Identity and access telemetry
Sessions, roles, service accounts, tokens, privilege changes, and identity provider risk signals.
▸IronSOC/SOC platform
SOC platform
One command layer for the modern attack graph.
IronSOC connects telemetry, threat intelligence, vulnerability context, and response workflows so analysts can see how an attack moves and stop it before blast radius expands.
Operating layers
Built for signals attackers actually use.
Cloud and SaaS control planes
IAM mutations, public exposure, admin events, CI/CD actions, Kubernetes activity, and SaaS integrations.
Threat-led detection engineering
ATT&CK and ATLAS mapped detections for behavior across endpoint, network, cloud, and AI systems.
Threat intelligence operations
Curated CTI feeds, KEV correlation, adversary tracking, and AI-era TTP research wired directly into detections and response playbooks.
Automated response orchestration
Revoke sessions, isolate hosts, disable integrations, open evidence packages, and trigger executive workflows.
Operational outcomes
- Reduce alert noise by correlating identity, asset, and exploit context.
- Turn vulnerability queues into risk-ranked remediation work.
- Detect policy drift before it becomes an incident path.
- Preserve evidence while containment actions run in minutes.
The SOC should decide faster than the attacker can pivot.
IronSOC prioritizes what is reachable, exploited, privileged, and business-critical. That is the difference between alert monitoring and active defense.
See the operating surfaceNamed capability
IronSOC Threat Intelligence.
A SOC without threat intelligence is a SOC reading yesterday’s signals. IronSOC Threat Intelligence is the curated feed, research, and adversary tracking that drives every detection, every triage decision, and every executive briefing we produce.
Request a sample briefingCISA KEV + exploit telemetry
Known-exploited vulnerabilities, observed exploitation timing, and reachability context against your asset graph.
Adversary TTP tracking
Behavior signatures mapped to MITRE ATT&CK and ATLAS — refreshed as campaigns move and tooling shifts.
AI-era threat research
Prompt-injection patterns, jailbreak corpora, MCP/plugin abuse, agent goal-hijack — fed back into runtime detections.
Briefings and evidence packs
Quarterly executive briefs, weekly analyst briefings, and per-incident evidence packs your auditors and board can read.
Detection quality
How we measure what we detect.
A detection that nobody can evaluate is not a detection. We publish methodology before we publish numbers. When customer telemetry is in production, the same scaffolding produces continuous quality metrics.
Mapped to attacker behavior
Every detection is tagged to MITRE ATT&CK for enterprise behavior and MITRE ATLAS for AI-specific tactics. Coverage gaps are treated as backlog items, not afterthoughts.
Eval set under version control
Detections ship with positive cases (real attacker behavior) and negative cases (benign-but-suspicious activity). Promotion to production requires the eval to pass in CI.
Detection-as-code
Rules, models, and playbooks are stored, reviewed, and versioned like application code. Rollback is a revert, not a console click.
Bounded automation
Each detection declares an action mode: autonomous, approval-gated, or blocked. Authority is explicit, auditable, and visible to analysts and customers.
Signals tracked
Precision · Recall · FP rate · MTTR
Coverage frame
MITRE ATT&CK · ATLAS · OWASP LLM
Promotion gate
Eval CI · Peer review · Owner sign-off
Rollback
Git revert · Detection version pin
Data flywheel
Resolved incidents become measurable detection lift.
The proprietary asset of a SOC is not its data — it is its labeled outcomes. IronSOC turns every closed case into eval fuel for the next detection. Lift is reported per customer, quarter over quarter.
Loop runs continuously. Customers see the lift in QBR.
Step 01
Resolved incident
Every closed case carries the analyst's narrative, the actions taken, the timeline, the false-positive verdicts, and the recovery path. Nothing is summarized into oblivion.
Step 02
Labeled outcome
Each case is labeled at close: true positive, benign-explained, false positive, missed-by-time. Labels are written by analysts, audited by the customer, and version-pinned to the detection that fired.
Step 03
Retrained detector
Labels feed the eval set. Detections are re-evaluated against the cumulative set, and either improved, retired, or held with a documented decision. Updates ship through CI, never console clicks.
Step 04
Measurable lift
Quarter-over-quarter precision, recall, and time-to-contain are reported per customer. Lift is a metric on a graph, not a sentence in a deck.
Tenant isolation by default
Customer telemetry stays inside the customer tenant. We do not commingle data across tenants to train shared detectors.
Federated learnings, not federated data
When a detection improves from one tenant's labeled outcomes, only the rule and eval case (sanitized of customer specifics) propagate — never raw data.
Customer opt-in for shared eval cases
Cases that benefit the wider ecosystem are contributed only when the customer explicitly opts in, with the right to review and revoke.
Why IronSOC
One operating model — not four products with four backlogs.
The wedge: identity and AI as one surface, exploit-aware vuln ops in the detection loop, and recovery engineered before the incident. The differentiation panel against generic AI-SOC tooling lives on its own page.