Zenity Unveils AI Security Platform with Runtime Boundaries for Autonomous Agents
Zenity's expanded AI security platform introduces Exposure Management and Runtime Boundaries to govern autonomous AI decisions before they lead to enterprise actions.
Zenity has announced a significant expansion of its AI security platform, introducing a new architecture centered on Exposure Management and Runtime Boundaries. This enhancement aims to provide granular control over autonomous AI agents, particularly those operating on long-horizon tasks, by governing their decisions at the point of choice before they translate into enterprise actions.
The core challenge with the first generation of enterprise AI was understanding its usage and access. However, autonomous AI, especially long-horizon agents that execute multi-step tasks over extended periods, presents a more complex risk landscape. These agents can generate risk through a sequence of individually permissible decisions as their context, access, and actions evolve throughout a workflow. As AI agents increasingly write code, access sensitive systems, handle confidential data, and execute business processes with growing autonomy, AI security must shift from post-action observation to pre-action governance.
The Zenity AI security platform now integrates three key capabilities: Surface, Enforce, and Protect. Surface focuses on discovering AI agents, validating attack paths, and prioritizing exposure. Enforce, powered by Runtime Boundaries, evaluates every AI action in real-time, determining whether it should proceed, be blocked, or be terminated before any business impact occurs. Protect closes the loop with AI-driven Digital Forensics and Incident Response (DFIR), while Guardian Agents continuously learn from investigations to refine policy decisions.
At the heart of the Enforce layer, Runtime Boundaries act as the decision engine for autonomous AI. This system continuously assesses each AI decision against the agent's execution history, intent, identity, requested action, accessed data, tools, and enterprise policies. This contextual analysis allows for the identification and mitigation of risks that emerge across multiple steps in a workflow, preventing potential business disruptions.
Zenity's platform supports a wide range of AI agents and platforms, including Claude Code, Cursor, Microsoft Copilot, Salesforce Agentforce, ChatGPT Enterprise, Amazon Bedrock, Azure AI Foundry, and custom-built agents. Whether the goal is to prevent sensitive data leakage, govern coding agents, restrict privileged operations, or control critical infrastructure, Runtime Boundaries offer enterprises precise control over their autonomous AI deployments.
"Every major technology shift has forced security to evolve," stated Tomer Teller, VP of Product at Zenity. "Autonomous AI introduces the next evolution: the decision itself. The future of AI security won’t be defined by what an AI agent already did. It will be defined by what AI is allowed to do before it acts. Security starts at the decision layer and Runtime Boundaries are the engine that makes that possible."
The Exposure Risk component proactively identifies the most likely attack paths, informing Runtime Boundaries before AI actions are taken. Post-event, AI-powered DFIR reconstructs decision chains, enabling Guardian Agents to continuously refine policies and enhance future protection. This creates a continuous AI security loop where exposure informs runtime decisions, investigations improve policies, and every decision strengthens future defenses.
This new architecture represents a significant step forward in securing the rapidly evolving landscape of autonomous AI, moving security controls upstream to the critical decision-making points within AI workflows.