Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

In this day and age, data runs the world and the livelihood of many companies. Data has become so integral that there are many people who pay top dollar to read this data and make insights which will increase their profits by a huge amount. There are courses provided to study and understand the behemoth that is data. While it is lucrative, its sensitive content is also subject to misuse in the wrong hands.

Protecto, the pioneer in Generative AI-driven (Gen AI) data protection, is thrilled to announce a groundbreaking partnership with Encora, a leading digital engineering services company. This collaboration marks a significant step forward in securing the future of AI and Large Language Model (LLM) applications, safeguarding sensitive data and ensuring regulatory compliance in today's increasingly data-driven world.

In a groundbreaking move, Meta has released Code Llama 70B, the latest iteration in its series of open-source code generation models. Code Llama 70B maintains the tradition of an open license, fostering research and commercial innovation. This release builds upon its predecessors, including Llama 2, and is poised to redefine AI-driven code generation. One standout feature in the suite is CodeLlama-70B-Instruct, a finely tuned version explicitly designed for instruction-based tasks.

In an era where artificial intelligence is transforming industries, AI agents are emerging as powerful tools for automating workflows, enhancing decision-making, and delivering tailored user experiences. These agents are entrusted with handling vast amounts of sensitive data from sensitive healthcare records to financial transactions and intellectual property. However, this trust comes with a significant responsibility: ensuring robust data security and compliance.

The term “cloud” in the domain of IT infrastructure and computing conjures images of a rather abstract concept for storing data – most don’t know how it works and where it is located. A common misconception is that it lacks a physical location. This, however, is not true – cloud ecosystems operate from servers, and these servers always have a physical location.

GenAI has revolutionized the way businesses interact with data. Thanks to easy accessibility and automation capabilities, it is increasingly becoming a part of more business workflows. If something sounds too good to be true, there’s usually a catch. GenAI works by continuously processing and improving on the data fed into it – often sensitive data, making privacy a tradeoff. Tools like Gemini, Claude, and ChatGPT are becoming the most common shadow IT tools.

Large language models like OpenAI’s GPT, Anthropic’s Claude, and Google’s Gemini have changed the way businesses process and transmit sensitive data. LLMs boosted productivity and enhanced customer experience like never before, triggering unprecedented adoption across enterprises. Amidst all the rush and excitement, the negative impacts were overlooked and swept under the carpet – till it became a privacy and compliance issue.

AI applications are becoming a primary target for cyber threats due to their reliance on vast amounts of sensitive data. Traditional security measures often fall short in protecting AI-driven environments. A privacy vault is essential for securing AI data, ensuring that sensitive information is protected while enabling innovation. AI models depend on vast datasets for training and operation, but this dependency introduces critical security risks.

Patient data privacy is of utmost importance in today’s healthcare environment. Security is equally critical, forming the foundation of trust between patients and providers. Healthcare organizations handle incredibly sensitive information, including medical histories, diagnoses, and treatment plans. Mishandling this data carries significant risks far beyond just financial implications. These threats come in the form of significant monetary fines under some regulations.

As more businesses integrate AI into their workflows, it opens the door to unprecedented security and privacy risks. Amidst LLM’s immense power and unmatched capabilities, concerns around security and privacy often take a backseat. While some businesses deliberately ignore privacy concerns, the most common cause of this lack of concern is a gap in understanding the nature of the risks.