Many Threats One Solution
Data Sources & Environments
Detection Capabilities
Threat Detection
Identity & Access
Business & Operations
Multi-Cloud Detection Use Case
Every major cloud provider ships built-in threat detection — AWS has GuardDuty, Microsoft’s Azure has moved Sentinel under their Defender offering, and Google has Security Command Center. They work well enough inside their own walls. But they were built to protect their cloud, not yours-across-three.
When you try to stretch them across environments, you hit real limits. Sentinel can ingest AWS and GCP data, but those sources are second-class citizens — different schemas, separate parsers, and detection rules that were written for Azure first. GuardDuty and SCC don't even try. And Google SecOps, the strongest streaming architecture of the three, caps multi-event detection rules at 75.
Meanwhile, the attacks that actually matter don't respect cloud boundaries. A compromised identity in Azure AD that pivots to an AWS S3 bucket through a GCP-hosted application looks like three unrelated low-priority events — unless you can correlate them in real time across sources.
How spotr.io does it?
spotr.io treats every data source as first-class. AWS CloudTrail, Azure Activity Logs, GCP Audit Logs, on-prem syslog — all normalized on stream, enriched in flight, and evaluated against thousands of concurrent detections sub second. No 75-rule cap. No cross-cloud schema headaches. No trusting one cloud vendor's security tool with a competitor's data.
One engine. Any source. Real-time detection that doesn't degrade the further you get from a single vendor's ecosystem.
High Velocity Data Use Case
The Data That’s Too Expensive For Your SIEM Still Needs Detection
HTTP, DNS, Windows Events — Three of the highest-value, highest-volume data sources in any enterprise. They're where attackers live: C2 beaconing, DNS tunneling, credential abuse, lateral movement, living-off-the-land.
Your SIEM either drops them, samples them, or sends them to cold storage because the volume would blow up your license. Data that never gets a single detection run against it. You're paying to collect it and then deliberately not using it.
How spotr.io does it?
spotr.io doesn't store the data. It detects on the stream — in real time, as it flows. Tens of thousands of detections running against every event. Signals fire in under a second. The raw data routes wherever you want afterward. spotr.io already found what matters.
The Conversation
"What data are you dropping before it hits your SIEM?" If the answer is HTTP, DNS, or Windows — that's the conversation.
CDN Logs Use Case
Your Easiest Path To Web Threat Detection
Every request to your web infrastructure already flows through your CDN — Cloudflare, Akamai, CloudFront, Fastly. Structured access logs are already being collected: source IPs, URLs, user agents, response codes, geo, everything. You're just not using them.
That's where credential stuffing, API abuse, bot activity, web app attacks, and account takeover patterns live. The front door — and nobody's watching it.
How spotr.io does it?
spotr.io takes CDN logs directly off the stream. No agents, no new infrastructure, no parser project. Flip on log streaming, point it at spotr.io — sub-second detection, enriched inline, sunk to your data lake ready for hunting.
The Conversation
You're already paying for the CDN. The logs are already there. Fastest time-to-value in security.
IoT / OT Use Case
Your Factory Floor Has More Endpoints Than Your Office. And Zero Detection.
The average manufacturing plant runs 5,000+ connected devices — PLCs, HMIs, SCADA systems, sensors, cameras, badge readers. Critical infrastructure runs 10x that. None of it sends logs to your SIEM. Most of it can't run an agent. It speaks Modbus, BACnet, DNP3, OPC-UA — protocols your security stack has never heard of.
But attackers have. Triton targeted safety systems at a petrochemical plant. Industroyer took down Ukraine's power grid. PIPEDREAM was built to attack ICS/SCADA across multiple industrial protocols. These weren't IT attacks that spilled into OT. They were purpose-built to exploit the monitoring gap.
Your IT SOC is blind to OT. Your OT team monitors for process safety, not security. The gap between them is where nation-state actors live.
How spotr.io does it?
spotr.io doesn't care what protocol your data speaks. Schemaless ingestion means Modbus registers, SCADA telemetry, BACnet property reads, OPC-UA node values, and syslog from industrial firewalls all flow through the same detection engine. No data transformation. No schema mapping project. Plug it in, detect on it.
Anomaly models catch behavioral drift — a PLC that's issued the same command pattern for 3 years suddenly doing something different. Threshold models catch volumetric anomalies — a sensor polling rate that doubles overnight. Sequence models catch attack playbooks — reconnaissance of the engineering workstation → credential access → controller reprogramming. Rate models catch communication pattern changes — devices talking to new endpoints or at unusual frequencies.
Your SIEM can't ingest it. Your EDR can't install on it. Your NDR only sees network patterns. spotr.io detects on the data itself — in real time, at any volume, in any format.
The most critical infrastructure has the least detection coverage. That's the gap.
API Security Use Case
Your APIs Handle More Traffic Than Your Website. And Nobody's Watching.
APIs are the new perimeter. They carry authentication tokens, customer data, payment flows, and business logic — and they're the fastest-growing attack surface in every organization. The Optus breach — 10M customer records stolen through an unauthenticated API endpoint. The T-Mobile breach — 37M records exfiltrated via API abuse over weeks. Peloton, Facebook, Parler — all API-first breaches where the attacker never touched a firewall.
Your WAF sees HTTP requests. Your SIEM sees access logs — if you're even ingesting them. Neither understands API semantics: who's calling what, how often, in what order, and whether the pattern looks like a customer or an attacker enumerating every record in your database.
