AI-XDR
Extended detection and response for vehicle fleets, powered by advanced AI analytics, with an investigation flow for analysts.
- Automotive cybersecurity
- VSIEM
- AI product design
- Redesign
- Design system
- Dashboards
- B2B SaaS
- Product
- XDR platform for vehicle fleets
- Industry
- Cybersecurity · VSIEM
- My role
- Redesigned an existing product and integrated AI features
I proposed the redesign of the existing vehicle SIEM and led it from specification to design. The teams adopted it.
On top of the redesign, we added AI capabilities to the alert-investigation flow.
- 1Alert
- 2Evidence
- 3Impact
- 4Affected vehicles
- 5Mitigation
Alerts
Every alert, with the critical one on top
Severity, source and status in one scannable table, and a new critical alert surfaced the moment it arrives.
Alert summary
One alert, its vehicles and related events
Link analysis shows how an alert connects to vehicles and to other detections.
Evidence
Every event behind the alert
Security events listed with their sensor, type and description, and a timeline of when they were detected.
Impact
Ask the AI for the potential impact
A prompt in plain language, and the answer written out, with recommended questions to continue.
Affected vehicles
Ask the AI which vehicles are at risk
The answer is laid out as charts and a map instead of a paragraph.
Dashboard on demand
Choose "Answer by graph" and get a dashboard
The AI builds a dashboard of the fleet's security posture: health score, alert status and severity, events per month, top vulnerabilities and the monitored vehicles.
Response actions
Top vulnerabilities, with an action for each
A prioritized table with the CVE, the affected component, the severity score and the action.
Challenge
The product was many separate screens that did not connect or guide the analyst, and analysts had to leave the platform for an external BI tool to find recommended actions.
Decision
The architecture was reorganized into one investigation journey: Alert, Evidence, Impact, Affected Vehicles, Mitigation, with AI insights on potential impact and suggested mitigation. I also created a design system and applied it to the existing product.
Impact
- The teams adopted the redesign of the vehicle SIEM.
- The prototype was presented at a major conference, where Google representatives saw it, and it led to a collaboration on developing the AI.
