185 lines
8.1 KiB
Python
185 lines
8.1 KiB
Python
from openai import OpenAI
|
|
from django.conf import settings
|
|
from .models import Risk, Control
|
|
import time
|
|
|
|
def extract_organization_details(organization):
|
|
excluded_fields = {"name", "email"}
|
|
risk_data = {}
|
|
|
|
for field in organization._meta.get_fields():
|
|
if field.name not in excluded_fields and hasattr(organization, field.name):
|
|
value = getattr(organization, field.name)
|
|
if value:
|
|
help_text = getattr(field, 'help_text', '').strip()
|
|
key = help_text if help_text else field.name
|
|
risk_data[key] = value
|
|
return risk_data
|
|
|
|
def get_top_risk(organization):
|
|
client = OpenAI(api_key=settings.OPENAI_API_KEY)
|
|
|
|
all_risks = Risk.objects.all()
|
|
|
|
risk_list = []
|
|
for risk in all_risks:
|
|
risk_list.append(f"""
|
|
Risk ID: {risk.risk_id}
|
|
Category: {risk.category}
|
|
Name: {risk.risk_name}
|
|
Primary Impact: {risk.primary_impact}
|
|
Secondary Impact: {risk.secondary_impact}
|
|
Tertiary Impact: {risk.tretiary_impact}
|
|
Detection Difficulty: {risk.detection_difficulty}
|
|
Recovery Complexity: {risk.recovery_complexity}
|
|
Business Impact Severity: {risk.businnes_impact_severity}
|
|
""")
|
|
|
|
organization_details = extract_organization_details(organization)
|
|
|
|
prompt = f"""
|
|
You are an AI risk assessor. Based on the following company details and list of known risks,
|
|
identify the 10 most critical risks for this company. Respond only with risk IDs.
|
|
|
|
Company Details:
|
|
{organization_details}
|
|
|
|
List of Risks:
|
|
{risk_list}
|
|
|
|
Provide only the 10 most critical risk IDs in a simple comma-separated format, e.g "1,3,7,12,..."
|
|
"""
|
|
|
|
response = client.chat.completions.create(
|
|
model="gpt-4o-mini",
|
|
messages=[{"role": "system", "content": prompt}]
|
|
)
|
|
|
|
risk_ids = response.choices[0].message.content.strip().split(",")
|
|
print(f"Risks: {risk_ids}")
|
|
|
|
return [int(risk_id) for risk_id in risk_ids if risk_id.isdigit()]
|
|
|
|
def get_controls_for_risk(risk, organization):
|
|
client = OpenAI(api_key=settings.OPENAI_API_KEY)
|
|
all_controls = Control.objects.all()
|
|
organization_details = extract_organization_details(organization)
|
|
control_list = [f"Control ID: {control.id}, Control Name: {control.name}" for control in all_controls]
|
|
valid_control_ids = {control.id for control in all_controls}
|
|
control_map = {control.id: control.name for control in all_controls}
|
|
|
|
def fetch_controls(prompt):
|
|
response = client.chat.completions.create(
|
|
model="gpt-4o-mini",
|
|
messages=[{"role": "system", "content": prompt}]
|
|
)
|
|
return response.choices[0].message.content.strip()
|
|
|
|
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** based on its effectiveness in reducing the risk.
|
|
### Rules:
|
|
1. **Each control ID must be unique** (no duplicates).
|
|
2. **Only return control IDs and weights** in the exact format below.
|
|
3. **Weights must be between 1 and 10** (1 = low impact, 10 = high impact).
