Enterprise 4.0 refers to a new wave of B2B startups that combine artificial intelligence, intelligent automation and proprietary access to data to deliver actionable insights for enterprise businesses. These disruptive startups provide full stack solutions in the form of hyper-niche vertical solutions, programmable cloud infrastructure or cloud-native security innovations to generate immediate business value and significant ROI improvements for their customers.

Our fascination with GenAI is different than with “revolutionary” technologies of the past. We’re now facing a “hierarchy of unknowns” that we think we can address with what we believe will work. What are frameworks can we use to protect and prepare us?

As sophisticated GenAI and ML technologies become increasingly entwined into every day business operations, understanding their fundamental principles, how they work, and associated security implications has never been more crucial.

AI- and its latest forms of GenAI and AGI- are now heralded as the next productivity frontier, projected to add trillions of dollars to the global economy. How big is the opportunity and what does that mean for GenAI-assisted cybersecurity?

In this engaging seminar, Caleb Sima, Chair of CSA AI Safety Initiative presents a business-friendly overview of GenAI and Large Language Models (LLMs), shedding light on their practical security implications and risks, rather than focusing on futuristic applications.

Over the past decade, Forgepoint and BGV have been investing in innovative AI and cybersecurity startups. Alberto Yépez and Anik Bose share their perspectives on how cybersecurity innovation will be impacted by GenAI. Their joint theses is that GenAI will enhance the capabilities of malevolent actors, increase the need for guardrails, with innovation advantage going to the incumbents in the near term and to startups for the longer term.

Organizations across the board significantly underappreciate AI risk. Poor implementation of AI can perpetuate existing workplace inequities. In this article, the World Economic Forum examines how businesses can ensure the ethical deployment of generative AI.

Developed in collaboration with the Markkula Center for Applied Ethics at Santa Clara University, this handbook offers organizations a strategic plan to enhance ethical management practices, empowering them to navigate the complex landscape of disruptive technologies such as AI, machine learning, encryption, tracking, and others while upholding strong ethical standards. 

Generative AI (GenAI) techniques, such as large language models (LLMs) and diffusion models, have shown remarkable capabilities. However, GenAI can be used just as well to generate new attacks and increase the velocity and efficacy of existing attacks. This paper reports the findings of a workshop held at Google (co-organized by Stanford University and the University of Wisconsin-Madison) on the dual-use dilemma posed by GenAI. 

Advancements in deep learning, reinforcement learning, and generative AI have dramatically extended the toolkit of machine learning methods available to enterprise practitioners. This book provides a comprehensive guide to how marketing, supply chain, and production operations can be improved using these new methods, as well as their use in conjunction with traditional analytics and optimization approaches.   

To help add clarity to the rapidly unfolding conversation around Generative Artificial Intelligence, the Aspen US Cybersecurity Group convened experts to draft high-level guidance on how companies can inform employee use of openly available GenAI technology.  

In this annual report, the EAIGG presents a collection of thought-provoking articles from leading policymakers, enterprise leaders and top VCs to elevate the discourse, offering insights that can illuminate the path forward and foster a platform for meaningful dialogue around GenAI.