// CASE 04 · SHARELOCK · AI-NATIVE SECURITY
When security experts need efficiency over elegance.
Sharelock detects identity threats by learning how every user behaves and spotting the anomaly. The machine learning was patented. The interface was the problem: analysts could not finish critical tasks in the first prototype, and managers could not read the risk reports.
fig 01 · Access, compliance and telemetry in one view, redesigned.
fig 01 · Access, compliance and telemetry in one view, redesigned.
2
audiences with opposite needs: security analysts and managers
4
phases, from discovery workshop to design system
1
component library shared with the development team
2019
shipped, still the base of the product's UI
fig 02 · Dashboard details: the anomaly, then the action.
01
Executive summary
Sharelock collects data from any source, logs, sensors, applications, builds a behavioural baseline per user and flags what deviates. That is a lot of data on a screen, for two kinds of people: analysts who need depth and speed, and managers who need a decision. We observed analysts doing their tasks, or failing to, redesigned the critical flows around what they were actually trying to do, and set up a design system so the product could stay coherent across every view without losing usability.
02
Four phases
➔ Discovery workshop and stakeholder interviews
➔ Usability testing with cybersecurity professionals and managers
➔ Iterative design sprints driven by what we saw in testing
➔ Design system implementation with the development team
fig 03 · The component library the development team built from.
Tools & tactics
Qualitative usability testing
Atomic design
Cross-functional design ops
Component library
03
Issues discovered
➔ Complex security workflows had no information hierarchy: everything looked equally urgent.
➔ Technical language locked managers out of the reports they were supposed to act on.
➔ Each section of the platform had its own visual logic.
➔ Critical actions sat in secondary navigation.
➔ Dashboards overwhelmed people instead of guiding a decision.
Four principles for expert users
Clarify complexity, keep the depth
Surface the critical action first
Show what each action does before it does it
Enable expertise, do not dumb it down
04
Results
Critical workflows became completable end to end by analysts, and managers got reports they could act on. The design system gave Sharelock a way to add features without redesigning the product each time, and the interface became part of the sales pitch. What I cannot show you: task completion rates come from our internal testing, not from a published study, so treat them as our observation. What stays with me: the same principle, organise rather than simplify, is what I apply today when I design interfaces on top of AI systems that produce more signal than a person can read.
05
Lessons learned
➔ Specialised users deserve thoughtful design, not a simpler product
➔ In B2B, visual coherence follows from solving real workflow problems
➔ Watching real users work beats any stakeholder meeting
➔ A design system is what lets a small team scale a complex product