AiStrike ranks the exposure.
Simbian proves it, and improves itself.
Both investigate every alert. Simbian adds an AI Pentest Agent that executes the technique, an AI Threat Hunt Agent that goes looking before an alert fires, one Context Lake™ all three agents share, and self-improving defense that resolves 92% of alerts autonomously in production.
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Both loops close. Only one of them includes the attacker.
AiStrike runs the defensive lifecycle end to end and improves it from your telemetry, your outcomes, and public intelligence. The separation is what happens outside that loop: who executes the attack, where the training data comes from, and who approves what the agents learn.
What both do
- ●Every alert investigated, not only the ones that look importantBoth reach a documented verdict with the evidence and the ATT&CK mapping attached.
- ●Detections tuned from what investigations concludedBoth run the loop from a closed case back into the rules that fired it.
- ●Response automated, with a human gate on the actions that matterBoth execute into the tools you already own and hold high-impact steps for approval.
- ●Your data stays yours, in the deployment you chooseNeither trains shared models on customer data, and both run air-gapped for the strictest environments.
Where Simbian pulls ahead
- ▸An AI Pentest Agent that executes the attackIt runs the technique in your environment and proves the path is open, then raises the severity of the SOC alert that touches it.
- ▸An AI Threat Hunt Agent that hunts before an alert firesIt returns Confirmed Threat, Suspicious Activity, Detection Opportunity, or Benign, and shows the benign explanations it ruled out.
- ▸One Context Lake™ across SOC, Threat Hunt, and PentestWhat one agent learns the others already know, and it carries the non-security context your SIEM never sees, such as runbooks, asset ownership, and HR records.
- ▸Self-improving defense, in three directionsEach agent learns from its own work, from analyst feedback, and from the other agents, with every change diffed and approved in Context Manager.
Two architectures for preemptive security operations
One improves the defense from everything your environment has already seen. The other adds a source of improvement your environment cannot produce.
AiStrikeSimbian
Core unit
AiStrikeOne AI-native platform covering the defensive lifecycle: detection engineering, triage and investigation, threat intelligence, hunting, and response.
SimbianAI SOC, AI Pentest, and AI Threat Hunt Agents on one substrate, sharing one Context Lake™.
Preemptive method
AiStrikeCorrelates intelligence with asset, vulnerability, and detection state to identify exploitable assets, ranking exposure by reachability, active exploitation, and blast radius.
SimbianExecutes the technique in the environment and demonstrates that the path is open.
Investigation
AiStrikeComposite AI combines machine learning, knowledge graphs, and LLMs to group related alerts into one root-cause thread and rebuild the timeline.
SimbianStep-by-step reasoning that picks direction at each step from Context Lake™, including offensive findings from the AI Pentest Agent and non-security context such as runbooks, asset ownership, and HR records. It can also reach out to the user on Slack or email when only a person can settle the question.
Context
AiStrikeIdentity, asset, and behavioral context correlated into each investigation, with analyst verdicts refining recommendations over time.
SimbianContext Lake spans SOC, Threat Hunt, and Pentest, so one entry reshapes every related alert without enumerating each address, host, or variant, and it holds the non-security context too: assets, identities, processes, runbooks, and the decisions your team already made.
Change control
AiStrikeApproval gates sit on response actions, and generated detections are validated before they deploy.
SimbianApproval gates sit on response actions and on knowledge. Every change to what the agents know is diffed, approved, audited, and expires if it was temporary.
Where improvement comes from
AiStrikeModels stated as pre-trained on continuous attack simulations, then improved from your telemetry, your investigation and response outcomes, your analyst verdicts, and external intelligence feeds.
SimbianAll of those, plus three directions of its own: each agent learns from its own closed work, each learns from analyst feedback, and each learns from the other agents, so a Pentest finding on an endpoint makes the SOC Agent treat that endpoint's alerts more seriously. On top of that, a war lab where Simbian authors both sides of the attack and trains against attacks that have not reached you yet.
Who writes the fix
AiStrikeDetections are auto-created and tuned from outcomes; workflows are extended by your team on a composable agent framework.
SimbianThree layers improve, not one: the agent's own investigation skills, the context and memory it reasons from, and the detection rules themselves. Simbian writes the correction and submits it for approval.
Deployment
AiStrikeSaaS and containerized cloud, with an air-gapped mode and bring-your-own-model documented for federal and critical-infrastructure deployments.
SimbianSaaS, dedicated SaaS, private cloud, on-premises, or air-gapped, with your choice of LLM.
Where Simbian separates
R1
Investigation coverage and depth
AiStrikeSimbian
Investigates every alert with no playbook to author first
Groups related alerts into one root-cause investigation
Maps every investigation to MITRE ATT&CK
Reasoning shown step by step with evidence attached
Investigation draws on offensive findings from the same platform
Non-security context in the investigation, such as runbooks, ownership, and HR records
R2
Autonomy and action
AiStrikeSimbian
Native response executed into the tools you already own
Human approval held on high-impact actions
Graduated, per-alert-class autonomy control
R3
Preemptive security: identify versus prove
AiStrikeSimbian
Ranks exposure by reachability, exploitation, and blast radius
Correlates live intelligence against your asset and detection state
Executes the technique in your environment to prove the path is open
Retests to verify a fix actually closed the path
R4
Platform breadth: offense and defense
AiStrikeSimbian
Autonomous defensive investigation
Proactive threat hunting as its own agent, with stated hunt verdicts
Offensive validation on the same platform as the SOC
One shared memory across offensive and defensive agents
R5
Context and change control
AiStrikeSimbian
Environment context reused across investigations
One context entry reshapes every related alert across SOC, Threat Hunt, and Pentest
Every change to agent knowledge diffed, approved, and audited
Temporary context carries an expiry so it cannot become a blind spot
Proposes corrections to its own investigation logic from closed cases
R6
Self-improving defense
AiStrikeSimbian
Coverage against AI-native attacks, not only writing about them
Each agent learns from its own closed work and applies it to the next case
Learning crosses agents, so a Pentest finding changes a SOC verdict
Trains against attacks manufactured outside any customer environment
Full Limited None
Frequently Asked Questions
Simbian. It investigates every alert with no playbook, then goes past the defensive loop every AI SOC platform now runs. An AI Pentest Agent executes techniques in your environment and proves which paths are open. A war lab manufactures defensive training data that does not exist in nature, and Context Manager version controls everything the agents learn. Simbian resolves 92% of alerts autonomously in production.
Both investigate every alert, tune detections from outcomes, hunt, and automate response with human approval on high-impact actions. Three things separate them. Simbian executes the attack, trains in a war lab against attacks no customer environment has produced, and puts every change to agent knowledge through a diff and an approval before the agents use it.
No. AiStrike's published surface covers the defensive lifecycle plus vulnerability prioritization and cloud exposure analysis, which rank the exposure an attacker could reach. No penetration testing, adversary emulation, or attack-execution capability appears anywhere across its 94 published pages as of September 2026. Simbian's AI Pentest Agent runs the technique, proves the path, and retests the fix.
Because the training data is manufactured, not collected. Simbian builds a synthetic company in a war lab and has one AI attack it while another defends. Because Simbian authors both sides, it knows the attacker's goal, every path tried including the dead ends, and which defensive action stopped it. Your telemetry never enters the lab.
Investigation quality and detection tuning are table stakes now. Test four other things: whether the platform executes an attack or only ranks exposure, where its improvements come from when it does not train on your data, whether changes to what the agents know are diffed and approved, and whether offensive validation runs on the same platform as the SOC.