HAX Research Laboratory

Security and Privacy

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.

HAX Research Laboratory

Security and Privacy

Building the Future of Secure, Autonomous Multi Agent Systems

Building Secure Systems

Enabling Safe Innovation

Strong security and privacy foundations support confident experimentation with autonomous agents while maintaining safety and trust.

Scaling Systems Responsibly

Adaptive protections keep pace with growing agent capabilities and increasingly complex environments.

Strengthening Trust

Clear safeguards and transparent data boundaries build confidence among users, developers, and stakeholders.

Supporting Global Interoperability

Unified, flexible frameworks help agent systems operate consistently across diverse regulatory and cultural contexts.

Key Questions

How do we design security systems that adapt as agents become more autonomous and capable?
What privacy guarantees can we provide when agents require rich contextual information to function effectively?
What privacy guarantees can we provide when agents require rich contextual information to function effectively?
How do traditional security models need to evolve for systems that reason, plan, and act independently?

Research Approach

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.

Adaptive Security for Autonomous Agents

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:

•Dynamic policies that shift based on context and risk signals.
•behavioral guardrails that set clear boundaries for safe operation.
•Continuous monitoring that flags anomalies and triggers fallback actions.

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Privacy-Preserving Context for Intelligent Agents

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:

•Scoped access that provides only the context needed for each task.
•Privacy techniques that protect data while keeping agents functional.
•Clear data boundaries tha show what is used, how, and why.

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Use Cases

Safe Automation of Routine Tasks

Coming Soon

Risks analysis in Agentic Discourses

Coming Soon

Adaptive Risk Detection

Coming Soon

Building Secure Systems

Enabling Safe Innovation

Strong security and privacy foundations support confident experimentation with autonomous agents while maintaining safety and trust.

Scaling Systems Responsibly

Adaptive protections keep pace with growing agent capabilities and increasingly complex environments.

Strengthening Trust

Clear safeguards and transparent data boundaries build confidence among users, developers, and stakeholders.

Supporting Global Interoperability

Unified, flexible frameworks help agent systems operate consistently across diverse regulatory and cultural contexts.

Key Questions

How do we design security systems that adapt as agents become more autonomous and capable?
What privacy guarantees can we provide when agents require rich contextual information to function effectively?
What privacy guarantees can we provide when agents require rich contextual information to function effectively?
How do traditional security models need to evolve for systems that reason, plan, and act independently?