How spotr.io does it?
spotr.io treats API logs as a first-class data source. High-volume, high-cardinality, streaming — exactly what we're built for. Track distinct endpoints hit per API key. Detect enumeration patterns — sequential ID walking, pagination abuse, response size anomalies. Flag credential stuffing against auth endpoints at any scale. Catch business logic abuse — a user accessing 10,000 other users' records one at a time, staying just under rate limits.
Threshold models catch brute force and volumetric abuse. Anomaly models catch behavioral deviation — an API key that normally makes 50 calls/hour suddenly making 50,000. Sequence models catch multi-step API attacks — auth → enumerate users → extract data → exfil. Rate models catch slow-and-steady scraping that stays under per-request limits but aggregates into massive data theft.
Your WAF blocks known-bad payloads. spotr.io detects unknown-bad behavior.
The next big breach won't come through your front door. It'll come through your API.
Pipeline Friendly Use Case
Most enterprises already have a data pipeline: Cribl, Fluentd, Vector, Logstash — routing, transforming, and filtering data before it hits the SIEM or lake. These tools are great at moving data. They do not detect threats.
Cribl's #1 use case is reducing volume before Splunk to control license costs. That means dropping data. The VPC flow logs, the DNS queries, the Sysmon event types that are too expensive to ingest — they get routed to cold storage or /dev/null. You're already paying to collect this data. You're just throwing it away before anyone looks at it.
What survives the filter goes to the SIEM for batch detection. What doesn't survive goes to the data lake for compliance — with zero detection. The pipeline is efficient. The detection has gaps the size of everything you dropped.
How spotr.io does it?
spotr.io plugs into any point in the pipeline — before your SIEM, after your SIEM, or alongside it. Native integration with Kafka, Cribl, Vector, Fluentd, Logstash, syslog, HTTP, S3, Snowflake, Databricks, Splunk, Elastic, Sentinel.
Three deployment positions, one architecture:
Before SIEM: Sources → spotr.io → SIEM/Lake. Detect on the stream first, then forward enriched data downstream. Your SIEM becomes a search tool, not a detection engine.
In the pipeline: Sources → Cribl/Kafka → spotr.io. Tap the stream in parallel. No disruption. No migration. The data Cribl was about to drop? spotr.io detects on it first.
After SIEM: Sources → SIEM/Lake → spotr.io. Forward from your existing infrastructure. Add real-time detection to data you've already collected.
No rip-and-replace required. Keep your pipeline, keep your SIEM, keep your lake. Add real-time detection to all of it.
Our pipeline, your pipeline, any pipeline….
The Conversation
"What data are you routing to cold storage without any detection?" — If they're using Cribl, the answer is "a lot."
"You're already paying to collect that data. What if you could detect on it before you drop it — for a fraction of your SIEM cost?" — That's the Cribl play.
_"Do you need to replace anything to deploy spotr.io?"_ — No. It plugs in wherever you are today.
Sequence Use Case (SIEM)
Attacks aren't single events — they're sequences. Phish → macro execution → C2 callback → credential dump → lateral movement. Each step looks benign on its own. The attack only becomes visible when you see the chain.
SIEMs detect one event at a time. No memory of what came before, no awareness of what should come next. Threshold rules can count ("5 failed logins") but can't express ordering ("login from new geo, THEN privilege escalation, within 10 minutes, by the same user"). And they can't express negation — "this happened but that didn't follow" — which is often the strongest signal that something is wrong.
The result: analysts manually reconstruct attack chains during investigation, hours after the fact. The detection fired on step 4. Steps 1-3 were invisible.
How spotr.io Does It
Sequence detection models define multi-step, ordered event chains with time windows, entity correlation, and negation — all evaluated in real time on the stream. "Login from new geo → privilege escalation → no MFA challenge → data access" becomes a single detection model, not four separate rules stitched together by a human.
Cross-source correlation means a sequence can span auth logs, endpoint telemetry, network data, and cloud events in one model. The state engine tracks active sequences as they unfold — firing the moment the chain completes, not on the next scheduled search.
The AI SOC Analyst presents the full chain as a single narrative signal: here's what happened, in what order, across which systems, and why it matters.
The Conversation
"Can your SIEM detect a multi-step attack as a single detection?" — Almost none can natively.
"Can it express ordering, negation, and cross-source correlation in one rule?" — That's where they go quiet.
"What if every kill chain was one signal instead of a week-long investigation?" — That's spotr.io.
Sequence Use Case (multi tool comparison)
Attacks are multi-step. Phish → macro → C2 → credential dump → lateral movement → exfil. The kill chain crosses every boundary — endpoint, network, cloud, identity. But every tool in the stack only sees its piece.
EDR sequences within the endpoint — blind to network and cloud. NDR sees traffic — can't link it to the process or user. XDR promises to unify but only sequences within one vendor's ecosystem. CrowdStrike XDR sees CrowdStrike data. Palo Alto XDR sees Palo Alto data. Cross-vendor? Still your analyst, manually, hours later.
SIEMs are worse — single-event rules with no ordering, no negation, no state. They can count ("5 failed logins") but can't express "A then B then NOT C, within 10 minutes, by the same user." And they run on a schedule, not in real time.
The result: each tool fires its own alert on its own step. Nobody detects the chain. Your analyst reconstructs it by hand across 3-4 consoles — if they catch it at all.
