Know What Your AI Agents Do With Data
AI agents now open files, invoke tools, and complete multi-step tasks under an employee’s identity, often without oversight. A log entry showing an AI app was used does not reveal what the agent read, did, or sent. This datasheet shows how Agentic Data Security in Cyberhaven Flow discovers AI use, traces activity with data lineage, scores risk, and controls risky data flows so AI adoption keeps moving.
What's inside
Why Agentic AI Creates Three New Security Gaps
How AI use moved past the browser tab to desktop assistants, coding agents, command-line tools, local models, and Model Context Protocol (MCP) servers.
Why a log entry showing an AI app was used leaves teams unable to see what an agent read, what it did, or where its output went.
The shadow AI, workflow-context, and control gaps security teams need to close as agents act on sensitive data across workflows.
Four Steps From AI Discovery to Data Control
Inventory sanctioned and unsanctioned AI apps, agents, corporate and personal accounts, and MCP servers across endpoints, browsers, and developer tools.
Connect each agent action, including tool calls and data access, to the files involved and the identity behind it with data lineage.
Score AI apps and agents with AI Risk IQ, then apply guardrails that block, warn, or monitor based on data context and agent behavior.
Where Flow Fits in Your Data Security Program
How Cyberhaven extends data loss prevention (DLP), data security posture management (DSPM), and insider risk management (IRM) to AI agents.
Three scenarios, from a developer running an AI coding assistant to an analyst investigating an AI-related exposure, mapped to how Flow helps.
Platform coverage across generative AI tools and agents, Windows and macOS endpoints, developer tools, and 300+ cloud and SaaS apps.