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.
We believe defenders deserve better tooling than they've been offered so far. For those who don't buy the AI hype, and think infrastructure and data come first.
The Reality
Security tools are supposed to save you time. Instead, they've created a new kind of full-time job: managing the tools themselves.
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.
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
So you buy a SOAR. Or you build your own. Neither solves the problem you actually have.
Requires 6–12 months of dedicated engineering before a single playbook runs.
No accountability layer - they track how many playbooks ran, not whether the AI was right.
Analysts still end up context-switching across an average of 6+ dashboards per incident.
Works fine - until it breaks exactly when you are at your busiest. Technical debt dressed up as security infrastructure.
You send data to a model and get an answer, but you aren't measuring accuracy or tracking AI drift.
Stitching tools together doesn't create a unified view. Analysts still jump between every vendor console to piece together what happened.
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.
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 MSSPs actually need.
AI Gym & Review Queue
Analysts grade past sessions, and automated safety checks gate new model deployments before they reach production.
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.
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.
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.
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.
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.
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.
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.
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
The playbook logic adapts from analyst behavior. Every AI decision gets logged against a baseline. Both happen inside the same pipeline.
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
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
Comparison
SOCmate connects what you already have - SIEM, 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
One platform, every client - each environment fully isolated. No data bleeding between clients.
Different triage thresholds, playbook steps, SLAs, and HITL criteria per client.
One operations dashboard across all client infrastructures. Analysts see everything in one place.
Close incidents programmatically - no manual clicking across vendor platforms.
Each client logs in to their own scoped view - incident history, open tickets, and resolution status.
FAQ
SOCmate is a vendor-agnostic automation layer that sits on top of your existing security stack - CrowdStrike, Palo Alto, Proofpoint, Sentinel - without replacing it. Alerts are ingested, normalised, enriched with asset context, correlated across entities, and evaluated against a rules engine before a ticket is generated. Analysts receive a structured incident response runbook with an AI-written narrative, ready to act on. Every AI decision is inspectable, correctable, and tracked against a baseline.
SOCmate tracks every AI decision against a locked baseline. Analysts accept or reject suggestions with a click, and every verdict feeds into a golden set used to benchmark model performance over time. You get acceptance rates by incident type, MTTR before and after, and per-client error budgets - whether you are deploying AI for the first time or already running it and need to know if it is actually working.
SOCmate is built for SOC teams and MSSPs who are managing security operations at scale. It is particularly suited to MSSPs running automation across multiple client environments - full multi-tenancy, per-client policy overrides, and a unified cross-client operations view, without the operational overhead multiplying with every new client.
Traditional SOARs require 6–12 months of dedicated engineering before a single playbook runs, and they have no accountability layer for AI decisions. SOCmate is operational in days on your real data. Playbooks tune themselves from analyst behavior. The rules editor requires no engineering support. And every AI decision is logged, measurable, and correctable - not a black box.
Read the full SOAR alternatives comparison →Writing
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No sales pitch. No enterprise pricing games. Direct work with SOC teams who want their AI to be accountable - not just deployed.