VPN Access Anomaly Flagging
VPN logs generate a connection record for every remote login, and buried somewhere in the routine daily volume is the occasional genuinely suspicious pattern — a login from a country the employee has never traveled to, two connections from geographically distant locations within an hour that no real travel schedule could explain, or a login at 3am from an account that's never once connected outside business hours. Nobody has time to eyeball raw VPN logs looking for these patterns manually, so unless an anomaly happens to be severe enough to trip an existing SIEM rule, it sits unnoticed in the log until — if the credential really was compromised — the damage from whatever the account was used for becomes the thing that actually gets noticed.
STARTING PRICE
From €299
Standard tier · Multi-step workflow with AI extraction/decisioning and 2-3 integrations.
Get a quote →Saves roughly 4-6 hrs/week of manual log review plus materially faster detection of compromised credentials.
How the automation works
We build a baseline of normal connection behavior per employee — typical login hours, typical source countries, typical device fingerprints — from historical VPN log data, then flag new connections that break from that individual baseline rather than applying one blanket rule across everyone, since a global sales team's normal pattern looks nothing like an office-based finance team's normal pattern. Flags are scored by how far outside the individual's baseline the event falls and by whether multiple anomaly signals stack on the same connection — a new country plus an odd hour plus a new device is far more suspicious than any one signal alone — with high-confidence flags routed to security immediately and lower-confidence ones batched for review, so the queue stays usable instead of drowning in low-signal noise.
Process flow
- 01
Build a per-employee connection baseline ai
Historical VPN log data establishes typical login hours, source countries, and device fingerprints individually per employee rather than one company-wide norm.
- 02
Monitor new connections continuously trigger
Each new VPN connection is evaluated against the connecting employee's individual baseline as it happens, not in a batch review after the fact.
- 03
Score anomalies by deviation and signal stacking ai
Deviations are scored by how far outside baseline they fall and whether multiple anomaly signals — new country, odd hour, new device — stack on the same connection.
- 04
Route by confidence level output
High-confidence, multi-signal anomalies route to security immediately; single, lower-confidence signals batch into a periodic review queue instead of triggering an individual alert each time.
- 05
Update the baseline over time ai
Confirmed legitimate new patterns — an employee relocating, a role change requiring new travel — get incorporated into the baseline so it doesn't keep re-flagging normal new behavior.
Inputs
- VPN connection logs with timestamp, location, and device data
- Employee travel or relocation records where available
- Identity provider login correlation data
- Historical baseline connection patterns
Outputs
- Scored VPN anomaly flags
- High-confidence security escalations
- Batched low-confidence review queue
- Updated per-employee baseline over time
Works with
Prefer a fully custom build instead of an off-the-shelf integration? We scope both options during your free consultation — most jobs like this one work fine on standard connectors, but higher-volume or non-standard systems sometimes need bespoke API work, reflected in the complex tier.
Where this goes wrong if you get it wrong
- Employees using a commercial VPN or privacy tool for unrelated personal reasons on the same device can shift the apparent source location and trigger false anomaly flags that have nothing to do with account compromise — the scoring needs a way to distinguish this pattern, or security ends up chasing routine personal VPN use.
- A genuine baseline shift — an employee relocating permanently, or a new role that requires regular travel — needs to update the model within a reasonable window, or the system keeps generating false positives against someone's new normal indefinitely, which erodes trust in the alerts over time.
- IP-based geolocation is imprecise and sometimes wrong at the country level, particularly for connections routed through certain ISPs or mobile carriers, so an 'impossible travel' flag driven by IP geolocation alone needs corroborating signals — device fingerprint, login timing — before it's treated as high confidence rather than a geolocation artifact.
- Flagging shared or service accounts using the same per-employee baseline logic used for individual human accounts produces constant false positives, since those accounts often connect from multiple locations and systems by design — they need a separate baseline model or an explicit exclusion, not the individual behavioral baseline.
Frequently asked questions
Does this block suspicious VPN connections automatically?
No, it flags and routes anomalies for a human security decision — blocking a connection automatically risks locking out a legitimate employee on a false positive, so the action step stays with your security team.
How does it avoid flagging every employee who travels for work?
The baseline is built per employee rather than company-wide, and known travel patterns or relocations can be incorporated so legitimate new behavior stops re-triggering flags.
What if VPN geolocation data is inaccurate?
High-confidence flags require multiple stacked anomaly signals — location plus timing plus device — rather than relying on IP geolocation alone, which reduces false positives from geolocation imprecision.
Does this work for service accounts and shared logins, not just individual employees?
Service and shared accounts need a separate model or explicit exclusion from individual behavioral baselining, since their normal connection pattern looks nothing like a human employee's.