backend/core/tables.py

@@ -4,56 +4,48 @@ from backend.core.utils import calculate_aggregate_likelihood, calculate_aggrega
 
 def risk_matrix_table():
     likelihood_labels = [
-        "Certain (90-100%)",
-        "Almost Certain (80-89%)",
-        "Very Probable (70-79%)",
-        "Probable (60-69%)",
-        "Highly Likely (50-59%)",
-        "Likely (40-49%)",
-        "Occasional (30-39%)",
-        "Possible (20-29%)",
-        "Unlikely (10-19%)",
-        "Rare (0-9%)"
+        "Almost Certain (90-100%) (5)",
+        "Probable (51-89%) (4)",
+        "Possible (25-50%) (3)",
+        "Unlikely (11-24%) (2)",
+        "Rare (0-10%) (1)"
     ]
 
     impact_labels = [
-        "Insignificant",
-        "Minor",
-        "Moderate",
-        "Major",
-        "Severe",
-        "Catastrophic",
-        "Critical",
-        "Extreme",
-        "Disastrous",
-        "Unrecoverable"
+        "Insignificant (1)",
+        "Significant (2)",
+        "Severe (3)",
+        "Material (4)",
+        "Major (5)"
     ]
 
     color_mapping = {
-        "Very Low": "green",
-        "Low": "lightgreen",
+        "Very Low": "lightgreen",
+        "Low": "green",
         "Medium": "yellow",
         "High": "orange",
         "Critical": "red"
     }
 
-    table_matrix_risk = [["Impact ↓ / Likelihood →"] + impact_labels]
+    def get_label(score):
+        if score <= 2:
+            return "Very Low"
+        elif score <= 4:
+            return "Low"
+        elif score <= 10:
+            return "Medium"
+        elif score <= 16:
+            return "High"
+        else:
+            return "Critical"
 
-    for likelihood_index, likelihood_label in enumerate(likelihood_labels, start=1):
-        reversed_index = 11 - likelihood_index  
-        row = [likelihood_label]
-        for impact_index in range(1, 11):
-            score = reversed_index * impact_index
-            if score <= 20:
-                label = "Very Low"
-            elif score <= 40:
-                label = "Low"
-            elif score <= 60:
-                label = "Medium"
-            elif score <= 80:
-                label = "High"
-            else:
-                label = "Critical"
+    table_matrix_risk = [["Likelihood ↓ / Impact →"] + impact_labels]
+
+    for likelihood in range(5, 0, -1):
+        row = [likelihood_labels[5 - likelihood]]
+        for impact in range(1, 6):
+            score = likelihood * impact
+            label = get_label(score)
             color_class = color_mapping[label]
             row.append((score, label, color_class))
         table_matrix_risk.append(row)
@@ -82,7 +74,7 @@ def get_risk_table(document):
             .values('control', 'control__name', 'weight', 'likelihood')
             .distinct()
         )
-        max_weight = 10*10
+        max_weight = 10*5
         total_weight = calculate_aggregate_weight(controls)
         total_likelihood = calculate_aggregate_likelihood(controls)
         impact, likelihood = map_weight_to_impact_likelihood(total_weight, total_likelihood, max_weight)

backend/core/tests/test_utils.py

@@ -71,7 +71,7 @@ class UtilsTests(TestCase):
         mock_openai.return_value = mock_client
         mock_response = MagicMock()
         
-        control_lines = [f"{i} : 8 : 5" for i in range(1, 11)]
+        control_lines = [f"{i} : 4 : 3" for i in range(1, 11)]
         mock_response.choices[0].message.content = "\n".join(control_lines)
         mock_client.chat.completions.create.return_value = mock_response
 
@@ -100,7 +100,7 @@ class UtilsTests(TestCase):
         self.assertEqual(calculate_aggregate_likelihood(controls), 6)
 
     def test_map_weight_to_impact_likelihood(self):
-        impact, likelihood = map_weight_to_impact_likelihood(50, 30, 100)
+        impact, likelihood = map_weight_to_impact_likelihood(50, 30, 50)
         self.assertAlmostEqual(impact, 5.0)
         self.assertAlmostEqual(likelihood, 3.0)
 
