Welcome to Agentic AI Unlocked, your deep dive into the transformative world of Agentic AI—systems combining large language models with advanced reasoning and autonomous action. These intelligent agents promise to disrupt industries, yet introduce a fundamentally new threat surface. Risks like memory poisoning, tool misuse, prompt injection, and insider threats highlight the urgent need for robust security and real-time governance.

The OWASP GenAI Security Project aims to provide actionable insights into these challenges, helping organizations responsibly develop, deploy, and govern agentic AI. We advocate a proactive, defense-in-depth approach across the entire agent lifecycle.

Join us as we explore crucial safeguards like fine-grained access control, runtime monitoring, memory hygiene, and secure tool integration. We'll also cover the evolving ecosystem of agent frameworks, emerging protocols, and complex regulatory landscapes like ISO/IEC 42001, NIST AI RMF, and the EU AI Act.

Agentic AI offers immense promise alongside significant risks. This podcast equips you with the understanding and strategies for secure and responsible deployment. Let’s unlock the future of AI, securely.

This episode explores DeepSeek, a Chinese AI startup challenging the AI landscape with its free alternative to ChatGPT. We'll examine DeepSeek's innovative architecture, including Mixture-of-Experts (MoE) and Multi-head Latent Attention (MLA), which optimize efficiency. The discussion will highlight DeepSeek's use of reinforcement learning (RL) and its impact on reasoning capabilities, as well as how its open-source approach is democratizing AI access and innovation.

We will also discuss ethical concerns, the competitive advantages and disadvantages of US-based models, and how DeepSeek is impacting cost structures and proprietary models. Join us as we analyze DeepSeek’s influence on the AI industry and the future of AI development and international collaboration

Are current AI safety benchmarks for multimodal models flawed? This podcast explores the groundbreaking research behind VLSBench, a new benchmark designed to address a critical flaw in existing safety evaluations: visual safety information leakage (VSIL)

We delve into how sensitive information in images is often unintentionally revealed in the accompanying text prompts, allowing models to identify unsafe content based on text alone, without truly understanding the visual risks This "leakage" leads to a false sense of security and a bias towards simple textual alignment methods.

Tune in to understand the critical need for leakless multimodal safety benchmarks and the importance of true multimodal alignment for responsible AI development. Learn how VLSBench is changing the way we evaluate AI safety and what it means for the future of AI.