Steel Mountain

Case Study 2

Elliot Alderson and his Fsociety team move beyond digital exploits and into physical infiltration. Targeting E Corp’s offsite data storage facility in Rye Brook, Elliot impersonates a corporate guest to sneak malware into Steel Mountain. Exploiting human vulnerability—particularly that of sales associate Bill Harper—Elliot gains access to secure areas and deploys a hidden Raspberry Pi device. This operation pushes Elliot into morally gray territory, causing guilt over the unintended fallout and highlighting the psychological burden of real-world hacking.

Our investigative platform transforms episodes like “Steel Mountain” into interactive, analytical case studies by visualizing relationships, actions, and risks in a way that’s accessible to non-technical users. Powered by the case files, a construct of a 3D network graph that maps characters, locations, devices, and timelines—bridging fiction and cybersecurity training.

Key Features of the Program:

Node-based Intelligence Mapping:
Every character or entity (e.g., Elliot, Bill, E Corp, Steel Mountain) is visualized as a node, color-coded by role:
- 🔵 Blue = Allies (Elliot, Darlene, Allsafe)
- 🔴 Red = Threats, targets, or compromised elements (E Corp, Cisco, Dark Army)
Link Logic & Storytelling:
Relationships (manipulated, employed by, infiltrated, dependent on) are modeled with directed links—Elliot → Steel Mountain --> Bill Harper — which trace the link of the social hack on Bill

Geo-spatial Visualization:
Each node carries real-world coordinates and an influence radius (in km), letting users visualize overlapping zones—e.g., where Elliot’s library safe-house radius intersects with Steel Mountain’s periphery.
Volatility Engine:
Using a proximity-based volatility function, the system calculates the chance of interaction between nodes in overlapping zones—allowing users to identify hot zones where exposure, contact, or failure is more likely.
Protected & Caution Flags:
Risk Tagging supported
- protected: true (e.g., Elliot’s digital “mind” or persona nodes)
- caution: true (e.g., Bill Harper, Cisco, E Corp servers)

By layering this structured dataset over the narrative of “Steel Mountain,” users don’t just watch Elliot’s hack—they investigate it. They see how digital footprints, physical proximity, human manipulation, and technical operations converge. The platform brings all of this together into a clickable, explorable web, letting users not only understand but reconstruct the operation.

This approach transforms a suspenseful episode into a training model for identifying vulnerabilities, tracking lateral movement, assessing operational risk, and predicting outcomes—making Mr. Robot not just a story, but a teaching tool.

With the Discovery tool we can filter out or filter in all status's of our nodes and separate between colors, locations, and names. See how easy it is to filter and see overlaps and zones. Use this to filter your Volatility in Risk as well.