## Step 1: Define the symbolic representation of the variables
The variables are 'automatic alerts', 'patches per day', and 'pen testers', which can be represented symbolically as $x_0$, $x_1$, and $x_2$ respectively.

## Step 2: Convert the natural language description into a symbolic representation
The objective function to maximize is $1x_0 + 4x_1 + 2x_2$.

## Step 3: List all the constraints in symbolic notation
The constraints are:
- $21x_0 + 20x_1 + 1x_2 \leq 134$ (computational load)
- $26x_0 + 9x_1 + 9x_2 \leq 168$ (data confidentiality impact)
- $24x_0 + 24x_1 + 5x_2 \leq 187$ (data integrity impact)
- $26x_0 + 19x_1 + 2x_2 \leq 146$ (data accessibility impact)
- $1x_0 + 5x_1 + 10x_2 \leq 133$ (dollar cost)
- $26x_0 + 2x_2 \geq 16$ (data accessibility impact from automatic alerts and pen testers)
- $19x_1 + 2x_2 \geq 39$ (data accessibility impact from patches per day and pen testers)
- $1x_0 + 5x_1 + 10x_2 \geq 36$ (dollar cost from automatic alerts, patches per day, and pen testers)
- $21x_0 + 20x_1 \leq 48$ (computational load from automatic alerts and patches per day)
- $21x_0 + 1x_2 \leq 88$ (computational load from automatic alerts and pen testers)
- $20x_1 + 1x_2 \leq 112$ (computational load from patches per day and pen testers)
- $21x_0 + 20x_1 + 1x_2 \leq 112$ (total computational load)
- $26x_0 + 9x_2 \leq 102$ (data confidentiality impact from automatic alerts and pen testers)
- $9x_1 + 9x_2 \leq 117$ (data confidentiality impact from patches per day and pen testers)
- $26x_0 + 9x_1 + 9x_2 \leq 117$ (total data confidentiality impact)
- $24x_0 + 5x_2 \leq 178$ (data integrity impact from automatic alerts and pen testers)
- $24x_0 + 24x_1 \leq 62$ (data integrity impact from automatic alerts and patches per day)
- $24x_0 + 24x_1 + 5x_2 \leq 143$ (total data integrity impact)
- $26x_0 + 19x_1 \leq 52$ (data accessibility impact from automatic alerts and patches per day)
- $19x_1 + 2x_2 \leq 139$ (data accessibility impact from patches per day and pen testers)
- $26x_0 + 19x_1 + 2x_2 \leq 139$ (total data accessibility impact)
- $1x_0 + 10x_2 \leq 103$ (dollar cost from automatic alerts and pen testers)
- $5x_1 + 10x_2 \leq 129$ (dollar cost from patches per day and pen testers)
- $1x_0 + 5x_1 + 10x_2 \leq 129$ (total dollar cost)

## 4: Define the symbolic representation of the problem
```json
{
    'sym_variables': [('x0', 'automatic alerts'), ('x1', 'patches per day'), ('x2', 'pen testers')],
    'objective_function': '1*x0 + 4*x1 + 2*x2',
    'constraints': [
        '21*x0 + 20*x1 + 1*x2 <= 134',
        '26*x0 + 9*x1 + 9*x2 <= 168',
        '24*x0 + 24*x1 + 5*x2 <= 187',
        '26*x0 + 19*x1 + 2*x2 <= 146',
        '1*x0 + 5*x1 + 10*x2 <= 133',
        '26*x0 + 2*x2 >= 16',
        '19*x1 + 2*x2 >= 39',
        '1*x0 + 5*x1 + 10*x2 >= 36',
        '21*x0 + 20*x1 <= 48',
        '21*x0 + 1*x2 <= 88',
        '20*x1 + 1*x2 <= 112',
        '21*x0 + 20*x1 + 1*x2 <= 112',
        '26*x0 + 9*x2 <= 102',
        '9*x1 + 9*x2 <= 117',
        '26*x0 + 9*x1 + 9*x2 <= 117',
        '24*x0 + 5*x2 <= 178',
        '24*x0 + 24*x1 <= 62',
        '24*x0 + 24*x1 + 5*x2 <= 143',
        '26*x0 + 19*x1 <= 52',
        '19*x1 + 2*x2 <= 139',
        '26*x0 + 19*x1 + 2*x2 <= 139',
        '1*x0 + 10*x2 <= 103',
        '5*x1 + 10*x2 <= 129',
        '1*x0 + 5*x1 + 10*x2 <= 129'
    ]
}
```

## 5: Write the Gurobi code to solve the optimization problem
```python
import gurobi

# Create a new model
model = gurobi.Model()

# Define the variables
x0 = model.addVar(name='automatic_alerts', vtype=gurobi.GRB.INTEGER)
x1 = model.addVar(name='patches_per_day', vtype=gurobi.GRB.INTEGER)
x2 = model.addVar(name='pen_testers', vtype=gurobi.GRB.INTEGER)

# Define the objective function
model.setObjective(1*x0 + 4*x1 + 2*x2, gurobi.GRB.MAXIMIZE)

# Add constraints
model.addConstr(21*x0 + 20*x1 + 1*x2 <= 134)
model.addConstr(26*x0 + 9*x1 + 9*x2 <= 168)
model.addConstr(24*x0 + 24*x1 + 5*x2 <= 187)
model.addConstr(26*x0 + 19*x1 + 2*x2 <= 146)
model.addConstr(1*x0 + 5*x1 + 10*x2 <= 133)
model.addConstr(26*x0 + 2*x2 >= 16)
model.addConstr(19*x1 + 2*x2 >= 39)
model.addConstr(1*x0 + 5*x1 + 10*x2 >= 36)
model.addConstr(21*x0 + 20*x1 <= 48)
model.addConstr(21*x0 + 1*x2 <= 88)
model.addConstr(20*x1 + 1*x2 <= 112)
model.addConstr(21*x0 + 20*x1 + 1*x2 <= 112)
model.addConstr(26*x0 + 9*x2 <= 102)
model.addConstr(9*x1 + 9*x2 <= 117)
model.addConstr(26*x0 + 9*x1 + 9*x2 <= 117)
model.addConstr(24*x0 + 5*x2 <= 178)
model.addConstr(24*x0 + 24*x1 <= 62)
model.addConstr(24*x0 + 24*x1 + 5*x2 <= 143)
model.addConstr(26*x0 + 19*x1 <= 52)
model.addConstr(19*x1 + 2*x2 <= 139)
model.addConstr(26*x0 + 19*x1 + 2*x2 <= 139)
model.addConstr(1*x0 + 10*x2 <= 103)
model.addConstr(5*x1 + 10*x2 <= 129)
model.addConstr(1*x0 + 5*x1 + 10*x2 <= 129)

# Optimize the model
model.optimize()

# Print the solution
if model.status == gurobi.GRB.OPTIMAL:
    print('Objective: ', model.objVal)
    print('Automatic alerts: ', x0.varValue)
    print('Patches per day: ', x1.varValue)
    print('Pen testers: ', x2.varValue)
else:
    print('The model is infeasible')
```