## Step 1: Define the symbolic representation of the variables
The variables are 'intrusion analysts' and 'automatic alerts', which we can denote as $x_1$ and $x_2$ respectively.

## Step 2: Translate the objective function into symbolic notation
The objective function to maximize is $9.56x_1 + 5.67x_2$.

## Step 3: Define the constraints in symbolic notation
The constraints are:
1. $0.03x_1 + 0.37x_2 \geq 100$
2. $0.99x_1 + 1.39x_2 \geq 23$
3. $-6x_1 + 2x_2 \geq 0$
4. $0.03x_1 + 0.37x_2 \leq 187$
5. $0.99x_1 + 1.39x_2 \leq 80$
6. $x_1$ is an integer
7. $x_2$ is an integer

## 4: Express the problem in a JSON format for clarity
```json
{
'sym_variables': [('x1', 'intrusion analysts'), ('x2', 'automatic alerts')],
'objective_function': '9.56*x1 + 5.67*x2',
'constraints': [
    '0.03*x1 + 0.37*x2 >= 100',
    '0.99*x1 + 1.39*x2 >= 23',
    '0.03*x1 + 0.37*x2 <= 187',
    '0.99*x1 + 1.39*x2 <= 80',
    '-6*x1 + 2*x2 >= 0',
    'x1 >= 0 and x1 is an integer',
    'x2 >= 0 and x2 is an integer'
]
}
```

## 5: Implement the optimization problem using Gurobi
```python
import gurobi

def solve_optimization_problem():
    # Create a new Gurobi model
    model = gurobi.Model()

    # Define the variables
    x1 = model.addVar(name="intrusion_analysts", vtype=gurobi.GRB.INTEGER)
    x2 = model.addVar(name="automatic_alerts", vtype=gurobi.GRB.INTEGER)

    # Define the objective function
    model.setObjective(9.56 * x1 + 5.67 * x2, gurobi.GRB.MAXIMIZE)

    # Define the constraints
    model.addConstr(0.03 * x1 + 0.37 * x2 >= 100)
    model.addConstr(0.99 * x1 + 1.39 * x2 >= 23)
    model.addConstr(0.03 * x1 + 0.37 * x2 <= 187)
    model.addConstr(0.99 * x1 + 1.39 * x2 <= 80)
    model.addConstr(-6 * x1 + 2 * x2 >= 0)

    # Set bounds for variables (non-negativity)
    x1.lb = 0
    x2.lb = 0

    # Solve the model
    model.optimize()

    # Print the solution
    if model.status == gurobi.GRB.OPTIMAL:
        print("Optimal solution found.")
        print(f"Intrusion Analysts: {x1.varValue}")
        print(f"Automatic Alerts: {x2.varValue}")
        print(f"Objective Function Value: {model.objVal}")
    else:
        print("No optimal solution found.")

solve_optimization_problem()
```