Exaforce's AI SOC learns from your environment.
Simbian's AI SOC Agent also trains against attacks it builds.
Both investigate every alert, hunt, and contain. Simbian's AI SOC Agent reasons from one Context Lake™ it shares with an AI Pentest Agent that executes the technique and an AI Threat Hunt Agent that looks before an alert fires, so self-improving defense resolves 92% of alerts autonomously.
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Both AI SOC platforms improve. Only one trains on attacks your environment never ran.
Exaforce runs the defensive lifecycle end to end and sharpens it from your telemetry, your baselines, and your analysts' verdicts. Self-improving defense asks the harder question, which is where an AI SOC improves from when your environment has never produced the attack.
What both do
- ●Every alert investigated to a verdict, with the evidence attachedBoth reach a defensible conclusion and show the events, entities, and reasoning behind it.
- ●Threat hunting from a plain-English hypothesis, on a scheduleBoth go looking before an alert fires, without writing query language first.
- ●Containment executed in the tools you already own, behind a human gateBoth isolate hosts, revoke sessions, and suspend identities, holding high-impact actions for approval.
- ●Your data stays yours, in the region you chooseNeither trains shared models on customer data, and both offer a single tenant in a cloud account you control.
Where Simbian pulls ahead
- ▸An AI SOC Agent that reasons from proven attack pathsAn AI Pentest Agent runs the technique in your environment and proves the path is open, which raises the severity of the alert the AI SOC Agent is already working.
- ▸An AI Threat Hunt Agent that states its verdictIt 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, so offensive findings and defensive history sit in the same memory.
- ▸Self-improving defense, trained in a war labSimbian authors both the attacker and the defender in a synthetic company, so the AI SOC improves against attacks that have not reached you yet.
Two AI SOC architectures for AI-era security operations
One assembles a live model of your environment, so the answers are already there when an investigation opens. The other adds a source of improvement your environment cannot produce.
ExaforceSimbian
Core unit
ExaforceFour Exabots covering detection, triage, investigation, and response, running on a unified data layer with a real-time knowledge graph, offered as an agentic SOC platform or as a managed service.
SimbianAI SOC, AI Pentest, and AI Threat Hunt Agents on one substrate, sharing one Context Lake™.
Context model
ExaforceA knowledge graph built at ingest, where a semantic model resolves entities and relationships, a behavioral model sets what normal looks like, and a knowledge model reasons over the result, so investigative context is retrieved rather than rebuilt per query.
SimbianContext Lake spans SOC, Threat Hunt, and Pentest and holds four layers: assets and what each is worth, identities and who they belong to, processes and your runbooks, and the decisions your team already made.
Investigation
ExaforceNatural-language search and visual pivots across logs, identity, configuration, and baselines, with business context rules, HR and knowledge-management inputs, and Slack prompts that ask a user or manager to confirm intent.
SimbianThe AI SOC Agent reasons step by step, picking direction at each step from Context Lake, including offensive findings from the AI Pentest Agent alongside the non-security context, and it can reach out to the user on Slack or email when only a person can settle the question.
Where improvement comes from
ExaforceBehavioral baselines that adapt as legitimate activity changes, analyst feedback on verdicts, and historical outcomes from past detections and confirmations, all drawn from the environment being defended.
SimbianSelf-improving defense adds three directions on top of all of those: 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 a host makes the AI SOC Agent treat that host's alerts more seriously. On top of that, a war lab where Simbian authors both sides of the attack.
Who writes the fix
ExaforceDetection adapts automatically, with no hand-written rules to maintain, and analysts extend response with automation agents built in a visual editor.
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.
Change control
ExaforceApproval gates sit on response actions, scoped by sensitivity, risk, confidence, and business hours, with timeouts falling back to safe defaults and every decision audited.
SimbianApproval gates sit on response actions and on knowledge. Context Manager diffs, approves, and audits every change to what the agents know, and expires it if it was temporary.
Deployment
ExaforceMulti-tenant SaaS, or a single tenant in a cloud account you manage, contained in the region you choose.
SimbianSaaS, dedicated SaaS, private cloud, on-premises, or air-gapped, with your choice of LLM.
Where Simbian separates
R1
Self-improving defense
ExaforceSimbian
Coverage against AI-native attacks, not only writing about them
Trains against attacks manufactured outside any customer environment
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
R2
AI SOC coverage: detection, investigation, and response
ExaforceSimbian
Investigates every alert with no playbook to author first
Groups related alerts and rebuilds the incident timeline
Reasoning shown step by step with source attribution
Containment across endpoint, identity, cloud, SaaS, and email
AI SOC investigation draws on offensive findings from the same platform
Retests to verify a fix actually closed the path
R3
Proactive hunting
ExaforceSimbian
Hunts from a plain-English hypothesis, on a schedule
Threat hunting shipped as its own agent with stated verdicts
Shows the benign explanations it tested and ruled out
R4
Preemptive security: model versus prove
ExaforceSimbian
Correlates identity, configuration, and behavior into a live environment model
Executes the technique in your environment to prove the path is open
Offensive validation on the same platform as the SOC
One shared memory across offensive and defensive agents
R5
Context Lake and change control
ExaforceSimbian
Environment context reused across investigations
Non-security context such as runbooks, ownership, and HR records
One Context Lake entry reshapes related alerts 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
R6
Deployment and data control
ExaforceSimbian
Data contained in the region you choose
Single tenant in a cloud account you manage
Customer data never used to train shared models
Air-gapped or on-premises datacenter deployment
Your choice of LLM behind the agents
Full Limited None
Frequently Asked Questions
Simbian. Its AI SOC Agent investigates every alert with no playbook to author first, then goes past the defensive loop the category now shares. An AI Pentest Agent proves which paths are open, and one Context Lake carries that finding into the AI SOC. Self-improving defense trains in a war lab, and Simbian resolves 92% of alerts autonomously in production.
Both investigate every alert, hunt from plain-English hypotheses, and contain threats with a human gate on high-impact actions. Exaforce AI runs four Exabots across that defensive lifecycle. The difference is where improvement comes from. Exaforce sharpens itself on your telemetry, your baselines, and your analysts' verdicts. Simbian's AI SOC Agent also learns from a Pentest Agent that executes attacks and from a war lab that manufactures new ones.
No. Exaforce's published surface covers detection, triage, investigation, response, and threat hunting, all defensive. Across all 323 English pages in its sitemap we could not find penetration testing, red teaming, adversary emulation, or attack simulation offered as a capability, 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. Since 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 depth and natural-language hunting are table stakes across every agentic SOC platform now. Test four other things. Whether the platform executes an attack or only models the environment around it. Where its improvements come from when it does not train on your data. Whether changes to what the agents know are diffed and approved. Whether offensive validation runs on the same platform as the AI SOC, and lands in the same Context Lake.