How spotr.io Does It
Sequence detection models define multi-step, ordered event chains with time windows, entity correlation, and negation — all evaluated in real time on the stream. Vendor-agnostic. Auth logs, endpoint telemetry, network data, cloud events, identity systems — one model, one detection, across all sources.
Negation is native: "login from new geo BUT no MFA challenge" detects what didn't happen. Time windows enforce ordering: "A before B, within X minutes, by the same entity." Cross-source correlation means a single sequence can span Sysmon events, firewall logs, Okta auth, and AWS CloudTrail.
The state engine tracks active sequences as they unfold in real time. The detection fires the moment the chain completes — not on the next hourly batch run.
The Conversation
"Can any single tool in your stack detect a multi-step attack across endpoint, network, cloud, and identity?" — Almost always no.
"Can it express ordering and negation in one rule?" — That's where they go quiet.
"How long does it take your analysts to reconstruct a kill chain across your tools?" — Hours. If they catch it at all.
"What if the kill chain was one detection, across all sources, in real time?" — That's spotr.io.
Anomaly Detection Use Case
Beyond Pattern Matching
The detection conversation always starts with rules. "How many Sigma rules do you support?" "Can you import our Elastic detections?" It's the first question everyone asks — and it's the wrong place to stop.
Sigma rules are pattern matching — filters that look for known-bad strings and field values. They're the simplest form of detection. Elastic rules go further — most convert easily, and even their ML-based detections can be deciphered and rebuilt as transparent, tuneable models. Splunk correlation searches convert too.
But all of this is the tip of the iceberg. The attacks that matter — credential theft chains, slow beaconing, lateral movement, privilege escalation, insider threats — don't match a pattern. They match a behavior.
How spotr.io does it?
Of course we run all the Sigma rules. All 3,000+. Concurrently. Sub-second. We convert Elastic rules — including their ML detections, rebuilt as transparent models you can actually tune. Splunk correlation searches too. That's day one. That's table stakes.
What's below the waterline:
Threshold & Aggregation — Count, rate, cardinality, group-by on the stream. Not in a scheduled query an hour later.
Statistical Anomaly Detection — Z-score, deviation, MAD, CUSUM, jitter. Detecting what's abnormal without writing a rule for every scenario.
Sequence & State Models — Multi-step attack chains with ordering, negation, and cross-source correlation. Real-time state tracking.
Behavioral Learning — New terms, rarity, trend analysis. Continuously adapting baselines per entity. "This has never happened before" is a detection.
Autonomous Coverage + AI SOC Analyst — Discovery → auto-deployed detections → automated triage. No human in the loop until the signal is ready.
Pattern matching is where detection starts. It's not where it ends.
The Conversation
"We already have Sigma rules." — Great. So do we. All of them. But what's running underneath?
"Can you import our Elastic detections?" — Yes. Including the ML ones — rebuilt as transparent models instead of black boxes.
"What detections do you have that rules can't express?" — That's the real conversation.
High-Cardinality Aggregation Use Case
A Million Unique Values. One Detection Engine. Zero Lag.
Your SIEM chokes on cardinality. Count distinct users per source IP? Fine at 100. At 100,000? The query times out, the cost explodes, or the search just... doesn't run. So you do what everyone does — you lower the fidelity. Fewer group-bys, wider time windows, less granularity. You trade detection quality for query survival.
That's why port scans across /16 subnets go unnoticed. Why slow credential stuffing against thousands of accounts flies under the radar. Why DNS beaconing across hundreds of domains blends in. The data is there. The SIEM can't count fast enough.
How spotr.io does it?
spotr.io aggregates on stream — no query, no storage, no cost cliff. Cardinality of the group-by key doesn't matter because we're maintaining state as events flow, not scanning a warehouse after the fact. Track distinct destination ports per source IP across your entire network. Count unique failed-auth usernames per origin across every identity provider. Measure DNS query entropy per host across millions of queries. All in real time. All simultaneously.
This unlocks detections that batch systems literally cannot run: cardinality spikes (a host suddenly resolving 500 unique domains in an hour), low-and-slow aggregation (50 failed logins spread across 50 accounts from one source over a week), and ratio-based anomalies (one user generating 10x the distinct connections of their peer group).
SIEMs scale by paying more. spotr.io scales by design.
When the group-by has a million keys, batch is bankruptcy. Streaming is just Tuesday.
UEBA Use Case
Traditional UEBA (User and Entity Behavior Analytics) (Exabeam, Securonix, Gurucul) is a separate product bolted onto your stack. You ship the same logs to a second platform, wait 30-90 days for baselines to build, then get opaque risk scores from a black box ML model that nobody can explain or tune. Most orgs stop trusting it within a year. It becomes shelfware.
How spotr.io does it?
Behavioral detection is built into the streaming engine — not a separate product. Statistical models (zscore, deviation, trend, anomaly, new_term, rarity) run continuously on the stream alongside every other detection type. No 90-day learning period. No duplicate data ingestion. No extra license.
Every behavioral detection is transparent and tuneable — your team can see what triggered, why, and adjust the thresholds. It's one layer in the detection hierarchy, not a standalone oracle.
The AI SOC Analyst correlates behavioral signals with threshold, sequence, and filter detections to build full-context signals. "This user deviated because X, correlated with Y" — not "risk score: 87."
