AI-era security operations center
CyberSecurityat the speed of AI
Twelve defense layers. Every automation earns trust fractionally. Every decision is auditable.
What we believe
“Accuracy over efficiency.”
If a feature improves throughput but introduces ambiguity about whether an alert was correctly handled, it does not ship. We protect critical infrastructure. The foremost metric is accuracy.
“Multi-layered defense.”
No single mechanism — AI or otherwise — is trusted alone. Every automated decision has a verification layer. Every verification layer has a human fallback.
“Fractional deployment.”
Nothing flips from off to on. Every new rule, filter, model, or automation ramps gradually, proves itself against historical data, and earns trust before it carries load.
“Solve one problem at a time.”
Each capability ships as a focused, complete solution to a specific operator pain point — not as a platform feature that requires three other features to be useful.
Today's headache
Three problems compounding into one crisis
The alert queue was already unsustainable. Coverage gaps keep widening. And the help on offer asks you to trust what you cannot inspect.
Alert fatigue is a staffing crisis
The problem
L1 analysts face thousands of alerts a day, roughly 12 minutes of manual triage each, and the vast majority are false positives. Analysts burn out clearing noise while real threats sit in the queue.
The IronSOC answer
AI-assisted filter authoring with fractional deployment and a rollback machine. Every filter earns trust against historical data before it carries production load — and rolls back automatically if accuracy degrades.
Coverage cannot keep pace with attack surface
The problem
Identity attacks account for 68.6% of threats (Expel 2025). Cloud breakout times have dropped to 48 minutes. Attack surfaces multiply faster than coverage grows, and every new tool is another silo with its own console.
The IronSOC answer
Twelve integrated defense layers — ITDR, CTEM, CNAPP, XDR, deception, and more — operating as one system with shared context, not twelve point tools with twelve dashboards.
You cannot audit what you cannot see
The problem
Every vendor claims AI triage. Almost none publish a benchmark, a held-out eval, or the model behind the decision. You are asked to trust a black box with your miss rate.
The IronSOC answer
We publish the coefficients, the benchmark, and a live API. Argue with our numbers — you can actually see them.
Try the product
Score an alert. Right here.
No demo call, no email gate, no scripted video. This console posts to the same live POST /api/triage endpoint a design partner pipes telemetry into — the model runs server-side and returns a decision with its full reasoning trace.
Want the eval behind it — held-out benchmark, confusion matrix, zero-miss tuning? Inspect the full harness.
Watch the engine think
Three alerts. Three verdicts. Full reasoning shown.
These are not illustrations — each card below is scored by the production model as this page renders, at the zero-miss operating point (τ = 0.515). The bars are the actual per-signal contributions, in logit space.
The loud one
auto-containAn agent retrieves a document carrying injection language, then reaches for a high-impact tool against sensitive data. Every signal pushes the same way.
Contained before an analyst is even paged — the tool call is held at the action layer.
The one that only looks scary
auto-closeInjection language, risky tooling — but inside a sanctioned red-team window. One strong negative signal collapses the whole score.
Closed without waking anyone. And 1 in 10 of these still gets re-read by a human — deterministically.
The quiet one
escalate to humanNo single signal screams. A service account drifts off its learned baseline, touching slightly more sensitive data than it ever has. The combination is the tell.
This is the flicker that snowballs: a human gets it with full context and starts pulling the thread.
Every weight is published. Disagree with a verdict? Inspect the coefficients and re-run the eval.
Measured, not claimed
Numbers computed from the benchmark, live
These figures are recomputed from the 144-alert AgentSOC-Bench held-out split every time this page renders, at the zero-miss operating point. Synthetic benchmark today; customer eval sets run through the same harness unchanged.
Where every alert goes — the 90/10 routing
Full methodology, model coefficients, and the interactive threshold explorer live on /proof. We publish customer-derived numbers only when a documented customer eval set stands behind them.
The twelve pillars
Everything a SOC needs, nothing it doesn't
Twelve capabilities organized around how operators actually work — from detection through response, exposure management through active defense. Each one ships as a complete solution to a specific problem.
