LogSeam builds an agentic security operations platform that brings SIEM, SOAR, threat intelligence, detection, hunting, case management and response into one system. It is aimed at security teams that want to stop stitching separate tools together and work from a single copy of their security data.
Specialist AI agents do much of the triage and investigation work under policies the security team sets, so analysts spend their time on decisions rather than on moving data between consoles.
At A Glance
| Vendor | LogSeam |
|---|---|
| Category | Security operations (agentic SOC platform) |
| Deployment | LogSeam Managed, Your Cloud or On-Premises, single-tenant |
| Agentic AI | Specialist agents under policy, in-product assistant, MCP server |
| Certifications | Controls built to SOC 2 and ISO 27001 standards, certification in progress |
| Market classification | Security Operations |
What LogSeam Does
LogSeam set out to change how security teams analyse and respond to threats by making log data easier to reach and act on. The core idea is to separate the data from the tools that use it. Security data lands in an open data lake in the customer’s own bucket, and every capability reads from that lake rather than keeping its own copy.
Detection, investigation and response then happen in one place, on the same data. Long retention stays searchable, because history lives in the lake rather than in an expensive hot tier.
Products
Pipeline
The Pipeline makes data usable the moment it arrives. It catalogues incoming data, recognises and parses most standard formats automatically, and accepts data from an agent, through an API or straight from a storage bucket. A component called Rosetta Stone looks at the actual values in a field instead of trusting the header. Enrichment adds threat intelligence, identity provider and asset inventory context, and tiering sends hot data to fast storage and bulk data to cheaper storage.
Lake
The Lake is the open data foundation: the complete record, held in open formats and always queryable. The Lake does not depend on the rest of the platform, so the data remains usable on its own, while every other layer depends on the Lake.
Compute
Compute scales with the load. It is put together for each query or investigation and released afterwards, so capacity follows the work.
AI And Work
The AI and Work layers are where analysts and agents do the SOC’s job:
- Investigation canvases linking evidence, entities, timelines and hypotheses
- Incident response cases showing attack progression, response plans and scope of compromise
- Link analysis from an internal address out to users, URLs, destination IPs and countries
- SOC workflows built from the team’s operating procedures
How It Works
| Component | What it does |
|---|---|
| Deployment options | LogSeam Managed runs in LogSeam’s cloud. Your Cloud runs on AWS, GCP, Azure or OCI. On-Premises runs on the customer’s own hardware and can be fully air-gapped. All three have the same features, and every deployment is single-tenant on dedicated infrastructure. Regional deployments can keep data within EU regions. |
| Data custody | Each deployment uses the customer’s own object storage. LogSeam processes the data but never takes custody of it. |
| Encryption | Data is encrypted at rest with AES-256 and in transit with TLS 1.3. |
| Model access | Customers choose the model that fits their data policy, and privately hosted models can run the same agent harness on the customer’s infrastructure. Agents pass models the context they need without redaction, which is why model choice is left to the customer. Model context and responses are cached so repeated work is not sent again. |
Agentic AI Capabilities
Specialist Agents
Specialist agents hunt, investigate, engineer detections and prepare response across the pipeline, lake, compute and work layers. Two controls govern them. A doctrine records the team’s rules of engagement and enforces them, and a harness defines which tools each agent may use and how far it can go before a human has to step in. An agentic SOC view breaks down agent activity and cost by capability and model.
Assistant
The assistant writes detection rules and holds each one for review before it is deployed. It runs queries and suggests follow-ups. It also builds investigation canvases, seeds entities, generates reports and runs ad hoc queries, such as plotting failed logins by source country.
MCP Server
An MCP server lets external AI agents and MCP clients work with the platform.
Integrations
| Area | Integrations |
|---|---|
| SIEMs | Splunk, Sentinel, QRadar, among more than 100 integrations |
| AI clients (MCP) | Claude, Cursor |
Company Background
| Origins | Founded by a team with decades of front-line security operations experience. |
|---|---|
| Leadership | Daniel Wiley, Co-founder and CEO; Billy Forester, Co-founder and CTO. |
| Compliance | GDPR, HIPAA, PCI DSS and CCPA status is available on request. |
Note: This overview is compiled from LogSeam’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.