The Conversation
"How long did it take your UEBA to start producing results you trust?" — Most will say months, or admit they never got there.
"Can your analysts explain why a user was flagged?" — If they can't, they can't triage it.
"What if behavioral detection was built in, transparent, and producing results on day 1?" — That's spotr.io.
Enrichment Use Case
Context Before Detection, Not After
In a traditional SIEM, enrichment happens at alert time — after the detection already fired. Lookup tables join GeoIP, threat intel, and asset data onto the alert as decoration. The detection ran on raw fields. The context arrived later. That's backwards.
Pipeline tools give you the ability to do your own enrichment — lookup functions, CSV joins, field mapping. But you're building and maintaining it yourself. You pick which fields to enrich, you upload the lookup files, you keep them current. And the tools don't understand what's in the data — you have to tell them which field is an IP, which is a user, which is a hostname.
Even when pipeline enrichment works, the enriched data still flows to a SIEM that detects on a schedule. Enriched data + batch detection = enriched data detected late.
How spotr.io Does It
spotr.io discovers the semantic types in your data — "this field is an IP, this is a hostname, this is a user principal" — and auto-enriches based on what it finds. No field mapping. No configuration. The data tells spotr.io what it is.
If the enrichment is generally useful — GeoIP, ASN, threat intel feeds, entity resolution, reputation scoring — spotr.io brings it and maintains it. Built in, updated, running. Not a DIY project.
If you have your own enrichment — asset inventories, business unit mappings, custom context tables — you can bring those too and layer them in alongside the built-in enrichment.
Enrichment happens on the stream, inline, before detection. That means "login from sanctioned country" IS the detection — not a footnote on an alert. "Connection to known-C2 infrastructure" fires because the threat intel is already part of the event when the evaluator sees it. Context becomes criteria.
The AI SOC Analyst receives signals that are already fully enriched. Triage starts with context, not a raw IP that needs manual lookup.
And the enriched data doesn't stop at detection. Pre-enriched events flow to every downstream sink — your SIEM, your data lake, your SOAR, Kafka, syslog, webhooks. Enrich once on the stream, use everywhere downstream. Every system in your stack gets better data.
The Conversation
"Who's building and maintaining your enrichment — you or the platform?" — If it's you, that's overhead you don't need.
"When does enrichment happen in your pipeline — before or after detection?" — If it's after, your detections are running without context.
"What if the platform discovered what's in your data, enriched it automatically, let you layer in your own context, and forwarded the enriched data everywhere?" — That's spotr.io.
Detection Efficiency Use Case
One attack. Five detection layers. One engine.
Not every threat needs machine learning. Not every pattern needs a rule. The Detection Efficiency Ladder shows how spotr.io applies the right detection technique at the right cost — from simple pattern matching to behavioral learning — all running simultaneously on a single stream.
The Scenario: A credential theft campaign unfolds over 72 hours. An attacker phishes a credential, brute-forces access across services, downloads anomalous volumes of data, moves laterally across the network, disables security controls, and exfiltrates to an external cloud provider.
Each rung of the ladder catches what the one below can't:
Rung 1 — Simple Filter Match(cheapest, sub-ms)
The phishing domain hits a known-bad IOC. Pure pattern matching. Every platform does this — it's table stakes. spotr.io catches it in under a second. Your SIEM catches it on the next scheduled search.
Rung 2 — Aggregation / Threshold(stateful, low cost)
500 failed logins in 10 minutes from the stolen credential. Requires real-time counting — not a batch query an hour later. spotr.io fires at attempt #51. Your SIEM won't see it until the next cron run.
Rung 3 — Statistical / Anomaly(adaptive, moderate cost)
The compromised account downloads 15x its 30-day average. There's no static rule for "more than usual." It requires a per-user baseline maintained continuously. Your SIEM can't do this without a separate UEBA product and a 90-day warm-up.
Rung 4 — Sequence / State Models(complex, high value)
The full kill chain: phish → credential dump → lateral movement → EDR disabled → cloud exfiltration. No single event is suspicious. The ordered sequence across auth, endpoint, network, and cloud is the attack. Your SIEM has no concept of cross-source sequencing.
Rung 5 — Behavioral Learning(most expensive, highest value)
The attacker accesses AWS S3 buckets the real user has never touched in 2 years. There's no rule for "things you've never done." Only continuous behavioral learning catches novel access patterns.
The Result:
Your SIEM catches 1 out of 5. spotr.io catches all 5 — each escalating in severity, each adding context the previous layer couldn't provide.
The Principle:Always prefer the cheapest evaluation that solves the problem. The IOC match didn't need ML. The kill chain didn't need a Sigma rule. The right engine gives you every rung and picks the right one automatically.
Detection Engineer Use Case
Your Best People Are Fighting the Platform Instead of the Threats
Detection engineers are among the most skilled people in security. They understand attacker TTPs inside and out. They write complex detection logic. They map coverage to MITRE ATT&CK. And every day, their SIEM fights them at every step.
The ceiling hits fast: ~200 active detections before search performance degrades. That means triaging which rules stay on — not based on risk, but based on what the platform can handle. Every rule runs on cron — 15 to 60 minute intervals. A detection designed to catch lateral movement in real time becomes a scheduled search that fires an hour after the attacker has already moved. High-volume data sources — Windows Events, DNS, HTTP, and any high-cardinality voluminous data — get bypassed entirely because they'd blow up the license or crush the search tier. And multi-step attack chains? Good luck stitching together scheduled searches with lookup tables pretending to be state.
