Prompt injection attacks can manipulate Large Language Models (LLMs) to perform unintended actions, potentially exposing sensitive data or causing harm. This article outlines three approaches to defend against such attacks, each with its own strengths and weaknesses. This article also introduces NexusGenAI, a platform that simplifies building secure LLM applications.
Organizations can govern user prompts and model outputs in large language models using data access governance and security solutions that integrate with model libraries. This allows for real-time enforcement of data privacy, regulatory compliance, and access control policies while preserving the benefits of generative AI. Additional notes: READ ARTICLE
Generative AI, while potentially displacing some jobs, will also create entirely new job roles focused on guiding, refining, and interpreting AI, as well as integrating its capabilities into various sectors. This list highlights the diverse range of new job roles emerging in the generative AI era, emphasizing the opportunities alongside…
The increasing adoption of generative AI models has brought new security challenges. Startups are emerging to address these challenges by providing solutions in three categories: governance, observability, and security. READ ARTICLE
Prompt engineering plays a crucial role in ensuring the efficiency, security, scalability, and innovation of blockchain networks and applications. It enables projects to deliver features faster, enhance competitiveness, optimize performance, address evolving needs, and drive adoption. Prompt engineering enables agility, adaptability to changing requirements, faster time-to-market, and better user experiences…
Data security company Metomic has launched a browser plugin tool that allows businesses to monitor what sensitive data employees are uploading to ChatGPT, in order to identify data risks and prevent sensitive company information from being exposed. READ MORE
Criminals are increasingly using malicious AI prompts to exploit vulnerabilities in ChatGPT and other large language models. There is a growing underground market for these exploits as well as stolen ChatGPT credentials. READ MORE
The evolving landscape of AI regulation. In 2023, significant developments occurred in AI policy, setting a precedent for the upcoming year. Key areas include the U.S. government's approach to AI regulation, challenges in addressing AI-related harms and risks, the role of AI in global technological competition, and the impact of…
Protecting sensitive data is critical for organizations using AI, as AI relies heavily on data. Methods like confidential computing, hardware-based security features, and federated learning can help secure AI data while still allowing models access to the data they need. READ MORE
In 2024, artificial intelligence will be dominated by "hybrid AI" where large "foundation models" like GPT-4 are used as the brain/orchestrator, while smaller specialized AI models focus on specific tasks and tie into the foundation models. Building the large foundation models will only be possible for the richest tech companies…