Datasheet (PDF)

Govern AI at the Speed It Operates

Autonomous agents like Claude Code, Codex, and Copilot now run on endpoints, inherit employee identity, and act on production data at machine speed, with no traditional security control watching. Legacy data loss prevention (DLP) was built for browser-based, human-speed interactions and cannot tell a governed corporate AI instance from a personal account leaking intellectual property. This datasheet shows how Cyberhaven Flow discovers every AI app and autonomous agent, monitors what data they touch, and enforces policy in real time before a leak becomes a breach.

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60%
Endpoint-based agentic AI app adoption in May 2026, up from 49% five months earlier (Cyberhaven Labs)
49.5%
Of developers use AI coding assistants
39.7%
Of AI interactions involve sensitive data
Trusted by enterprise security teams

What's inside

01

Why legacy DLP cannot see agentic AI risk

  • Autonomous agents inherit employee identity, access production data, and act at machine speed, with no traditional control seeing it.

  • Legacy DLP cannot distinguish a governed corporate ChatGPT instance from a personal account leaking intellectual property, or detect goal hijacking and privilege abuse across multi-step agent workflows.

  • Shadow agents and unmanaged Model Context Protocol (MCP) servers expand the attack surface faster than security teams can catalog it.

02

Cyberhaven Flow governs AI apps and agents in real time

  • Cyberhaven Flow, the AI-native data security platform for the agentic enterprise, combines a Data Lineage graph, AI-powered content inspection, and a lightweight endpoint agent.

  • Four steps, discover, monitor, score, and control, give security teams visibility into AI apps, agents, and data flows across software as a service (SaaS) apps, endpoints, and developer environments.

  • Policies enforce based on behavior and data context, not assumptions, so teams can adopt AI without losing control.

03

Outcomes: fewer blind spots, faster investigations

  • Reconstruct full execution lifecycles for Claude Code, Codex, Copilot, and other agents, including tool calls, data access, and API invocations.

  • Runtime guardrails block, warn, or redact at the prompt and response level, with plain-English risk explanations in place of generic block pages.

  • Investigate 5x faster and score AI apps and agents across five risk dimensions, so security teams can accelerate AI adoption instead of blocking it.

Continue your research

Use Case
Secure AI Usage
Adopt AI across your workforce with visibility into every app and agent, and guardrails that steer employees toward sanctioned tools instead of shadow AI.
Product
The AI Security Platform
See how Cyberhaven governs AI apps, agents, and data flows from a single platform built around data lineage, not perimeters.
Playbook
The Insider Risk Incident Response Playbook
A practical framework to detect, investigate, and contain insider risk, covering offboarding gaps and risks from shadow AI and agentic workflows.