The result: engineers who know exactly what to detect, writing dumbed-down rules for a platform that won't let them. They have a backlog of detections they've already written but can't activate. They know the coverage gaps exist. They just can't fix them.
How spotr.io does it?
spotr.io gives the detection engineer a partner: the Detection Engineer Agent. It handles the grunt work — building, testing, and deploying detections — while the engineer guides it with their experience and expertise. Envision what you want to catch, and the agent brings it to life. Bring your custom rules and the Detection Engineer Agent will make them even better.
Every detection evaluates on the stream — sub-second, every event, no scheduling. Thousands of detections running simultaneously with no performance ceiling. Every data source is fair game — Windows Events, DNS, HTTP, and high-cardinality data — all streaming, all detectable. No more sources you "can't afford to look at."
The detection logic is full-fidelity. Multi-step sequences with ordering and time windows. Anomaly baselines per user, per host, continuously learning. Stateful correlation that tracks what's happening now, not what a lookup table cached an hour ago.
The Conversation
"How many detections did you have to turn off last quarter because search couldn't keep up?" If they have a backlog of rules they can't activate — and they will — that's the opening. "What if you could just describe what you want to catch, and the Detection Engineer Agent had it running in minutes?"
You envision it. spotr.io builds and runs it.
Ransomware Early Warning Use Case
Ransomware Doesn't Start With Encryption. It Ends With It
Colonial Pipeline — $4.4M ransom, fuel shortages across the East Coast. JBS Foods — $11M ransom, meat processing shut down nationwide. MGM Resorts — $100M+ in losses, casinos dark for days. In every case, the encryption event was the last step in a chain that started days or weeks earlier. Initial access. Credential harvesting. Reconnaissance. Lateral movement. Privilege escalation. Data staging. Exfiltration. Then — and only then — encryption.
Your EDR is optimized to catch the payload. But modern ransomware groups disable EDR before deploying it. Your SIEM runs a search an hour later — after the encryption is already running. By the time you see the alert, the damage is done.
How spotr.io does it?
spotr.io detects the attack chain, not the payload. The pre-encryption sequence is where ransomware is stoppable — and it follows predictable patterns. Credential access tools appearing on hosts that have never seen them. Lateral movement velocity spiking across the network. Service accounts authenticating to systems they've never touched. Reconnaissance commands (net group, nltest, AdFind) clustering on a single host. Shadow copy deletion. Backup service disruption. Each event alone might be noise. The sequence — correlated across identity, endpoint, and network in real time — is unmistakable.
Threshold models catch volumetric indicators like mass file renames or rapid encryption spread. Anomaly models catch behavioral firsts — a service account that's never left one server suddenly touching fifty. Sequence models catch the full kill chain: initial access → discovery → lateral movement → privilege escalation → defense evasion → impact. And because spotr.io operates on stream, detection happens in seconds — not after the next scheduled search.
EDR catches the ransomware binary. spotr.io catches the attacker building toward it.
The best time to stop ransomware is before the ransom.
Lateral Movement Use Case
They Got One Machine. Then They Got All of Them.
Every major breach has the same chapter in the middle: lateral movement. The attacker lands on one endpoint and pivots — host to host, credential to credential, network segment to network segment — until they own enough of your environment to achieve their objective. SolarWinds. NotPetya. Colonial Pipeline. The initial access was one machine. The damage was the entire network.
Your EDR sees each host in isolation. Your SIEM sees events in batches, hours apart. Neither can answer the question that matters in real time: "Is someone moving through my network right now?"
How spotr.io does it?
spotr.io correlates authentication, process execution, and network connections across every host simultaneously, on stream. A service account that's only ever touched two servers suddenly authenticating to forty. PsExec fan-out at machine speed — 15 hosts in 3 minutes. RDP sessions chaining from endpoint to endpoint like dominoes. SMB lateral file copy followed by remote service creation on the target. Each hop is a single event on a single host. The pattern only exists across hosts, across data sources, in real time.
Threshold models catch the velocity — no human admin touches 30 machines in 5 minutes. Anomaly models catch the deviation — that service account has never left its home server. Sequence models catch the technique chain — credential dump on Host A → authentication on Host B → remote execution on Host C → same pattern on Host D. Rate models catch the acceleration — movement that starts slow and speeds up as the attacker automates.
EDR protects machines. spotr.io protects the spaces between them.
The breach doesn't happen on one host. It happens in the movement.
Insider Threat Use Case
The Threat That Already Has Your Badge
External attackers have to break in. Insiders just log in. They already have credentials, access, and context — and they don't trip a single perimeter alarm. The Tesla saboteur who exfiltrated gigabytes to personal cloud storage. The Capital One engineer who pulled 100M customer records using internal access. The Twitter employees who hijacked high-profile accounts for a crypto scam. No malware. No exploit. Just legitimate access, used illegitimately.
Your SIEM sees a login. Your DLP sees a file download. Your EDR sees... nothing suspicious. Each event is normal. It's the pattern across days and systems that reveals intent — and batch queries over siloed data can't connect those dots in time.
How spotr.io does it?
spotr.io correlates behavior across identity, data access, endpoint, and network in real time. Detect when a user on a performance improvement plan starts accessing repos they haven't touched in months. Flag when download volume spikes 10x above a user's own baseline in the week after they give notice. Catch off-hours access to sensitive systems from users who've never worked late. Track credential sharing, privilege escalation patterns, and data staging across cloud storage — all as it happens, not in a post-incident forensic review.