Detect & Respond
Extended Detection & Response
Cross-layer detection across endpoint, network, email, cloud, and identity correlated in one timeline.
Identity Threat Detection
Behavioral baselining, impossible travel detection, privilege escalation tracking, and service account monitoring across every identity provider.
Detection Engineering & Validation
Version-controlled detections with automated breach simulation, MITRE ATT&CK and ATLAS mapping, and continuous validation.
Analyst Operations
AI-Assisted Filter Authoring
AI drafts suppression filters with retroactive simulation; analysts approve and deploy through Draft, Shadow, Partial, Active stages.
SOC Automation & Orchestration
Fractional-trust automation: enrichment auto, triage auto, low-risk response with 90/10 human audit, high-impact gating.
Autonomous Enrichment
AI fetches threat intel, CVE details, machine specs, and OSINT automatically — every source cited, every result timestamped.
Exposure & Posture
Continuous Threat Exposure Management
Five-phase exposure management — scope, discover, prioritize, validate, mobilize — operating upstream of detection to shrink the attack surface.
Cloud-Native Security Posture
Unified cloud security correlating posture, workload, identity, and data findings into exploitable attack paths.
Asset Context Map
Live profile of every asset: identity, function, baseline traffic, deviation indicators, and relationship graph.
Active Defense & Ecosystem
Virtual Lab & Deception
Sandbox for replaying incidents and testing detections; honeypots and canary tokens with near-zero false positive rates.
Bidirectional Integrations
Ingest from appliances and query back into them on demand for process trees, session details, and policy state.
Incident Contact Roster
Structured directory of ISPs, vendors, peer SOCs, and internal stakeholders, contextually linked to assets and incidents.
The operating loop
Six phases, every pillar working
Asset Context Map profiles your environment; CTEM scopes exposures; ITDR baselines every identity.
XDR correlates signals across layers; CNAPP monitors cloud posture; deception catches lateral movement with zero false positives.
AI-assisted filters clear noise through fractional deployment; autonomous enrichment adds context before humans see the alert.
Detection engineering surfaces coverage gaps; bidirectional integrations pull full context from source appliances on demand.
SOC automation handles low-risk containment with 90/10 audit; the contact roster connects responders to the right people instantly.
Post-incident findings feed back into CTEM scoping, detection rules, and filter tuning — the loop closes automatically.
Design doctrine
The rules that govern every feature
Accuracy over efficiency
If a feature improves throughput but introduces ambiguity about whether an alert was correctly handled, it does not ship.
Multi-layered defense
No single mechanism — AI or otherwise — is trusted alone. Every automated decision has a verification layer with a human fallback.
Fractional deployment
Nothing flips from off to on. Every rule, filter, model, and automation ramps gradually and earns trust before it carries load.
Solve one problem at a time
Each capability ships as a focused, complete solution to a specific operator pain point — not a platform feature requiring three others to be useful.
See IronSOC in action
Explore the interactive SOC — real-time threat detection, attack graph visualization, and response orchestration.
How engagement starts
Not a platform sale. A two-week decision.
You do not have to buy a SOC to find out whether we are any good. You buy a sprint — and keep everything it produces.
AI red-team sprint
Fixed scope, fixed price, two to four weeks against your production agent surface. No platform install, no procurement marathon.
Detections and filters, not a PDF
Every finding ships as a runtime detection plus a filter recommendation with rollback simulation, mapped to MITRE ATLAS and the OWASP LLM Top 10. Yours to keep either way.
Continuous defense — if we earned it
Twelve defense pillars ramp fractionally onto your telemetry, each proving itself against historical data before it carries load. Baselines learn your actors, and 10% of every AI close stays human-audited. The flywheel spins on your data.
Design partners pay — a discount buys a reference and telemetry rights, never zero. Read the full engagement model.
Get started
Ready to see a different kind of SOC?
Test the triage engine, or talk to the founding team about a fixed-scope sprint against your agent surface.