Decoding AI Risk

In this episode, we unpack one of the most overlooked but dangerous risks in AI deployment— insider threats . While organizations often focus on securing data at rest and in transit, there's a blind spot few talk about: data in use. Imagine a secure, on-prem AI system running in your data center. It sounds safe—but what if a trusted insider with just enough access could dump memory and expose...

In this thought-provoking episode, we explore a major obstacle standing in the way of AI innovation: the complete lack of an enterprise-grade AI platform that meets the unique demands of high-trust industries. From banking and intelligence to healthcare, organizations are eager to deploy AI—but only if it guarantees the highest levels of privacy, security, and compliance. We walk through real-worl...

In this episode, we tackle one of the most pressing questions in today’s AI-driven world:  Who’s responsible when generative AI gets it wrong?   As enterprises increasingly adopt GenAI for productivity, content creation, and analytics, the stakes rise just as fast. But with those benefits come real challenges—AI hallucinations, misinformation, data privacy breaches, and regulatory risks. We dive i...

In this episode, we examine one of the most significant legislative frameworks shaping the future of artificial intelligence—the EU AI Act. As AI continues to transform industries and daily life at an unprecedented pace, the European Union is leading the charge in regulating its development and use through a risk-based, human-centered approach. We explore how the Act categorizes AI systems into Un...

As AI adoption accelerates across industries, so do the risks and regulatory concerns. In this episode, we deeply dive into AI governance—what it is, why it matters, and how organizations can implement it to ensure ethical, compliant, and trustworthy AI systems. We unpack the real-world challenges businesses face when deploying AI, especially when sensitive personal data is involved, and explain h...

In this episode, we explore how large language models (LLMs) have human-computer interaction and revolutionized why they're not without limitations. While LLMs can generate impressively human-like responses, they often rely on static training data, leading to outdated or inaccurate answers that may erode user trust. To address these challenges, we dive into the powerful technique of Retrieval-...

Fortanix product managers encountered challenges in overhauling their pricing model due to scattered and unstructured data residing in various systems. They envisioned using AI to extract insights by querying this data with natural language. However, concerns about data security and confidentiality arose with public AI models. This led to the concept of an  on-premise AI solution  that keeps sensi...

Organizations are increasingly integrating generative AI, but this adoption introduces significant security, privacy, and regulatory concerns. OWASP has identified the top ten security risks for large language models in 2025 to guide enterprises in mitigating these challenges.  These risks range from prompt injection and sensitive information disclosure to supply chain vulnerabilities and misinfor...

Rapid AI adoption presents significant security challenges, as these intelligent systems learn from, store, and potentially leak sensitive data. A recent GenAI report highlights that a large majority of organizations have already experienced data breaches, indicating current security measures are insufficient for AI environments.  This crisis is fueled by the exposure of sensitive data in AI model...