Threshold models catch volume spikes. Anomaly models catch behavioral deviation from self and peer baselines. Sequence models catch the multi-step data staging playbook: enumerate → collect → stage → exfil. And because spotr.io maintains state continuously, low-and-slow exfiltration over weeks doesn't disappear between scheduled searches.
DLP tells you data moved. spotr.io tells you why it's suspicious.
The most expensive breach doesn't come through your firewall. It comes through your front door.
Windows Application Control Use Case
You Can’t Block Everything So Watch Everything
Every security team knows the dilemma. PsExec, PowerShell, RMMs, Sysinternals — your admins need them. Your business runs on them. But so did the attackers behind MGM ($100M+), Caesars ($15M ransom), and UnitedHealth (7M patients exposed). Same tools. Same permissions. Different intent.
You can't shut them off. Blocking PsExec breaks your ops team. Blocking RMMs kills your helpdesk. Blocking PowerShell... good luck. These tools have to run — and attackers know it.
How spotr.io does it?
spotr.io watches the applications you can't afford to block — and catches the ones that should never be running at all. Flag a banned app the instant someone fires it up. No scheduled scan. No waiting for the next audit. It ran, you know.
From there, behavioral detection models cover the full spectrum: threshold (multiple RMM tools appearing on one host), anomaly (a tool used by someone who's never touched it), rate (lateral movement faster than any human), and sequence (driver load → EDR killed → credential dump in 90 seconds). Every event involving a watchlist tool — 280+ RMMs, LOLBins, LOLDrivers, vulnerable drivers — gets tagged and tracked on stream.
Allowlisting decides what can run. spotr.io catches what shouldn't be running — and detects when what's allowed is being weaponized.
The tools have to stay. The attackers don't.
DNS Use Case
DNS is the #1 protocol attackers depend on — C2 callbacks, data exfiltration tunnels, DGA domains, reconnaissance. Every attack touches DNS. But most orgs can't afford to watch it.
A mid-size org generates 100K+ DNS queries per second. At per-GB SIEM pricing, full DNS ingestion is a budget killer. So teams drop it, sample it, or send only blocklist hits. They're 90% blind to their most critical data source.
What gets through is matched against static blocklists — known-bad domains that are always behind. DGA, DNS tunneling, slow beaconing, and first-seen domains sail right past.
How spotr.io Does It
Full DNS stream, every query, no sampling. Detection happens on the stream — no storage cost for detection. The full hierarchy runs against DNS: blocklists for known-bad, thresholds for volume anomalies, anomaly models for DGA and tunneling patterns, behavioral baselines for beaconing detection.
The AI SOC Analyst correlates DNS signals with process, user, and network context — "this host resolved a first-seen domain, then initiated an outbound connection on a non-standard port" becomes one signal, not two disconnected events.
The Conversation
"Are you ingesting your full DNS stream into your SIEM?" — Almost nobody is.
"How are you detecting DGA or DNS tunneling today?" — Most aren't.
"What if you could watch every DNS query in real time without blowing your SIEM budget?" — That's spotr.io.
Identity Use Case
Cloud accounts are now the #1 MITRE ATT&CK technique — dethroning PowerShell for the first time (Red Canary 2025). Identity attacks grew 4x year over year. The perimeter isn't the firewall anymore — it's Okta, Entra ID, and Active Directory.
But most orgs treat identity logs like any other data source: ship them to the SIEM, run a few simple rules. "5 failed logins." "Impossible travel." That's it. The impossible travel rule fires on every VPN reconnect, every airport WiFi, every phone switching from cellular to broadband. It's noise, not detection.
Worse, there's no cross-source correlation. Your IdP knows someone logged in from a new location. Your EDR knows a suspicious process ran. Your cloud logs know a sensitive resource was accessed. But nobody connects those three events into one story because they live in three different tools.
And it all runs on a schedule. A compromised credential can be used for hours before the next batch search catches the initial login.
How spotr.io Does It
All identity sources — Okta, Entra ID, AD, Duo, LDAP — stream alongside endpoint, network, and cloud data. One pipeline. One detection engine.
Cross-source sequence models connect the full chain: auth event → endpoint action → cloud resource access. Behavioral baselines learn per-user patterns continuously — login times, access patterns, privilege usage. Negation catches what didn't happen: "login from new geo BUT no MFA challenge."
Eight identity-specific detection types out of the box: credential stuffing, account takeover, impossible travel (with real context, not just geo), privilege escalation, first-time access, MFA bypass, dormant account usage, and lateral movement via identity.
The AI SOC Analyst presents identity signals with full context — who, from where, what they accessed, what's abnormal, and why it matters.
The Conversation
"Cloud accounts are the #1 attack technique. How are you detecting identity-based attacks today?" — Most will say impossible travel and failed login thresholds.
"Can you correlate an auth event with what that user did next on the endpoint and in the cloud?" — Almost nobody can.
"What if every identity event was correlated with everything else, in real time, with behavioral baselines?" — That's spotr.io.
Zero Trust Continuous Verification Use Case
Zero Trust Stops at the Door. Who's Watching After They're Inside?