@@ -123,7 +123,7 @@ class UtilsTests(TestCase):
     def test_generate_risk_graph(self):
         risks_with_controls = [
             {'risk': {'id': 1}, 'impact': 5.0, 'likelihood': 3.0},
-            {'risk': {'id': 2}, 'impact': 7.0, 'likelihood': 4.0}
+            {'risk': {'id': 2}, 'impact': 4.0, 'likelihood': 4.0}
         ]
         
         graph_data = generate_risk_graph(risks_with_controls)

backend/core/utils.py

@@ -90,14 +90,14 @@ def get_controls_for_risk(risk, organization):
     prompt = f"""
     You are an expert in cybersecurity risk management. Given the risk "{risk.risk_name}" and its associated organization details "{organization_details}",
     your task is to select **exactly 10 unique controls** from the provided list that best mitigate this risk. Each control should be assigned:
-    - A weight between **1 and 10** (1 = low impact, 10 = high impact).
-    - A likelihood score between **1 and 10** (1 = rare occurrence, 10 = highly likely).
+    - A weight between **1 and 5** (1 = low impact, 5 = high impact).
+    - A likelihood score between **1 and 5** (1 = rare occurrence, 5 = highly likely).
 
     ### Rules:
     1. **Each control ID must be unique** (no duplicates).
     2. **Only return control IDs, weights, and likelihood scores** in the exact format below.
-    3. **Weights must be between 1 and 10** (1 = low impact, 10 = high impact).
-    4. **Likelihood scores must be between 1 and 10** (1 = rare occurrence, 10 = highly likely).
+    3. **Weights must be between 1 and 5** (1 = low impact, 5 = high impact).
+    4. **Likelihood scores must be between 1 and 5** (1 = rare occurrence, 5 = highly likely).
     5. **Do NOT add explanations, descriptions, or extra text.**
     6. **Ensure that control IDs are randomly distributed and diverse across different categories.**
     ### Available Controls:
@@ -108,8 +108,8 @@ def get_controls_for_risk(risk, organization):
     <control_id> : <weight> : <likelihood>
 
     ### Example Correct Response (NO DUPLICATES):
-    12 : 8 : 90
-    45 : 7 : 60
+    12 : 5 : 2
+    45 : 4 : 1
 
     ⚠️ **If you provide duplicate control IDs, your response will be rejected. Ensure all control IDs are unique.**
     ⚠️ **Follow the response format exactly. Any deviation will be considered invalid.**
@@ -138,7 +138,7 @@ def get_controls_for_risk(risk, organization):
                     weight = int(weight_str)
                     likelihood = int(likelihood_str)
                 
-                    if control_id in valid_control_ids and 1 <= weight <= 10 and 1 <= likelihood <= 10 and control_id not in control_ids_seen:
+                    if control_id in valid_control_ids and 1 <= weight <= 5 and 1 <= likelihood <= 5 and control_id not in control_ids_seen:
                         selected_controls.append((control_id, weight, likelihood))
                         control_ids_seen.add(control_id)
                 except ValueError:
@@ -155,14 +155,14 @@ def get_controls_for_risk(risk, organization):
         retry_prompt = f"""
         You are an expert in cybersecurity risk management. Given the risk "{risk.risk_name}" and the organization's details "{organization_details}",
         your task is to select **exactly {missing_count} unique controls** from the provided list that best mitigate this risk. Each control should be assigned:
-        - A **weight** between **1 and 10** based on its effectiveness in reducing the risk.
-        - A likelihood score between **1 and 10** (1 = rare occurrence, 10 = highly likely).
+        - A **weight** between **1 and 5** based on its effectiveness in reducing the risk.
+        - A likelihood score between **1 and 5** (1 = rare occurrence, 5 = highly likely).
 
         ### Rules:
         1. **Each control ID must be unique** (no duplicates).
         2. **Only return control IDs, weights, and likelihood scores** in the exact format below.
-        3. **Weights must be between 1 and 10** (1 = low impact, 10 = high impact).
-        4. **Likelihood scores must be between 1 and 10** (1 = rare occurrence, 10 = highly likely).
+        3. **Weights must be between 1 and 5** (1 = low impact, 5 = high impact).
+        4. **Likelihood scores must be between 1 and 5** (1 = rare occurrence, 5 = highly likely).
         5. **Do NOT add explanations, descriptions, or extra text.**
         6. **Ensure that control IDs are diverse and well-distributed across different categories.**
 
@@ -174,8 +174,8 @@ def get_controls_for_risk(risk, organization):
         <control_id> : <weight> : <likelihood>
 
         ### Example Correct Response (NO DUPLICATES):
-        12 : 8 : 85
-        45 : 7 : 60
+        12 : 4 : 5
+        45 : 5 : 3
 
         ⚠️ **If you provide duplicate control IDs, your response will be rejected. Ensure all control IDs are unique.**
         ⚠️ **Follow the response format exactly. Any deviation will be considered invalid.**
@@ -201,7 +201,7 @@ def get_controls_for_risk(risk, organization):
                         weight = int(weight_str)
                         likelihood = int(likelihood_str)
 
