Mate is a security operations company that builds an AI SOC for detection, investigation and response teams. Its agentic cyber defence system learns how an organisation is put together, then uses that knowledge so AI agents can triage alerts, hunt threats and respond with the context a senior analyst would bring.
The problem it addresses is alert volume and slow investigations in SOCs where data is scattered across many tools. Mate gives analysts agents that do the legwork while people keep control of what gets acted on.
At A Glance
| Vendor | Mate |
|---|---|
| Category | AI SOC and agentic security operations |
| Agentic AI | AI agents for investigation, hunting, detection engineering and response |
| Certifications | HIPAA, SOC 2 and ISO, with a Trust Center |
| Customers | Bridgewater, FitchGroup, AlphaSense, OCBC, Freshworks |
| Funding | $35 million Series A (July 2026) |
| Market classification | Security Operations |
What Mate Does
General frontier models lack the context a SOC needs: they do not know which servers matter, how the network is laid out or how this team handles a given alert. Mate builds that context first, by federating data from across the security stack into a single body of knowledge about the organisation that agents can rely on. Agents then do the repetitive work of a SOC (looking at every alert, hunting, writing detections and proposing responses) while analysts decide how much the agents may do on their own. Each finished investigation feeds back into the system, so the next one starts from what the team has already learned.
Products
Agentic Investigation
Agentic Investigation takes each alert from start to finish. Agents collect evidence, apply domain-specific logic and reach a verdict with a full audit trail, which lets a stretched SOC look at every alert instead of sampling. Reason mining keeps the reasoning behind each conclusion, and outcome scoring feeds results back into the system. Mate puts the effect at a reported 93% cut in mean time to respond.
Agentic Threat Hunting
Threat hunting usually depends on a few experienced people writing queries by hand. Here, hunt agents form their own hypotheses and start hunts, and analysts can also paste in a threat report or ask a question in plain language. The agents pull out indicators and techniques, pivot across connected tools and data sources rather than running one fixed query, map findings to MITRE ATT&CK and hand back a detection ready to deploy.
Agentic Detection Engineering
Detection rules drift out of date as environments change. This product creates, tunes or retires detections based on what investigations actually found, so the rule set reflects real outcomes rather than guesswork.
Contextual Incident Response
Response actions are scored on confidence, precision and reversibility before anything runs. Teams then choose one of three autonomy levels for each kind of action: recommend only, supervised, or auto-run. That gives cautious teams a way to start with recommendations and widen automation as trust grows.
Gamebooks
Gamebooks replace brittle, step-by-step playbooks with an investigation process built for AI agents. A closed investigation becomes an editable Gamebook that sets how to investigate, what evidence is needed, which actions are allowed and what approvals apply. Gamebooks fix the intent of an investigation while leaving agents flexible in how they carry the work out.
How It Works
| Component | What it does |
|---|---|
| Security Context Graph | The graph continuously maps architecture, crown jewels, exposures, activity and relationships across assets, users, business processes and data. Every fact carries a tag for source, confidence and freshness, and the graph also records analysts’ decision reasoning, network architecture and guardrails. |
| Data federation | Mate connects to everything analysts already use, from legacy systems to scripts, portals and spreadsheets. |
| Flows | Tool access sits behind Flows, so swapping one security product for another means replacing a Flow without rewriting investigation processes. |
Agentic AI Capabilities
Governing Agent Behaviour
Gamebooks define when agents must escalate, stop or ask for approval, which keeps agent behaviour inside limits the SOC has agreed. Before a custom Gamebook goes live, Mate tests it against past cases, runs it in shadow mode and waits for sign-off from the customer. Together with the three autonomy levels for response, this gives teams a staged path from agent suggestions to agent action, with an audit trail for each verdict and a record of why each conclusion was reached.
Integrations
| Area | Integrations |
|---|---|
| Collaboration | Slack |
| Identity and mail | Microsoft identity and mail, Google users and mail |
Company Background
| Founded | Early 2025. |
|---|---|
| Leadership | Asaf Wiener (Co-Founder, CEO), Oren Saban (Co-Founder, CPO) and Guy Pergal (Co-Founder, CTO). |
| Funding | A $15.5 million seed round from Team8 and Insight Partners in November 2025, followed by the Series A in July 2026, taking total funding to a reported $50 million. |
| Investors | Canaan, Team8, Insight Partners and M12. |
| Recognition | Included in Gartner Emerging AI SOC Vendor Solutions (July 2026), and the Latio Security Operations Market Report (June 2026). |
Note: This overview is compiled from Mate’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.