Every organization is "doing Zero Trust." MFA is deployed. Identity providers verify every login. Conditional access policies check device posture. You've built a fortress at the front gate. And then... nothing. Once the session is established, the user is trusted for hours. Days. Until the token expires.
That's not zero trust. That's trust-once.
The Okta breach — attackers used stolen session tokens to bypass MFA entirely. The Microsoft Midnight Blizzard attack — compromised OAuth tokens gave persistent access across tenants. Snowflake — stolen credentials from infostealer malware used to exfiltrate data from 165+ customer environments. In every case, authentication was "passed." The post-auth behavior was where the attack lived.
Your IdP verifies identity at login. Your CASB checks posture at connection. Your SIEM reviews access logs tomorrow. Nobody is continuously evaluating whether the authenticated session still looks like the person who started it.
How spotr.io does it?
spotr.io makes zero trust continuous. Every authenticated session is behaviorally profiled on stream — what they access, when, from where, how much, and how it compares to their own history and their peers. The moment the behavior diverges from the identity, the signal fires. A developer token suddenly querying HR databases. A finance user's session accessing engineering repos at 3am from a new country. An OAuth token making API calls at machine speed from an IP it's never used.
Anomaly models catch session hijack indicators — geo-impossible travel, device fingerprint changes, behavioral deviation mid-session. Threshold models catch token abuse — access volume or breadth exceeding any human pattern. Sequence models catch the post-compromise playbook — token theft → privilege discovery → data access escalation → exfiltration. Rate models catch automated abuse of stolen credentials across multiple services simultaneously.
Authentication proves who you were at login. spotr.io proves who you are right now.
Zero trust means never trust. Not trust once.
MSSP Multi Tenant Use Case
MSSPs feel the weight of every new customer. Another instance, another license, another round of onboarding that takes weeks or more. Each customer has different tools, different portals, different ways they want to consume alerts. Operational overhead grows with every logo. Growth creates complexity.
How spotr.io Does It
Native multi-tenancy — one engine, isolated tenants, built in from day one. Schemaless ingestion means onboarding in hours: point the data and go.
Detections are deployed automatically based on the context discovered in each tenant's data. spotr.io discovers what's in the environment — data sources, field types, entities — and activates the right detection models for each customer. No manual rule deployment. No copy-paste across tenants.
The AI SOC Analyst is included — not an add-on. Every tenant gets automated triage, risk analysis, and investigation. Your analysts focus on what needs human judgment, not triaging noise across dozens of customers.
Signals feed into whatever your customers already use — their SIEM, their SOAR, their ticketing system, their homegrown portal, or yours. spotr.io is the detection engine underneath. It fits your workflow, not the other way around.
The whole package — real-time detection, AI SOC Analyst, multi-tenant management, flexible signal delivery — at a fraction of what SIEM licensing alone would cost.
The Conversation
"How long does it take to onboard a new customer today?" — If the answer is weeks or more, there's a better way.
"What if every customer got real-time detection and an AI SOC Analyst, signals delivered wherever they need them, and it cost less than your current platform?" — That's spotr.io.
M&A Use Case
Security Visibility On Day One
You acquire a company. You inherit an unknown environment with unknown tools generating unknown data. Your SIEM needs 6-12 months of parser development, schema mapping, and detection rebuilds before it sees anything. Meanwhile, you're blind — and attackers know it.
How spotr.io does it?
spotr.io is schemaless and data-agnostic. Point the acquired company's logs at it and detection starts immediately. Your existing detection models extend to the new data on day one. Cross-environment correlation — acquired company to parent — works from the first event.
No parser projects. No schema normalization. No licensing shock from doubling your data volume. No 6-month blind spot.
Who cares: PE roll-ups, serial acquirers, MSSPs, any CISO who's survived an integration.
The Conversation
"You just spent $50M on an acquisition. With your SIEM, you can see inside it in 6 months. With spotr.io, day one."
Compliance Use Case
Compliance frameworks — SOC 2, PCI DSS, HIPAA, NIST — require log retention. 12 months minimum, sometimes 7 years. So everything goes into the SIEM and stays there.
The result: 80% of your SIEM budget is storage, not detection. You're paying Splunk prices to park data that gets queried once during an annual audit. And when cost pressure hits, teams start dropping sources or shortening retention — creating the exact compliance gaps the SIEM was supposed to prevent.
Then the auditor shows up and asks for 6 months of DNS logs. You dropped them in month 2 because volume was blowing up your license. Now you have a finding.
The SIEM is trying to be two things — a detection engine and a compliance archive — and failing at both. Detection suffers because budget goes to storage. Compliance has gaps because budget pressure forces data drops.
How spotr.io Does It
Decouple detection from retention. Detection happens on the stream in real time — no storage required. Then the pre-enriched data flows to cheap storage: S3, Snowflake, Databricks — pennies per GB, retain as long as you need.
The compliance archive gets better data than the SIEM ever stored because it's pre-enriched with GeoIP, threat intel, and entity context before it hits the lake. When the auditor shows up, you have complete, enriched, searchable logs going back as far as you need. No gaps. No data drops.
Hunting and investigation happen on-demand against the lake when you need them. But detection already happened in real time — it's not gated by querying stored data.
The Conversation
"What percentage of your SIEM budget is detection vs. storage?" — Most don't know, but it's usually 80% storage.
"Have you ever dropped a data source or shortened retention to control costs?" — Almost everyone has.
