SOC operations without the overhead. AI without the black box.
A vendor-agnostic operations layer to run playbooks that dynamically tune themselves from analyst behavior. Whether you are deploying your first model or managing an existing stack, SOCmate ensures your AI is constantly monitored and measured - stopping operational overhead from scaling with your growth, from a single enterprise pipeline to dozens of client environments.
The Reality
The Modern SOC Is Caught Between Two Problems
Security tools are supposed to save you time. Instead, they've created a new kind of full-time job: managing the tools themselves.
1. Playbooks shouldn't require a dedicated engineer to maintain.
Static playbooks are broken. Building them requires a six-month engineering project, and maintaining them is a full-time job.
The moment a vendor changes an API, your logic breaks silently. Your analysts stop trusting the automation and go back to manual triage.
The Reality
Playbooks live as code in a Git repo that only one engineer understands. When they leave, the logic dies.
2. If you don't track your AI, you're just hoping it works.
Everyone is rushing to deploy AI agents, but nobody wants to talk about the fact that AI is probabilistic, not deterministic. It guesses.
When an agent hallucinates a decision or drops a critical alert, there is rarely an audit trail pointing to why. If you can't baseline your metrics before and after you plug a model in, you're flying blind.
The Reality
40–60% of AI-assisted SOC teams can't quantify their false positive rate. Most have no "before" metrics — nothing to measure the investment against.
The Alternatives
You've already tried the obvious answers.
So you buy a SOAR. Or you build your own. Neither solves the problem you actually have.
The Enterprise SOAR Route
Operational Overhead
Requires 6–12 months of dedicated engineering before a single playbook runs.
The AI Blindspot
No accountability layer — they track how many playbooks ran, not whether the AI was right.
Tool Sprawl
Analysts still end up context-switching across an average of 6+ dashboards per incident.
The DIY Infrastructure Route
Operational Overhead
Works fine — until it breaks exactly when you are at your busiest. Technical debt dressed up as security infrastructure.
The AI Blindspot
You send data to a model and get an answer, but you aren't measuring accuracy or tracking AI drift.
Tool Sprawl
Stitching tools together doesn't create a unified view. Analysts still jump between every vendor console to piece together what happened.
SOCmate
... and there is SOCmate
An automation layer that sits on top of your existing stack - regardless of your SIEM, CrowdStrike, Palo Alto, Proofpoint, Sentinel. No rip and replace. We clear the operational overhead from your playbooks and open up the AI black box.
See It In Action
From raw alert to closed ticket - with a full accountability layer on top.
Alert triage & AI suggestions
Enriched ticket with AI recommendation cards - accept or reject MITRE tags, verdict, root cause.
AI Performance dashboard
MTTR before/after, acceptance rate, error budget with CISO sign-off - the numbers no SOAR gives you.
AI Gym & Review Queue
Analysts grade past sessions, and automated safety checks gate new model deployments before they reach production.
Every AI decision is inspectable, correctable, and actionable.
Not a black box. Not a trust-and-hope deployment. Every suggestion comes with the exact source it used. Analysts accept or reject with a click - and every verdict feeds directly into your Golden Set to benchmark what comes next.
Pipeline, not patchwork.
Alerts arrive, get normalized into a vendor-agnostic schema, enriched with asset context, correlated across entities, and evaluated against a rules engine - before the ticket is generated. Your analyst receives a structured incident response runbook with an AI-written narrative, primed for action.
Rules you own.
When a rule misfires, you open the rules editor, fix the condition, verify it against your testing suite, and move on. No support ticket, no engineer on call. The logic is yours.
Multi-tenancy from day one.
A new client is a record, a CSV, and a policy delta - not a project. One global rules baseline, each client a small override. No template forks, no config sprawl, no six-week onboarding cycle.
Bring your own model.
Anthropic, OpenAI, Groq, Mistral, or a local model on your own infrastructure. One environment variable. If your data needs to stay on-prem, it does.
Accountability metrics no SOAR built.
Acceptance rate by incident type. MTTR before and after, against a locked baseline you declare. Per-client error budgets with CISO sign-off and a timestamp. The numbers you need to walk into a board meeting with an actual answer.
AI Gym - because trust is not a deployment strategy.
Analysts grade past sessions to build a golden set. A safety suite tests every update before a new model reaches production. A deployment gate blocks rollout until it passes. You deploy confidence, not hope.
Built for the agentic layer.
Every alert, ticket, IOC, correlation, and playbook session is exposed through an MCP server - so the AI agents your team is building on top have structured, queryable access to your full incident model. Not raw logs. Actual context.
When something degrades, you see it before your clients do.
How It Works
From Raw Alert to Closed Ticket - Automatically
The playbook logic adapts from analyst behavior. Every AI decision gets logged against a baseline. Both happen inside the same pipeline.
Ingestion & Enrichment
Ingest
CrowdStrike · Palo Alto · Proofpoint · any source
Normalize
Unified schema · no vendor lock-in
Internal Context
Asset · Owner · Hostname · Criticality
Triage Gate
Suppressed rules stop here
External Threat Intel
AbuseIPDB · VirusTotal · gated · no wasted calls
↓
Incident Resolution
Correlate
Related alerts grouped into one incident view · Not an avalanche
AI Playbook
Plain-language narrative · Recommended next steps · Per-client overrides
Auto-ticket
Notify customer · Compliance report
Audit Trail
Full audit trail · timestamps · resolution record · NCA · NIS2 · ISO 27001 · GDPR · any framework
SOCmate vs. Everything Else
Traditional SOAR / DIY
SOCmate
Every new scenario is a new engineering project
New incident type live in hours, not months
6–12 months before you see value
Up and running in days on your real data
Requires a dedicated engineer to build and maintain
Your existing analysts run it, no engineers
No way to measure if AI is actually working
Acceptance rate, MTTR delta, error budget - live
AI decisions are a black box
Rules engine decides. AI explains. You can read both.
SOCmate connects what you already have - CrowdStrike, Palo Alto, Proofpoint, Sentinel - and normalises it into a single AI-guided operations layer. It doesn't replace your stack. It makes your stack accountable.
Built for MSSPs
Multi-Client Operations Without the Chaos
Full Multi-Tenancy
One platform, every client - each environment fully isolated. No data bleeding between clients.
Per-Client Policy Overrides
Different triage thresholds, playbook steps, SLAs, and HITL criteria per client.
Unified Cross-Client View
One operations dashboard across all client infrastructures. Analysts see everything in one place.
API-Driven Closure
Close incidents programmatically - no manual clicking across vendor platforms.
Client Portal
Each client logs in to their own scoped view - incident history, open tickets, and resolution status.
Ready to see SOCmate in action?
Drop your email and we'll set up a live demo.
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