Enterprise AI Security

Enterprise AI Security

Enterprise AI Security for the Autonomous Future

AI moves faster than traditional security. This project explores how organizations can continuously discover, assess, mitigate, and govern AI risks.

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Glossary →

Definitions of the terms used across our research on enterprise AI agents and AI security.

Reference Architecture →

How AI agent systems evolve from a stateless LLM call to a governed, autonomous system.

The Problem

Security has not kept pace with AI

AI agents can reason, use tools, access data, and act with increasing autonomy.

Security, governance, compliance, and audits are still manual, slow, and document-driven.

This creates a gap.

Thesis

From static policy to living control

Research Areas

What we study

AI Agents

How autonomous agents reason, use tools, and act on real systems.

AI Risk

Identifying and classifying the risks introduced by agentic AI.

AI Controls

Mitigations and safeguards that constrain AI behavior in production.

AI Governance

Turning policy and compliance requirements into enforceable practice.

AI Architectures

Runtime, harness, gateway, and control plane patterns for AI systems.

Autonomous Security

Security processes that operate continuously, without manual review.

Future Platform

From risk discovery to autonomous controls

The long-term platform should support the full lifecycle of enterprise AI risk management: