with those values, resulting in Egnyte’s maintenance and continuous expansion of our portfolio of industry-specific compliance certifications.

Egnyte AI agents are smart, task-specific AI assistants built to automate repetitive, time-consuming work, so that your team can stay focused on high-impact and strategic tasks. From reviewing documents to researching topics or translating content, these agents act like always-on digital coworkers who execute task-specific instructions while securely leveraging information contained in your private documents and on the web.

In the rapidly evolving landscape of AI, Retrieval-Augmented Generation (RAG) has become the go-to solution for grounding language models in factual data. It has been effective for question-answering, but for complex, exploratory tasks that demand vast data analysis and culminate in a structured narrative, we must look beyond simple RAG. This isn’t just a theoretical challenge, it's the next step in our own product journey: This progression now culminates in a more advanced capability.

We’re excited to share new updates and enhancements for July, including: For more information on these updates and others, please read the complete list below and follow the links for more detailed articles.