We’re building the future of secure, autonomous multi-agent systems. As AI agents grow more capable and autonomous, they open the door to new ways of working and building. This progress also gives us a chance to evolve our security and privacy models to support safer, more resilient agent ecosystems.
Strong security and privacy foundations support confident experimentation with autonomous agents while maintaining safety and trust.
Adaptive protections keep pace with growing agent capabilities and increasingly complex environments.
Clear safeguards and transparent data boundaries build confidence among users, developers, and stakeholders.
Unified, flexible frameworks help agent systems operate consistently across diverse regulatory and cultural contexts.
Our research focuses on defining how autonomous agents can operate safely and responsibly as they take on more decision-making and contextual reasoning. We examine two foundational dimensions — security and privacy — to develop adaptive models that evolve alongside increasing agent capability.
The environments agents operate in are dynamic and unpredictable, calling for security models that can respond with similar agility. We focus on creating mechanisms that adapt to context, behavioral signals, and evolving system states.
We Explore
Agents work with a wide range of context—identity signals, system state, historical patterns, and environmental cues—to operate effectively. Ensuring this context is handled in a safe, responsible, and transparent way is essential for building systems that remain both capable and trustworthy.
We Explore