"What if detection cost a fraction and compliance retention was pennies per GB?" — That's spotr.io.
MTTD-MTTR Compression Use Case
Every security team tracks two numbers: Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR). With traditional SIEMs, measuring MTTD is almost pointless — the architecture guarantees it will be slow.
Splunk's default correlation search runs on a 1-hour batch cycle. If an event arrives one second after the batch fires, it waits for the next run. Worst case: up to 2 hours before a matching rule even sees it. That's 2 hours of MTTD before MTTR can even start.
MTTR is gated by MTTD. Your SOC can't respond to what it can't see. The fastest analysts in the world sit idle while the attacker moves laterally, dumps credentials, and establishes persistence.
How spotr.io does it?
Detection (MTTD → sub-second): Events are evaluated on the stream the moment they arrive. No indexing. No scheduled search. No batch window. There is no worst case because there is no cycle.
Response (MTTR → compressed): The AI SOC Analyst automatically performs risk analysis, triage, and initial investigation. Signals arrive pre-enriched with full context — who, what, where, risk score, and recommended action. Your analyst confirms and acts. They don't hunt.
Noise (fewer, better signals): The detection hierarchy — filter, threshold, sequence, anomaly, behavioral — produces higher-fidelity signals. Group-by aggregation on the stream correlates by host, user, and process tree before triggering. Less noise means faster human decisions.
The Conversation
"What's your current MTTD?" — Most can't answer. The honest ones say 15–60 minutes. Anyone on Splunk defaults is closer to 1–2 hours.
"What happens to MTTR when detection takes an hour?" — It doesn't start.
"What if detection was sub-second and triage was automated?" — MTTD collapses. MTTR becomes the only metric that matters — and your team actually controls it.
AI SOC Token Waste Use Case
Fix the Problem, Don't Just Triage It
Third-party AI SOC tools burn tokens triaging the same alerts over and over. Noisy detection? The AI writes the same "likely false positive" summary every time it fires — 10 times, 100 times, 1,000 times. Same problem. Same tokens. Same conclusion. Nothing changes.
That's not intelligence — it's expensive repetition.
How spotr.io does it?
spotr.io's AI SOC Analyst feeds back into the detection engine. When the AI identifies a pattern — recurring false positives, a threshold that's too sensitive, a model that needs tuning — it doesn't just document the problem. It fixes it at the source.
• Noisy detection? Adjust the model, not the triage queue
• Recurring false positive pattern? Tune it once, not triage it forever
• Tokens spent investigating? They produce a permanent fix, not a disposable summary
The Conversation
"Does your AI SOC just stop working when you hit your token limit?"
"Other AI SOCs spend tokens describing the same problem over and over. Ours spends tokens once — to fix it."
Field and Entity Discovery Use Case
Know Your Data in Minutes, Not Months
Every SIEM deployment starts the same way: weeks of professional services mapping fields, normalizing schemas, defining entities. Before a single detection fires, you've spent months and thousands of dollars just teaching the tool what your data looks like.
And it's never done. Every new log source restarts the process. Schema changes break what you already built. The mapping spreadsheet becomes a full-time job.
How spotr.io does it:
spotr.io's Discovery Agent uses AI-powered entity recognition to understand your data by reading it — not by reading field names. It doesn't care whether your firewall calls it src_ip, your cloud provider calls it source_address, or your EDR calls it SrcAddr. It looks at the values and knows what they are.
• Send your data raw — no schema mapping, no normalization, no field definitions
• Fields classified automatically — AI reads values, not names. Vendor-agnostic from day one
• Entities discovered in real time — IPs, users, hosts, services identified by analyzing the data itself
• Attack surface mapped continuously — a living picture of your environment that updates as your data changes
• New log source? — discovery runs automatically. Zero-touch. No PS call required
The Conversation
"How long before your SIEM was actually useful after you bought it?"
"What used to take months of professional services — understanding your data, mapping your environment — our Discovery Agent does in minutes. Automatically."
Actions and Outputs Use Case
Seamless Signal Routing — Your Stack, Our Speed
Security teams don't need another tool to check. They need the tools they already use to work better.
How spotr.io does it?
spotr.io pushes enriched, investigated signals directly into your existing operational workflow — in sub-second time. Every signal arrives with full context: what happened, what it correlates with, why it matters, and what to do about it.
Ticketing & Collaboration — Signals automatically create prioritized tickets in ServiceNow, Jira, or OpsGenie, and post to Slack or Teams channels. Your on-call team sees a fully contextualized alert, not a raw log line they need to decode.
SIEM Integration — Already invested in Splunk or Elastic? Enriched signals flow back in, pre-correlated and deduplicated. Your existing dashboards, saved searches, and workflows just got dramatically better data — without changing a single query.
SOAR Orchestration — Tines and XSOAR playbooks trigger automatically from spotr.io signals. Block an IP, isolate a host, disable a compromised account — all within seconds of detection, not hours.
AI SOC Analyst — Signals that would normally queue for a Tier 1 analyst are autonomously investigated: risk scored, correlated across data sources, and triaged. Your team focuses on decisions, not data gathering.
Compliance & Retention — Every enriched signal and its investigation context sinks to S3, Snowflake, or Kafka for long-term retention. Audit-ready, searchable, and richer than the raw data you started with.
No new dashboards to learn. No workflow migration. No retraining. spotr.io amplifies the tools your team already trusts — it just makes them faster, smarter, and more complete.