AI-Native Security Controls: Which Platform Fits Your Risk Model?
AI agent security controls are security and governance mechanisms designed to limit, monitor, and approve the actions of AI agents as they access, transform, and share enterprise data, enforcing policy-aware guardrails across human and AI workflows so organizations can meet compliance requirements while still benefiting from autonomous AI-driven work. Box, Zenity, 7AI, and Cyberhaven all tackle this challenge, but they serve different security teams. Box is best if your main risk is AI agents touching sensitive content in a central repository. Zenity suits firms worried about long-running, autonomous agents making risky decisions over time. 7AI is ideal for security operations needing AI-native detection and response across scattered data. Cyberhaven Flow fits organizations whose biggest concern is data security across every workflow, human or agentic.
| Aspect | Box | Zenity | 7AI | Cyberhaven Flow |
|---|---|---|---|---|
| Primary focus | Content-level AI agent security and governance | Decision-layer governance for autonomous AI agents | Federated SIEM and agentic security workflows for investigations | AI-native data security across human and AI workflows |
| Key AI agent security controls | Agent guardrails, classification-based access, prompt-injection detection, audit trails | Runtime Boundaries and Exposure Management to govern AI actions before execution | Context graph with agentic workflows and AI-native security services | Agentic data protection using lineage, identity, and behavior across workflows |
| Best for | Enterprises centralizing content in Box that need strict enterprise AI governance | Organizations deploying long-horizon, autonomous AI agents in production workflows | Security teams that want AI-powered, cross-data investigation and automation without centralizing all logs | Companies whose top priority is preventing data loss and insider risk in AI-driven work |
Box: Content-Centric Guardrails for AI Agents
Box extends its existing content security framework to workflows where AI agents search, analyze, create, modify, or share files, so agent actions stay within policy-defined boundaries. If your enterprise content lives in Box, this is the most straightforward way to add AI agent security controls without deploying another product, since all permissions, labels, and audit trails apply directly to agent activity. Box’s agent guardrails let administrators define what custom agents can and cannot do based on company policies and content sensitivity, including label-based restrictions, approvals for deletion, and blocks on external sharing. The platform also adds prompt-injection detection that can log, flag, or block attempts to manipulate agents into ignoring instructions or exposing protected information. "Box’s 2026 State of Enterprise AI report found that 90% of surveyed IT leaders viewed security, regulatory and trust concerns as the largest obstacle to giving AI agents access to company content".
Zenity: Governing Autonomous AI at the Decision Layer
Zenity focuses on enterprise AI governance for autonomous agents, especially those long-horizon workflows where agents execute multi-step tasks over hours or days. Instead of waiting for alerts after harm, Zenity introduces security at the decision layer, evaluating every AI action before it becomes an enterprise action. Its Exposure Management continuously discovers AI agents, validates exploitable attack paths, and prioritizes AI exposure, helping teams see which agents can cause real damage. Runtime Boundaries then act as a decision engine, enforcing autonomous AI guardrails by analyzing each decision in the context of the agent’s broader execution history—intent, identity, requested action, accessed data, tools, previous activity, and enterprise policy—so risks emerging across multiple steps can be stopped before business impact. This is the right fit if your main worry is agents writing production code, invoking tools, and triggering workflows with growing autonomy, and you need controls that intervene before execution rather than after the fact.
7AI: Federated SIEM and Agentic Workflows for Security Teams
7AI targets security operations centers that want AI-native detection and response without forcing all data into a single SIEM. 7AI Federated SIEM lets teams query, investigate, and act on data wherever it lives, separating detection from storage so agents can read from and act on data in the vendor’s lake, the customer’s existing tools, or a mix of both. The platform builds a context graph for each environment, connecting federated data access, enterprise insights, and customer-defined skills so agents reason against how the organization actually works. With 7AI Build, enterprises and partners can define agentic workflows, skills, and AI-native security services on top of this platform, extending context to match local processes. For SOC teams, the value is clear: you get AI agents that investigate, optimize detections, respond, and hunt based on your reality, not a vendor’s default workflow, while every extension inherits transparency, controls, and enterprise governance.
Cyberhaven Flow: Data Security Across Human and Agentic Work
Cyberhaven Flow is built as an AI-native data security platform that protects data across human and agentic workflows, connecting lineage, identity, and behavior to follow data as it is created, copied, fragmented, and shared. If your main risk is data leaving the organization—whether through human mistakes, insider threats, or AI agents moving at machine speed—Flow is the most comprehensive option. It secures data on endpoints, in browsers, and in the cloud, unifying visibility, exfiltration prevention, and insider risk management in one platform regardless of where data lives or who handles it. The platform itself runs agentically: embedded agents handle configuration, detection, and analysis, automating data security programs end to end. Cyberhaven notes that endpoint-based agentic AI app adoption doubled year-over-year, reaching 60% adoption, and Flow was built so organizations can protect data across every human and agentic workflow with confidence.
Buy if / Skip if
- Buy the Box platform if your enterprise content is already centralized there and you need tight AI agent security controls tied to existing labels, permissions, and audit trails.
- Skip the Box platform if your main challenge is autonomous agents acting across many systems rather than content-level control in a single repository.
- Buy the Zenity platform if you deploy long-horizon, autonomous AI agents and need decision-layer Runtime Boundaries and Exposure Management to stop risky actions before execution.
- Skip the Zenity platform if your AI usage is limited to short-lived, human-supervised interactions where traditional monitoring is enough.
- Buy the 7AI platform if you run a busy SOC and want federated SIEM plus agentic workflows that investigate and respond using your existing data sources and context graph.
- Skip the 7AI platform if you do not operate a central security operations function or have limited need for large-scale, automated investigations.
- Buy the Cyberhaven Flow platform if your top priority is data security AI workflows that prevent exfiltration and insider risk across human and AI workflows, wherever data lives.
- Skip the Cyberhaven Flow platform if your main concern is governing AI agent decisions rather than tracking and protecting data across every workflow.
