Roman Zhukov (Red Hat) and Damian Brady (GitHub) explore the evolving landscape of AI-assisted software development, discussing its impact on developer workflows, code quality, security, and the future of developer roles. They emphasize that while AI tools are powerful amplifiers, human oversight remains essential for quality, security, and legal compliance.
This post explores how to secure modern agentic AI systems by applying the core principles of Zero Trust. It details the unique attack surfaces of AI agents, such as prompt injection and model poisoning, and outlines a comprehensive security architecture including non-human identity management, AI firewalls, and the critical role of human oversight.
The panel discusses OpenAI's new Codex application, framing it as a necessary 'table stakes' move in the competitive AI coding agent market rather than a game-changer. The conversation pivots to the importance of agent orchestration as the next frontier for value creation and monetization. They also explore the Moltbook (OpenClaw) phenomenon—a social network for AI agents—debating whether it's a valuable sociological experiment or a mere novelty, while highlighting the significant security vulnerabilities and practical hurdles it exposes.
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.
A deep dive into the security vulnerabilities of Multi-Context Protocol (MCP) for AI agents. The talk explores how identity loss, "all-or-nothing" permissions, and disappearing audit trails create significant attack surfaces, and presents solutions like identity chain tracking, context-aware permissions, and intelligent auditing to secure agent-to-tool communication.
To build secure AI systems, we must first learn to break them. Katharine Jarmul explores the landscape of adversarial AI, detailing how attackers exploit fundamental weaknesses in deep learning models—from poisoned training data and overparameterization to the attention mechanism itself. This talk provides a practical taxonomy of attacks and a primer on building robust defenses.