Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Over the past few years, enterprises have rapidly integrated GenAI into an increasing number of workflows and use cases. Amidst the rush and excitement to adopt a free tool that significantly boosts productivity, business leaders de-prioritized privacy, till it became a compliance issue. As privacy tools offering a quick patch quickly flooded the market, businesses ran into a new problem – which is the best tool?

AI security is one of the most pressing challenges facing the world today. Artificial intelligence is extraordinarily powerful, and, especially considering the advent of Agentic AI, growing more so by the day. But it is for this reason that securing it is so important. AI handles massive amounts of data and plays an increasingly important role in operations; should cybercriminals abuse it, the consequences can be dire.

By now we’re all familiar with the capabilities of generative AI for creating images. For some tasks, like casting an existing image in a recognizable art style, it works well. Much more than that and it encounters limitations: complex prompts often don’t return exactly what you imagined and iterating on a failed prompt can quickly become time-consuming.

Noam Cohen is a serial entrepreneur building seriously cool data and AI companies since 2018. Noam’s insights are informed by a unique combination of data, product, and AI expertise — with a background that includes winning the Israel Defense Prize for his work in leveraging data to predict terror attacks. As the Head of Artificial Intelligence at Torq, Noam is helping build truly next-gen AI capabilities into Torq’s autonomous SOC platform.

The shared responsibility model, introduced by AWS in 2011, defines the division of cloud security responsibilities between cloud providers and customers. Cloud providers are responsible for securing their physical infrastructure, while customers are responsible for securing their own data, configurations, and access. Cloud environments have grown and become much more complex since 2011.

Whether you're a founder, operator, or investor, this episode offers actionable startup advice and insight from one of the most influential voices in tech. ‍ Topics covered (timestamps):‍ ‍ Subscribe for more startup strategy and founder stories: YouTube Follow us on all social media platforms: LinkedIn, X, Instagram, and TikTok Follow Frameworks for Growth on Spotify and Apple Podcasts ‍ Read the full transcript‍

In the ever-evolving landscape of AI, a new enabler has emerged that's quietly transforming how language models interact with the digital world: Model Context Protocol, or MCP. It may not be a household name yet, but if your organization is experimenting with AI agents, it's time to get acquainted. MCP is becoming the cornerstone of LLM integration - bridging the gap between isolated AI systems and the interconnected web of enterprise & client applications.