Alignment faking in AI creates hidden enterprise risk. Learn how deceptive model behavior bypasses safety checks and exposes new attack surfaces.

An autonomous AI agent hacked McKinsey’s internal chatbot in two hours. Learn how the vulnerability worked and what it reveals about enterprise AI security risks.

The Sears exposure shows that AI chatbot logging is a security risk, not just telemetry. Modern agents generate large volumes of sensitive data across text and voice, often stored without proper access control, retention limits, or session management. Voice data adds another layer of risk since it can be used for impersonation and fraud. Beyond data leakage, logs can also expose how the agent works, making it easier to reverse engineer or manipulate. The issue isn’t the breach itself, it’s treating AI logging like a feature instead of a regulated data system.

Deep Agents don’t add new AI capability, they change the architecture. Planning, memory, filesystem storage, and subagents make agents more powerful but also expand the security surface. Stored content becomes a prompt injection risk, memory spans sessions, and agent handoffs create trust gaps. It’s more observable, but only secure if teams enforce strong controls around tools, storage, and monitoring.

AI agents are not traditional IT assets. Discover why enterprises need runtime governance and behavioral controls to secure autonomous systems.

Explore the top 10 Guardian Agents securing enterprise AI in 2026. Compare runtime governance, AI agent protection, and control platforms.

Meta’s acquisition of Moltbook highlights the growing importance of infrastructure for coordinating and identifying AI agents in the emerging “agentic web

MWC 2026 spotlights the rise of AI agent security as autonomous AI scales across enterprise systems, with NeuralTrust winning Digital Horizons.