This document describes a Bayesian network model with nodes for human error (H), IT failure (I), and failure type (financial, security, or general; F). It lists the conditional probability tables for each node. It then performs Bayesian inference to calculate the posterior probability of IT failure being false given that failure occurred, finding a value of 0.55. Finally, it extends the model to include a node for a data breach (D) and re-calculates the conditional probability tables.

1. P(H=F) P(H=T)
0.75 0.25
P(I=F) P(I=T)
0.80 0.20
H I P(F=M) P(F=S) P(F=G)
F F 0.70 0.20 0.10
T F 0.30 0.10 0.60
F T 0.35 0.15 0.50
T T 0.10 0.20 0.70
Bayesian network and CPT

2. Result
P(I=F) P(I=T)
0 1

3. Bayes theorem
H I P(F=M) P(F=S) P(F=G)
F F 0.70 0.20 0.10
T F 0.30 0.10 0.60
F T 0.35 0.15 0.50
T T 0.10 0.20 0.70
• Financial loss
P(H=F) P(H=T)
0.75 0.25
• Human error
P(I=F) P(I=T)
0.80 0.20
• IT failure
P(I=F) P(I=T)
P(H=F) 0.60 0.15
P(H=T) 0.20 0.05
H I P(F=M) P(F=S) P(F=G)
F F 0.42 0.12 0.06
T F 0.06 0.02 0.12
F T 0.0525 0.0225 0.075
T T 0.005 0.01 0.035
• Likelihood = 0.11 / 0.29 = 0.3793
• Normalising constant = 0.20
• Prior = 0.29
• Posterior = ( 0.3793 * 0.29) / 0.20 = 0.55

4. P(I=F) P(I=T)
0.80 0.20
D P(H=F) P(H=T)
F 0.75 0.25
T 0.25 0.75
P(D=F) P(D=T)
0.80 0.20
H I P(F=M) P(F=S) P(F=G)
F F 0.70 0.20 0.10
T F 0.30 0.10 0.60
F T 0.35 0.15 0.50
T T 0.10 0.20 0.70
Bayesian network and CPT

5. Result
P(D=F) P(D=T)
0 1
P(I=F) P(I=T)
0 1