How spotr.io AI Works
Many Agents. One Mission.
spotr.io isn't AI-assisted — it's AI-architected. Autonomous agents operate at every layer of the detection pipeline, working together to find threats faster and smarter than any human team could alone.
We Learn Your Environment. You Don't Lift a Finger.
Discovery Agent
Your data is messy. Different formats, inconsistent schemas, unlabeled fields. Traditional tools demand weeks of normalization before you see value.
The Discovery Agent changes that.
What it does:
Automatically identifies data sources as they arrive
Extracts and classifies fields semantically — not just by name, but by meaning (a src_ip, source_address, and SrcAddr are all recognized as the same thing)
Recognizes entities (users, hosts, IPs, applications) across your environment
Maps discovered fields to enrichment tables automatically — so threat intel, asset context, and identity data attach to events before detection even begins
Builds a living map of your infrastructure — continuously updated as new sources and schemas appear
Why it matters:
No onboarding delays. No professional services engagement. No manual field mapping. Your environment is understood in minutes, not months — and the moment a field is discovered, it's already being enriched and fed into detection models.
Coverage Policy Agent
The Right Detections for Your Attack Surface
You don't need 10,000 generic rules. You need the right detections for your environment — and they need to keep up as that environment changes.
The Coverage Policy Agent makes that automatic.
What it does:
Consumes everything the Discovery Agent learns — every data source, field, entity type, and schema in your environment
Continuously evaluates: "Given what we can see, what should we be detecting?"
Can activate relevant detection models the moment the data to support them appears — no manual configuration, no ticket, no delay
Maps coverage against frameworks like MITRE ATT&CK — not as a goal, but as a lens to validate completeness
Re-evaluates continuously
Why it matters:
Security environments change constantly — new data sources, evolving threats, shifting infrastructure. Detection coverage doesn't keep up. Gaps open quietly, and most teams don't have the resources to continuously re-evaluate what they're detecting and what they're missing.
The Coverage Policy Agent eliminates that drift. Your detection coverage becomes a living reflection of your actual environment — adapting as sources and threats change. It runs fully autonomously or with human approval gates — your team, your policy. Coverage that used to require a dedicated detection engineering team stays current without the overhead.
Detection Engineer Agent
Detections That Build and Tune Themselves
Writing detection logic is hard. Tuning it to reduce false positives without missing real threats? Even harder. Building detections that go beyond simple pattern matching — into behavioral baselines, statistical anomalies, and multi-stage attack sequences? That's a team of specialists most organizations can't hire, let alone retain.
The Detection Engineer Agent does all of it. And it starts with a conversation.
Watch how the Detection Engineer Agent does all the grunt work so you don’t have to.
What it does:
Takes plain English descriptions of what you want to detect — "Detect when the same user triggers a failed MFA push, a password reset, and a successful login within 10 minutes" — and builds production-ready detection models.
No query language to learn. No syntax to debug. Describe the threat, and the Detection Engineer figures out the right model type, the right data sources, and the right logic
Builds across the full detection spectrum
Pattern matching for known indicators and signatures
Threshold models that aggregate and count across time windows
Cardinality detection that spots unusual spread (one user hitting 50 hosts, one IP resolving 500 domains)
Anomaly detection that learns your environment's normal and alerts on statistical deviation — transparent and tunable, not a black box
Sequence models that track ordered chains of events across sources (credential access → lateral movement → data staging — with timing constraints and negation logic)
Selects the right algorithm automatically — you describe the what, it figures out the how
Validates detection logic against historical data before deployment — catching false positive storms before they hit your SOC
Monitors detection performance in production — precision, recall, noise levels — and auto-tunes thresholds based on your environment's evolving baseline
Can operate autonomously or with human review gates at any stage — draft, validate, deploy, tune
Detection engineering at machine scale. In plain English. Continuously.
Why it matters:
Today, building a detection requires knowing a query language (SPL, KQL, YARA-L, EQL), understanding the data schema, selecting the right detection approach, tuning thresholds, and testing against historical data. That's a senior detection engineer's full afternoon — for one rule.
The Detection Engineer Agent compresses that into a prompt. Describe the threat. Review the output. Deploy. The hard stuff — anomaly detection, behavioral baselines, multi-step correlation — is no longer stuck on the backlog. It's a conversation away.
AI SOC Analyst
Triage at Machine Speed. Context at Human Depth.
A signal fires. Now what? Traditional SOCs throw it in a queue and hope a human gets to it. AI copilots summarize the alert and suggest a runbook. Neither one actually changes anything.
Our AI SOC Analyst doesn't just respond — it adapts.
What it does:
Enriches signals with full context the moment they fire — identity, asset criticality, threat intel, historical behavior
Correlates across related events, prior signals, and active investigation threads
Assesses risk and urgency with full environmental awareness — not just "is this IOC bad?" but "is this bad here, now, for this entity?"
Recommends or executes response actions through your existing tools (SOAR, ticketing, messaging)
Tunes detection in real time — adjusting thresholds, suppressing known-good patterns, escalating detection models that need attention
Feeds learnings back to the Detection Engineer Agent — closing the loop between investigation and prevention
Watch how you can drill into any detection with a simple click.
Full control. Investigate at any time. A simple click on a signal will do it.
Why it matters:
Most AI security tools are read-only — they can see alerts but can't touch the detection engine that created them. Our AI SOC Analyst has direct control over detection configuration, enrichment tables, and response actions. It doesn't just close tickets. It makes the system smarter with every signal it processes.
And it runs autonomously. No human in the loop required for routine signals. Investigation, risk assessment, response, detection tuning — the entire workflow can execute end-to-end without anyone touching a keyboard. Your team sets the policies. The AI SOC Analyst enforces them, 24/7, at machine speed.
Time-to-respond drops from hours to sub second. Analysts aren't buried in triage — they're reviewing decisions, not making them from scratch. And the detections themselves get better over time, automatically.
The Closed Loop
These agents don't work in isolation. Discovery feeds Coverage Policy. Coverage Policy directs the Detection Engineer. Signals flow to the AI SOC Analyst and feedback makes the whole system smarter.