-                        if control_id in valid_control_ids and 1 <= weight <= 10 and 1 <= likelihood <= 10 and control_id not in control_ids_seen:
+                        if control_id in valid_control_ids and 1 <= weight <= 5 and 1 <= likelihood <= 5 and control_id not in control_ids_seen:
                             selected_controls.append((control_id, weight, likelihood))
                             control_ids_seen.add(control_id)
                     except ValueError:
@@ -243,10 +243,8 @@ def calculate_aggregate_likelihood(controls):
     return total_likelihood
 
 def map_weight_to_impact_likelihood(total_weight, total_likelihood, max_weight):
-    normalized_weight = total_weight / max_weight
-
-    impact = min(10.0, max(1.0, normalized_weight * 10.0)) 
-    likelihood = min(10.0, max(1.0, total_likelihood / 10.0))
+    impact = min(5.0, max(1.0, total_weight / 10.0)) 
+    likelihood = min(5.0, max(1.0, total_likelihood / 10.0))
 
     return impact, likelihood
 
@@ -256,12 +254,12 @@ def generate_risk_graph(risks_with_controls):
     likelihoods = [risk['likelihood'] for risk in risks_with_controls]
     risk_ids = [risk['risk']['id'] for risk in risks_with_controls]
 
-    bg_img_path = find('img/graph_matrix (3).png')
+    bg_img_path = find('img/graph_matrix.png')
     bg_img = mpimg.imread(bg_img_path)
 
     fig, ax = plt.subplots(figsize=(10, 8))
 
-    ax.imshow(bg_img, extent=[0, 11.2, 0, 11.2], aspect='auto')
+    ax.imshow(bg_img, extent=[0.0, 5.4, 0.0, 5.4], aspect='auto')
 
     scatter = ax.scatter(
         likelihoods, impacts, 

backend/settings.py

@@ -38,6 +38,7 @@ DEBUG = config('DEBUG', default=False, cast=bool)
 ALLOWED_HOSTS = config('ALLOWED_HOSTS', default=[], cast=Csv())
 
 SITE_DOMAIN = "http://64.226.105.114"
+#SITE_DOMAIN = "http://127.0.0.1:8000"
 
 # Application definition
 

document_template.yml

@@ -148,26 +148,26 @@
       Impact is aslo defined with the following table:
     headers: ["Level", "Financial Cost", "Reputational Impact", "Management Effort", "Operational Resources", "RPO", "RTO"]
     rows:
-    - ["Insignificant (1-2)", "< 0.05% Material", "Normal", "Normal", "Normal", "16 hr", "64 hr"]
-    - ["Significant (3-4)", "> 0.5% Material", "Minor", "Minor", "Minor", "8 hr", "32 hr"]
-    - ["Severe (5-6)", "> 10% Material", "Moderate", "Moderate", "Moderate", "4 hr", "16 hr"]
-    - ["Material (7-8)", "> 50% Material", "Critical", "Critical", "Critical", "2 hr", "8 hr"]
-    - ["Major (9-10)", "> Material", "Precarious", "Precarious", "Precarious", "1 hr", "4 hr"]
+    - ["Insignificant (1)", "< 0.05% Material", "Normal", "Normal", "Normal", "16 hr", "64 hr"]
+    - ["Significant (2)", "> 0.5% Material", "Minor", "Minor", "Minor", "8 hr", "32 hr"]
+    - ["Severe (3)", "> 10% Material", "Moderate", "Moderate", "Moderate", "4 hr", "16 hr"]
+    - ["Material (4)", "> 50% Material", "Critical", "Critical", "Critical", "2 hr", "8 hr"]
+    - ["Major (5)", "> Material", "Precarious", "Precarious", "Precarious", "1 hr", "4 hr"]
 
 - segment_type: "list_stack_sights"
   content:
     - description: |
         For determining likelihood, we use StackSight's commonly referenced scale:
-    - level: 1-2
-      description: "0-3% chance of happening in a year"
-    - level: 3-4
-      description: "4-10% chance of happening in a year"
-    - level: 5-6
-      description: "11-50% chance of happening in a year"
-    - level: 7-8
-      description: "51-90% chance of happening in a year"
-    - level: 9-10
-      description: "91-100% chance of happening in a year"
+    - level: 1
+      description: 1. "0-3% chance of happening in a year"
+    - level: 2
+      description: 2. "4-10% chance of happening in a year"
+    - level: 3
+      description: 3. "11-50% chance of happening in a year"
+    - level: 4
+      description: 4. "51-90% chance of happening in a year"
+    - level: 5
+      description: 5. "91-100% chance of happening in a year"
 
 - segment_type: "likelihood_table"
   content: