Monad is a security data pipeline company. It collects data from security and cloud tools, then normalises, filters, enriches and routes it before it reaches a SIEM, data lake or warehouse. It is built for security teams whose log volumes and SIEM bills keep growing faster than the value they get from the data.
The problem it solves is cost and noise: most security telemetry is stored at SIEM prices whether or not anyone ever queries it.
At A Glance
| Vendor | Monad |
|---|---|
| Category | Security Operations, security data pipelines |
| Deployment | SaaS, on-premises or hybrid |
| Agentic AI | MCP server for building and debugging pipelines from AI clients |
| Customers | Robinhood, CoreWeave, Rubrik, Saviynt, Upstart |
| Market classification | Security Operations |
What Monad Does
Security teams send large volumes of logs to the SIEM, and much of it is low value. Monad sits between the tools that produce that data and the places it is stored and queried. It decides what goes where: high-value logs go to the SIEM, the rest goes to cheaper archive storage. The company reports that filtering can remove up to 70% of data volume, and that the platform absorbs 10x spikes in log volume. The same pipelines also support moving from one SIEM to another, because data can be routed to both while a migration runs.
Products
Managed Data Pipelines
Managed Data Pipelines is the core product. It collects data from security and cloud sources, reshapes it in flight and delivers it to one or more destinations, so teams stop maintaining their own collectors and scripts. Routing rules decide which records go to which destination, and transforms can be written with Python, Go or the REST API. Universal S3 and HTTP inputs and outputs cover sources and destinations without a dedicated connector.
Schema Drift Detection
Schema Drift Detection catches the moment a source changes the shape of its data, before broken records pile up downstream. It sits alongside pipeline health monitoring and alert rules, so a team learns that a feed has changed or stopped without waiting for a missing dashboard to tell them.
Monad MCP Server
The MCP server is for engineers who would rather describe a pipeline than click through one. It lets an AI client build, route and debug pipelines on the user’s behalf, within the user’s existing permissions. Typical tasks include cutting SIEM ingest, supporting a SIEM migration, finding missing or malformed records and discovering connectors and their schemas, and the user can inspect sample records to check the result. Claude, Cursor, ChatGPT and any other MCP client can connect.
How It Works
Monad runs as SaaS, on-premises or as a hybrid. Inside a pipeline, inputs are the data sources, transforms reshape records, enrichments add context and standardise mixed data, and outputs are the destinations. A single source can feed several destinations at once, each with its own routing rules.
| Component | What it does |
|---|---|
| Organisations | Accounts are arranged as root orgs and sub-orgs with roles, and resources flow “one way across your hierarchy”, which suits groups that run several business units or customers. |
| Data protection | Data is protected with TLS 1.3 encryption, per-customer keys and a hardware security module. |
Agentic AI Capabilities
Access Control
Nothing needs to be installed. Access is switched on for each user, and the server uses two scopes, mcp:read and mcp:write, which are checked against the user’s RBAC role. A user with only the read scope can explore pipelines and sample data through an AI client without being able to change anything.
AI Activity Data
The input list includes Anthropic sources such as Claude Code, Claude Cowork, Chat Messages and Compliance Activities, so AI assistant activity can be routed to the SIEM like any other audit log.
Integrations
| Area | Integrations |
|---|---|
| Cloud inputs | AWS CloudTrail Events, AWS GuardDuty, AWS IAM Access Analyzer, AWS Secrets Manager, AWS Config Inventory, Azure Activity Logs, Google Cloud Audit Logs |
| SaaS and identity inputs | Okta Audit Logs, Duo Admin Activity Logs, 1Password Audit Events, GitHub Audit Logs, Atlassian Jira Audit Logs |
| Security tool inputs | Wiz Findings, Semgrep Findings, CrowdStrike Vulnerabilities, Abnormal Security Threats, Aikido Issues, CISA KEV |
| SIEM outputs | Splunk, Microsoft Azure Sentinel, Crowdstrike Falcon Next-Gen SIEM, Palo Alto XSIAM, IBM QRadar, Sumo Logic, Panther |
| Data lake and storage outputs | Snowflake, Databricks, Google SecOps, Google BigQuery, AWS Security Lake, AWS S3, Hydrolix, Kafka |
| Other outputs | Datadog Logs, PagerDuty, Slack, Monad OTel |
Company Background
| Leadership | Christian Almenar, co-founder and CEO. |
|---|---|
| Funding | Backed by Index Ventures and Sequoia Capital, with a reported $19 million raised by 2024. |
| Acquisitions | Acquired Tarsal, an early security data pipeline company with more than 150 integrations, in July 2025. |
Note: This overview is compiled from Monad’s publicly available information as of 3 November 2026. It is provided for information only. NHI Mgmt Group does not endorse any vendor, and nothing on this page represents the opinions of NHI Mgmt Group.