|
|
4. **Do NOT add explanations, descriptions, or extra text.**
|
|
5. **Ensure that control IDs are randomly distributed and diverse across different categories.**
|
|
### Available Controls:
|
|
{control_list}
|
|
|
|
### Expected Response Format (STRICTLY FOLLOW THIS FORMAT):
|
|
<control_id> : <weight>
|
|
<control_id> : <weight>
|
|
### Example Correct Response (NO DUPLICATES):
|
|
12 : 8
|
|
45 : 7
|
|
⚠️ **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.**
|
|
"""
|
|
|
|
selected_controls = []
|
|
control_ids_seen = set()
|
|
|
|
result = fetch_controls(prompt)
|
|
|
|
for line in result.split("\n"):
|
|
line = line.strip()
|
|
parts = line.split(":")
|
|
if len(parts) == 2:
|
|
control_id_str = parts[0].replace("ID:", "").replace("id:", "").replace("Id:", "").strip()
|
|
weight_str = parts[1].strip().replace("Weight:", "").replace("weight:", "").strip()
|
|
print(f"Control:{control_id_str} Weight:{weight_str}")
|
|
print(f"ControlType: {type(control_id_str)} WeightType: {type(weight_str)}")
|
|
|
|
control_id_str = ''.join(filter(str.isdigit, control_id_str))
|
|
weight_str = ''.join(filter(str.isdigit, weight_str))
|
|
|
|
if control_id_str and weight_str:
|
|
try:
|
|
control_id = int(control_id_str)
|
|
weight = int(weight_str)
|
|
|
|
if control_id in valid_control_ids and 1 <= weight <= 10 and control_id not in control_ids_seen:
|
|
selected_controls.append((control_id, weight))
|
|
control_ids_seen.add(control_id)
|
|
except ValueError:
|
|
continue
|
|
|
|
if len(selected_controls) == 10:
|
|
return selected_controls
|
|
|
|
while len(selected_controls) < 10:
|
|
missing_count = 10 - len(selected_controls)
|
|
remaining_controls = valid_control_ids - control_ids_seen
|
|
remaining_controls_list = [f"Control ID: {cid}, Control Name: {control_map[cid]}" for cid in remaining_controls]
|
|
|
|
retry_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 {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.
|
|
### Rules:
|
|
1. **Each control ID must be unique** (no duplicates).
|
|
2. **Only return control IDs and weights** in the exact format below.
|
|
3. **Weights must be between 1 and 10** (1 = low impact, 10 = high impact).
|
|
4. **Do NOT add explanations, descriptions, or extra text.**
|
|
5. **Ensure that control IDs are randomly distributed and diverse across different categories.**
|
|
### Available Controls:
|
|
{remaining_controls_list}
|
|
|
|
### Expected Response Format (STRICTLY FOLLOW THIS FORMAT):
|
|
<control_id> : <weight>
|
|
<control_id> : <weight>
|
|
### Example Correct Response (NO DUPLICATES):
|
|
12 : 8
|
|
45 : 7
|
|
⚠️ **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.**
|
|
"""
|
|
|
|
result = fetch_controls(retry_prompt)
|
|
for line in result.split("\n"):
|
|
line = line.strip()
|
|
parts = line.split(":")
|
|
if len(parts) == 2:
|
|
control_id_str = parts[0].replace("ID:", "").replace("id:", "").replace("Id:", "").strip()
|
|
weight_str = parts[1].strip().replace("Weight:", "").replace("weight:", "").strip()
|
|
print(f"Control:{control_id} Weight:{weight_str}")
|
|
print(f"ControlType: {type(control_id)} WeightType: {type(weight_str)}")
|
|
|
|
control_id_str = ''.join(filter(str.isdigit, control_id_str))
|
|
weight_str = ''.join(filter(str.isdigit, weight_str))
|
|
|
|
if control_id_str and weight_str:
|
|
try:
|
|
control_id = int(control_id_str)
|
|
weight = int(weight_str)
|
|
|
|
if control_id in valid_control_ids and 1 <= weight <= 10 and control_id not in control_ids_seen:
|
|
selected_controls.append((control_id, weight))
|
|
control_ids_seen.add(control_id)
|
|
except ValueError:
|
|
continue
|
|
|
|
if not remaining_controls:
|
|
break
|
|
return selected_controls if len(selected_controls) == 10 else []
|