Cybrary ForgeCYBRARY / FORGE
AI Range

Northstar Benefits demo suite

Third-party benefits administration. One incident — OAUTH-1181 — unreviewed enterprise consent grant on the claims tenant — worked eight times. Only the model runtime changes, so every difference in the room is attributable to the runtime decision rather than to a different story.

8 stages · 9 hours end to end · tenant northstar-claims.onmicrosoft.example

  • Priya Raman — SOC lead, runs the bridge
  • Dan Whitlock — incident responder, owns containment
  • Ana Sousa — AI platform engineer, owns runtime configuration
  • Marcus Bell — governance lead, owns what may be recorded and shown
R1
The case without any AI
deterministic

Work OAUTH-1181 with nothing but the console, the sign-in logs and the consent record.

Establish the human baseline: time to triage, and what the analyst actually had to read.

Debrief: What took the longest, and was any of it judgement rather than lookup?

40 min · SOC analyst, Incident responder

R2
A private analyst on the device
chrome-builtin

Same case, with an on-device assistant that never sends a token off the laptop.

Privacy comes free; capability does not. The small local model summarizes but misreads scope.

Debrief: Which of R1's slow steps did the local model actually remove?

40 min · SOC analyst

R3
Bring your own copilot
byok

The analyst supplies their own provider key and picks a model mid-case.

A key in a browser is a control problem before it is a capability win.

Debrief: Who owns the prompt, the key and the log in this configuration?

55 min · SOC analyst, AI consumer

R4
Through the company's own gateway
org-gateway

Northstar's regulated gateway is wired in: their endpoint, their retention, their allow-list.

The gateway makes the assistant auditable — and makes every limitation the company's own.

Debrief: Which controls moved from the lab to the enterprise, and who is accountable now?

75 min · AI platform administrator, Security architect, SOC lead · shared session

Launch stage
R5
Assessing people, fairly, with AI in the room
deterministic

Two candidates work the same case with different assistants; the grade must stay fair.

Ground truth is the scenario predicate, never the model's opinion.

Debrief: What would have made this assessment indefensible?

60 min · Incident responder, Assessment designer · shared session

Launch stage
R6
The router under pressure
hybrid

A provider degrades and the allowance crosses 80% mid-incident.

Routing and budget policy are incident controls, not procurement details.

Debrief: Did the fallback preserve the investigation, or just preserve the uptime number?

80 min · AI operations engineer, Security architect · shared session

Launch stage
R7
The shared war room
cybrary-managed

The whole team works one run: one control state, one trace, one allowance, one join code.

Shared AI needs shared discipline — who may move a control, and who may quote the model.

Debrief: Where did the budget actually go, and what did the team learn from it?

120 min · SOC lead, Incident responder, Threat hunter, Communications lead · shared session

Launch stage
R8
The bake-off
cybrary-managed

Same prompts, same context budget, same tools — four candidate models, one scorecard.

A comparison without a held-constant harness is theater.

Debrief: Which model would you actually put in front of Northstar's analysts, and why?

90 min · AI security lead, Governance lead, SOC leadership · shared session

Launch stage