Jul 29, 2026

How Kepler Built Verifiable AI for Financial Services — Vinoo Ganesh

Vinoo Ganesh, CEO of Kepler, discusses building verifiable AI for financial services. He argues that while LLMs are powerful probability machines, they are unreliable for deterministic financial tasks. Kepler's solution involves a deterministic substrate that wraps AI, based on three tenets: atomic provenance (AI references, not creates, numbers), scope determinism (AI decides what to compute, but deterministic tools perform the math), and derivation chains (traceable calculation paths for derived figures). This ensures numerical accuracy, verifiability, and traceability, moving beyond simple citations to true proof of work.

Jul 20, 2026

Why Your Agent Disagrees With Itself (And What To Do About It) - Diane Lin, Datadog

This session addresses the critical problem of AI agent inconsistency, particularly in LLMs, which leads to "flip-flops" in high-stakes domains like cybersecurity. It argues that this isn't a model failure but a signal of ambiguity in the "gray zone" near decision boundaries. The proposed solution involves using active learning to identify these ambiguous cases, followed by augmenting agents with semantic memory (explicit policies) and episodic memory (past similar cases) to clarify decisions, improve consistency, and adapt to customer-specific preferences without relying solely on expensive fine-tuning.

Apr 29, 2026

Is open source safe? Featuring Mixture of Experts

AI and security experts debate the complex relationship between open source and AI, weighing the foundational role of open source in innovation against the significant security challenges of both proprietary and open models, and exploring the difference between 'secure' and 'securable' systems.

Feb 03, 2026

Securing & Governing Autonomous AI Agents: Risks & Safeguards

Experts Jeff Crume and Josh Spurgin explore the critical security and governance challenges posed by autonomous AI agents. They detail common threats like prompt injection, data poisoning, and model theft, and discuss governance issues such as bias, transparency, and accountability, providing a set of actionable safeguards to build secure, trustworthy, and compliant AI systems.

Aug 19, 2025

915: How to Jailbreak LLMs (and How to Prevent It) — with Michelle Yi

Tech leader and investor Michelle Yi discusses the critical technical aspects of building trustworthy AI systems. She delves into adversarial attack and defense mechanisms, including red teaming, data poisoning, prompt stealing, and "slop squatting," and explores how advanced concepts like Constitutional AI and World Models can create safer, more reliable AI.