Cyera brings considerable weight to any partnership. The AI Security Platform is valued at $9 billion, has raised more than $1.7 billion from Accel, Blackstone, Cyberstarts, Georgian, Lightspeed, and Sequoia, and counts enterprises such as Paramount, Chipotle, and Valvoline among its users. Its newest integration connects that data intelligence directly to the employee endpoint.
Bloom Security, which builds endpoint security purpose-built for the AI-native endpoint, has integrated with Cyera DSPM. The result pairs Bloom’s endpoint AI visibility and AI usage control with Cyera’s data sensitivity context. Security teams gain a way to understand, prioritize, and enforce policies on AI access to sensitive data, informed by both where AI is running and what it can reach.
A Complementary Fit
The strategic logic is complementary coverage. Cyera’s platform is built for the age of agents and secures data at rest, in motion, and in use, whether people or AI agents are working with it. Enterprises use it to control exactly what data their AI can reach and to govern what happens next. It knows which datastores contain sensitive data, including PII, PHI, PCI, credentials, and business records, and how much of each is stored.
Bloom covers the device. It monitors every AI tool and agent on an endpoint, tracking what is installed, who is using it, what it connects to, and whether the activity looks risky.
That combination matters because of where AI work now happens. Employees run dozens of AI agents, MCPs, and tools on their laptops, and those tools often connect to cloud datastores, SaaS platforms, and databases where sensitive data lives. With Bloom monitoring endpoint AI activity and Cyera identifying which datastores are sensitive, enforcement decisions draw on endpoint visibility and data context together.
What Changes for Security Teams
For organizations, the value shows up in three areas. The first is prioritization. Bloom surfaces the agent, tool, user, device, and connection target, while Cyera adds sensitivity level, data type, record count, and regulatory classification, letting teams focus on activity tied to the most sensitive data. The second is consistency, with policies enforced the same way across endpoints. The third is investigation, where teams understand the data impact of an event as they examine it.
Underpinning all three is real-time action. Teams see which AI agents are accessing which sensitive data and can block, pause, investigate, or remediate on the spot.
Classification That Reaches Runtime
For Cyera customers, the integration carries data classification into runtime enforcement at the endpoint. They can block AI access to sensitive datastores in real time as agents run. A policy blocking agents from data classified as Sensitive and above, for instance, is enforced by Bloom on every endpoint, every time.
Customers also see endpoint threats to their sensitive data in real time, and risky AI activity can be stopped before sensitive data leaves the organization. That gives Cyera’s classification a direct, active role in what happens on employee devices.
Bloom’s Fleet-Wide Approach
Bloom brings its own breadth to the arrangement. The platform offers precise control over every AI tool and the software ecosystem it introduces, assessing each against asset intelligence and the full context of the endpoint: who is using it, what else is running, and what data it can reach. Bloom prevents risky installations, fixes misconfigurations, and enforces context-aware runtime policies across the fleet.
Itay Keren, CEO and co-founder of Bloom Security, connected the integration to his own background:
“Data security has always been close to my heart, having spent a large part of my career building in this space. The challenge now is that AI agents are running on endpoints with direct access to sensitive data, and traditional endpoint security simply can’t see which agents are touching which data. Bloom monitors endpoint AI activity. Cyera knows what data is sensitive. Together, we’ve brought together endpoint visibility with data context: real-time enforcement grounded in data sensitivity.”
For security leaders deciding how to govern AI on employee devices, the pairing offers a data-informed path forward: endpoint control that understands the sensitivity of what it protects.
