Chapter 13 — Human factors and cyber policy: risk scenarios
Lesson map
Lesson 13 published. Map, example, EX037.
flowchart TD
N0["Learning outcomes"]
N1["13.1 Writing scenarios"]
N2["13.2 Estimating risk"]
N3["Lab — Ranking risk scenarios"]
N4["13.3 Linking controls"]
N5["Synthesis"]
N0 --> N1 --> N2 --> N3 --> N4 --> N5
N5 --> EX["Worked example"]
EX --> Q["Exercise EX037"]
Logical drawing
flowchart LR
N0["Learning outcomes"]
N1["13.1 Writing scenarios"]
N2["13.2 Estimating risk"]
N3["Lab — Ranking risk scenarios"]
N4["13.3 Linking controls"]
N5["Synthesis"]
N0 --> N1 --> N2 --> N3 --> N4 --> N5 --> Q["EX037"]
[Learning outcomes]
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v
[13.1 Writing scenarios]
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v
[13.2 Estimating risk]
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v
[Lab — Ranking risk scenarios]
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v
[13.3 Linking controls]
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v
[Synthesis]
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v
[Worked example] --> [Exercise EX037]
Learning outcomes
You will (1) write realistic risk scenarios involving people; (2) estimate likelihood and impact simply; (3) link scenarios to controls.
13.1 Writing scenarios
Lucas writes scenarios describing how an attack might unfold at Brightwell, each with an attacker, a method, a target and an outcome. In one, a caller posing as the practice system vendor persuades a receptionist to install remote access software, then copies patient records. In another, a fake supplier email changes bank details and a manager pays a large invoice to the attacker. In a third, a fake booking system login page captures a receptionist's password. Each scenario uses synthetic details.
13.2 Estimating risk
Lucas estimates likelihood and impact on simple three-level scales with the practice manager. The vendor call is medium likelihood and high impact; the fake supplier change is high likelihood and high impact; the fake login is high likelihood but medium impact once security keys are in place. The lab multiplies the scores and ranks scenarios. He notes that the scores are judgements and that their main value is in prompting discussion about which risks to address first. The manager reviews every score.
Lab — Ranking risk scenarios
# Rank risk scenarios by likelihood x impact on 1-3 scales (synthetic).
s = [("fake vendor call", 2, 3), ("fake supplier bank change", 3, 3), ("fake booking login", 3, 2)]
for n, l, i in sorted(s, key=lambda x: -x[1] * x[2]): print(f"{n}: {l}x{i} = {l * i}")
Run with python3 lab_scenarios.py. Verified output:
fake supplier bank change: 3x3 = 9
fake vendor call: 2x3 = 6
fake booking login: 3x2 = 6
13.3 Linking controls
For each scenario, Lucas lists the controls that would stop or limit it. The vendor call is countered by a rule that remote access is granted only after calling the vendor back on a known number and getting manager approval. The supplier change is countered by an automatic hold and callback. The fake login is countered by security keys and reporting. Mapping controls to scenarios shows managers why each control exists and which risk would grow if it were removed. Managers keep the table.
Worked example
Lucas writes three people-centred scenarios, ranks them with simple scores and maps controls to each.
Synthetic case. Scenarios are synthetic.
Errors and limits
Scores are subjective. Scenarios may miss new methods.
Exercises
- EX037 — Vendor scenario. What does the fake vendor caller want? Rubric: 3 = installation of remote access software;; 2 = remote access;; 1 = passwords;; 0 = none.
- EX038 — Top scenario. Which scenario ranked highest in the lab? Rubric: 3 = fake supplier change with score 9;; 2 = supplier;; 1 = vague;; 0 = none.
- EX039 — Remote access rule. What rule counters the vendor call? Rubric: 3 = callback on a known number plus manager approval;; 2 = callback;; 1 = vague;; 0 = none.
Synthesis
Scenarios make risks concrete and connect each to the controls that address it.
Continue studying — chapter 13 of 30
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