Fintech vendor with sensitive customer data
A London fintech gets UEBA baselines on database access patterns, catching a compromised employee credential being used to pull unusually large data exports outside normal hours.
AI Threat Detection & UEBA in the UK
CyberSecurity · United Kingdom
AI threat detection in the UK from 4hosting builds a behavioural baseline for your users and hosts, then flags genuine deviation rather than every event that technically looks unusual. It combines UEBA, ML-based endpoint detection, network anomaly detection and honeytoken deception, with AI-assisted triage keeping the alert volume something an engineer can actually act on.
- UEBA baselines flag real deviation, not every anomaly
- ML-based EDR plus network detection, not signatures alone
- AI-assisted triage cuts false positives before human review
- Users, hosts and network trafficDetection scope
- 1–8 h/month on VPS; 5–10 h/month on dedicated servers depending on the plan; 10 h/month on GPU serversIncluded hours
- Frankfurt, in the EUData residency
- EUR, with UK VAT (20%) itemised on the invoiceBilling
01
What UEBA actually catches
User and entity behaviour analytics builds a baseline of what normal looks like for each user and host — typical login times, typical data volumes, typical access patterns — then flags activity that deviates from it. This catches compromised credentials being used in ways the legitimate user never would, and insider activity that no signature-based rule would ever trigger on, because nothing about a single action looks malicious in isolation.
- Baselines built per user and per host, not one global threshold
- Flags credential misuse that doesn't match the legitimate user's pattern
- Surfaces insider activity signature-based detection misses entirely
02
EDR, network detection and deception together
ML-based endpoint detection watches process behaviour on the host itself, catching techniques that don't match a known malware signature. Network detection (NDR) does the same at the traffic level, flagging command-and-control patterns and unusual lateral movement. Honeytokens — fake credentials or files placed specifically to be touched only by an attacker — add a high-confidence signal with essentially zero false positives, since no legitimate process should ever access them.
- ML-based EDR on process behaviour, not signature matching alone
- NDR flags anomalous network traffic and lateral movement
- Honeytokens: near-zero false positives when triggered
03
Why AI-assisted triage is the difference
Behavioural detection at this depth produces far more raw alerts than signature-based detection alone, and attackers increasingly use AI-assisted tooling that behaves just plausibly enough to slip past a naive threshold. AI-assisted triage correlates alerts across sources, ranks them against MITRE ATT&CK-mapped attack patterns using Sigma rules, and cuts the volume down to what an engineer should genuinely review — which is what keeps this practical rather than a firehose nobody has time to act on.
04
Where the included hours go
Detection tuning and threat-intel integration draw on the CyberSecurity hours in your plan: 1–8 hours a month on VPS, 5–10 on dedicated servers depending on the plan, 10 on GPU servers, from Advanced upward on cPanel. You request detection setup, a new baseline, or Sigma rule tuning from the client panel, propose a timeframe, and we deliver against it. Hours do not roll over into the following month, so it pays to plan a baselining exercise deliberately rather than delaying it; work beyond the monthly quota is still accepted, billed separately.
How we work
Request from the panel
Open a request under Services describing which users, hosts or network segments you want under behavioural monitoring.
Build the baseline
We collect activity data to establish a normal-behaviour baseline per user and host, and confirm the included hours it uses.
Deploy detection and deception
We deploy EDR, network detection and honeytokens, and tune Sigma detection rules against your actual environment.
Monitor and tune
We review flagged deviations, tune thresholds to cut false positives, and hand over documentation on what's being watched.
Use cases
SaaS company with remote engineering teams
A distributed engineering team gets host-level EDR and honeytokens placed in credential stores, adding high-confidence detection without adding friction to normal developer workflows.
E-commerce platform defending against automated abuse
An online retailer gets network detection tuned to flag automated, AI-assisted scraping and credential-stuffing traffic that individually looks like normal browsing.
Technology stack
- Wazuh
- Suricata
- Zeek
- Sigma rules
- MITRE ATT&CK
- OSSEC
- Honeytokens
Every subscription includes monthly DevOps, CyberSecurity and Programming hours: 1 h from the cPanel Advanced plan up, 1–8 h on VPS, 5–10 h on dedicated servers depending on the plan, and 10 h on GPU servers. Hours are requested from your client panel, don't roll over to the next month, and anything above the quota is accepted and billed separately.
Frequently asked questions
What is UEBA and how is it different from a normal alert rule?
A normal rule flags a fixed condition, like five failed logins in a minute, regardless of context. UEBA compares current activity against a learned baseline for that specific user or host, so it can catch subtler deviations a fixed rule would never trigger on, and ignore activity a fixed rule would wrongly flag.
What is a honeytoken and why does it have so few false positives?
It's a fake credential, file or database record placed specifically so no legitimate process or person should ever interact with it. Any access to it is a strong signal of unauthorised activity, since there's no normal reason it would ever be touched — unlike most detection signals, which need tuning to separate real threats from normal noise.
Does behavioural detection replace signature-based detection?
No — they complement each other. Signatures catch known malware and attack patterns efficiently; behavioural detection catches novel techniques and misuse of legitimate credentials that no signature would match. We run both together.
How does AI-assisted triage reduce alert fatigue?
It correlates alerts across sources and ranks them against known attack patterns, so an engineer reviews a shortlist of genuinely suspicious activity instead of every individual anomaly a behavioural system flags in isolation.
Can this detect an attacker using AI tools against us?
Automated, AI-driven attack tooling still generates network and host activity that deviates from a legitimate user's baseline, even when it's designed to look plausible request by request. UEBA and network detection are specifically built to catch that kind of volume and pattern deviation, not just known malicious signatures.
Where is the detection data processed?
Log and telemetry processing happens in our Frankfurt data centre, within the EU. We don't currently operate UK-resident processing for this service; the EU adequacy decision keeps UK-to-EU data flows straightforward for most compliance purposes, but check your own obligations if data residency inside the UK specifically is a hard requirement.
Want detection that learns your normal, not a generic rule?
Tell us which systems matter most and we'll build behavioural baselines using your included CyberSecurity hours.