Mate Security gives your SOC one more thing to own.
Simbian gives it one less.
Mate's Gamebooks are an investigation process your team migrates from its old playbooks, extends in plain language, and signs off on before it runs. Simbian's agents write and revise their own investigation skills, put every change through review with a before and after diff, and resolve 92% of alerts autonomously in production.
Talk to an AI SecOps ExpertIn production across 300+ enterprise environments
Both learn your environment. Only one keeps improving itself.
A durable model of the environment is the baseline for an AI SOC now. What separates the two platforms is who maintains the investigation process, whether the platform proposes its own corrections, and what else runs alongside the SOC.
What both do
- ●A durable environment model, not context rebuilt per alertBoth hold institutional knowledge that persists across investigations instead of reassembling it every time.
- ●Every alert investigated, with the evidence shownBoth reach a documented verdict and open the reasoning that produced it.
- ●Autonomy granted in stages, never switched onBoth keep humans in authority over what executes and widen scope as accuracy proves out.
- ●Knowledge that outlasts the analyst who leavesBoth capture why a decision was made, not only what was decided.
Where Simbian pulls ahead
- ▸No investigation process to author or migrateNothing to translate out of your old playbooks, extend in plain language, or approve before it runs.
- ▸A platform that proposes its own correctionsSimbian reads its own closed investigations, finds recurring blind spots, and writes the fix into the approval queue. In preview, and nothing applies until a human approves it.
- ▸Agent knowledge under version controlEvery change to what the agents know arrives as a request with a diff, an approver, and full history in Context Manager.
- ▸Offense and defense on one platformAn AI Pentest Agent proves which paths are exploitable, while the AI SOC and AI Threat Hunt Agents share one Context Lake.
Two architectures for the agentic SOC
One hands your team a process to keep current. The other keeps itself current, and puts every change through review.
Mate SecuritySimbian
Core unit
Mate SecurityA Security Context Graph of mined memories, with agents that traverse it on every investigation.
SimbianAI SOC, AI Pentest, and AI Threat Hunt agents on one substrate, with the AI SOC and AI Threat Hunt Agents sharing one Context Lake.
Investigation process
Mate SecurityGamebooks: an investigation intent your team brings over from existing playbooks, extends in plain language, and signs off on, with agents dynamic inside each step.
SimbianNo process object at all. The agent reasons per alert and consults context to choose direction at each step.
Context
Mate SecurityMemories mined from tools, tickets, SOPs, and chat, with confidence that decays and a graph that rebuilds as the environment changes.
SimbianContext Lake holds generalised context, so one entry reshapes every related alert instead of enumerating each address, host, or variant.
Who keeps it current
Mate SecurityYour team extends and approves Gamebooks; detections are tuned from investigation outcomes.
SimbianThe platform writes and revises its own skills, and proposes its own corrections from its closed investigations.
Change control
Mate SecurityCustomizations run through Mate's evals and tests before they go live.
SimbianContext Manager tracks every knowledge change as a request with diffs, an approver, and an audit trail, and a conflicted request is never applied.
Autonomy
Mate SecurityAutonomy earned per category under guardrails enforced outside the agent's reasoning loop, with rollback to supervised mode when accuracy drifts.
SimbianRead-only, Dry-run, Guided, or Autopilot, set per alert class.
Learning loop
Mate SecurityCD/CR: closed investigations compress into new and tuned detections.
SimbianClosed investigations also revise the agent's own investigation skills. That capability is in preview, and applies only once a human approves the change.
Domain breadth
Mate SecurityThe published platform runs the defensive lifecycle: know, hunt, investigate, act, and adapt.
SimbianOffense and defense from one vendor on one platform, with an AI Pentest Agent proving which paths are exploitable.
Deployment
Mate SecurityDelivered as a cloud platform, built on AWS and sold through AWS Marketplace.
SimbianSaaS, private cloud, on-premises, or air-gapped, with your choice of LLM.
Where Simbian separates
R1
Investigation coverage and depth
Mate SecuritySimbian
Investigates every alert in the queue, including informational ones
Reasons through an alert with no matching template
Reasoning shown step by step with evidence attached
R2
The investigation process
Mate SecuritySimbian
Runs with no investigation process to author, migrate, or approve
Keeps working when a tool in the stack is swapped out
Revises its own investigation skills as the environment changes
R3
Context and change control
Mate SecuritySimbian
A durable environment model reused across investigations
Context generalises across related alerts rather than per artifact
Every change to agent knowledge diffed, approved, and audited
Proposes corrections to its own investigation logic from closed cases
R4
Autonomy and action
Mate SecuritySimbian
Native response executed against the tools you already own
Graduated, per-alert-class autonomy control
Publishes an autonomous resolution rate from production
R5
Platform breadth: offense and defense
Mate SecuritySimbian
Autonomous defensive investigation
Proactive threat hunting
Detection coverage tuned from investigation outcomes
Offensive validation of exploitable paths on the same platform
R6
Deployment and data control
Mate SecuritySimbian
Deploys with no playbooks to build first
On-premises or air-gapped deployment
Bring your own LLM to your own endpoint
Choose the region your data and model calls stay in
Full Limited None
Frequently Asked Questions
Simbian. Its agents investigate every alert without a playbook, then write and revise their own investigation skills instead of leaving your team a process to maintain. Every change to what the agents know is diffed, approved, and audited in Context Manager. It deploys on-premises or air-gapped with your own LLM, and resolves 92% of alerts autonomously in production.
Both build a durable model of your environment and investigate every alert against it. Mate ships Gamebooks, an investigation process your team migrates from existing playbooks, extends, and signs off on. Simbian has no process object: the agent reasons per alert, revises its own skills as your environment changes, and runs an AI Pentest Agent and an AI Threat Hunt Agent on the same platform.
No. There is nothing to translate, extend, or approve before Simbian investigates. Your existing playbook logic goes in as context rather than as a process the platform executes, and the agent decides how to investigate each alert from there. Nothing binds it to those steps, and nothing needs rebuilding when you swap a tool in your stack.
A graph models your environment. Context Lake changes the investigation. One generalised entry, such as traffic to a given region being normal for a subsidiary, reshapes every related alert without enumerating each address or variant. Every entry is version controlled in Context Manager with a diff, an approver, and full history, so agent knowledge is reviewable the way code is.
Investigation quality is the baseline now, so test four other things: whether you have an investigation process to migrate and maintain, whether the platform proposes its own corrections from closed cases, whether every change to agent knowledge is diffed and approved, and whether offensive validation runs on the same platform as the SOC.
See how Simbian stacks up
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