585 lines
24 KiB
Python
585 lines
24 KiB
Python
from openai import OpenAI
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from django.conf import settings
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from .models import Risk, Control
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from weasyprint import HTML
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from django.http import HttpResponse
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from PIL import Image
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import io
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import base64
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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from django.contrib.staticfiles.finders import find
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import matplotlib.image as mpimg
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site_domain = settings.SITE_DOMAIN
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import random
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import re
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def extract_organization_details(organization):
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excluded_fields = {"name", "email"}
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risk_data = {}
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for field in organization._meta.get_fields():
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if field.name not in excluded_fields and hasattr(organization, field.name):
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value = getattr(organization, field.name)
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if value:
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help_text = getattr(field, 'help_text', '').strip()
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key = help_text if help_text else field.name
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risk_data[key] = value
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return risk_data
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def get_top_risk(organization):
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client = OpenAI(api_key=settings.OPENAI_API_KEY)
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all_risks = Risk.objects.all()
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risk_list = []
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for risk in all_risks:
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risk_list.append(f"""
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Risk ID: {risk.risk_id}
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Category: {risk.category}
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Name: {risk.risk_name}
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Primary Impact: {risk.primary_impact}
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Secondary Impact: {risk.secondary_impact}
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Tertiary Impact: {risk.tretiary_impact}
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Detection Difficulty: {risk.detection_difficulty}
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Recovery Complexity: {risk.recovery_complexity}
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Business Impact Severity: {risk.businnes_impact_severity}
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""")
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organization_details = extract_organization_details(organization)
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prompt = f"""
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You are an expert cybersecurity risk analyst. Your task is to identify
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the top 10 most critical cybersecurity risks for a client based on
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their specific company profile and a comprehensive risk catalog. Your
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analysis must be logical, evidence-based, and directly tied to the
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client's details.
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Methodology:
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Analyze the Company Profile: Carefully review all details provided
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about the company, including its industry, size (revenue and
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employees), IT dependency, regulatory requirements, and operational
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characteristics (e.g., remote work, third-party vendors, internal
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development).
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Evaluate the Risk Catalog: Review the provided list of known risks.
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Map Profile to Risks: Correlate specific details from the company
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profile to the risks in the catalog. For example:
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A company in the Financial sector subject to GDPR is highly
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susceptible to "Privacy Regulation Violation" (Risk ID 61).
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A company with significant "Internal Software Development" is more
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vulnerable to "CI/CD Pipeline Compromise" (Risk ID 30) and "Source
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Code Exposure" (Risk ID 9).
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High dependency on a "Cloud Provider" increases the criticality of
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"Cloud Provider Service Outage" (Risk ID 20).
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Prioritize by Impact: Determine the most critical risks by assessing
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the potential impact (financial, operational, reputational, and
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regulatory) on this specific company. A risk is critical if it poses a
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severe threat to the company's core operations, data, or compliance
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standing.
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Final Selection: Select the 10 risks with the highest criticality and
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provide a clear, concise justification for each choice.
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Company Details:
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{organization_details}
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List of Risks:
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{risk_list}
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Required Output Format:
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Provide your response as a numbered list from 1 to 10. For each item,
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include the Risk ID, the Risk Name, and a brief, one-sentence
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justification that links a specific company detail to why that risk is
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critical.
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Example:
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Risk ID 18 (Ransomware Infection): Critical due to the company's high
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IT dependency and the severe operational and financial impact a
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ransomware event would cause.
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Risk ID 61 (Privacy Regulation Violation): Critical because the
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company operates under GDPR, making any breach of personal data a
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significant legal and financial liability.
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"""
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=[{"role": "system", "content": prompt}]
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)
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content = response.choices[0].message.content.strip()
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matches = re.findall(
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r'Risk ID\s*(\d+)\s*\((.*?)\)\*\*:\s*(.+?)(?=\n\d+\.|\Z)', content, re.DOTALL
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)
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results = []
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for risk_id, risk_name, explanation in matches:
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results.append({
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"risk_id": int(risk_id),
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"risk_name": risk_name.strip(),
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"explanation": explanation.strip()
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})
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return results
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def get_controls_for_risk(risk, organization):
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client = OpenAI(api_key=settings.OPENAI_API_KEY)
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all_controls = Control.objects.all()
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organization_details = extract_organization_details(organization)
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control_list = [f"Control ID: {control.id}, Control Name: {control.subcategory} - {control.function or ''}".rstrip(" -") for control in all_controls]
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valid_control_ids = {control.id for control in all_controls}
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control_map = {control.id: (f"{control.subcategory} - {control.function or ''}").rstrip(" -") for control in all_controls}
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def fetch_controls(prompt):
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=[{"role": "system", "content": prompt}]
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)
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return response.choices[0].message.content.strip()
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prompt = f"""
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You are a senior cybersecurity risk consultant. Your objective is to
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analyze the risk "{risk.risk_name}" in the context of the
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organization's profile and recommend the 10 most effective mitigating
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controls from the provided list.
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For each of the 10 selected controls, you must assign two scores from 1 to 5:
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* **Weight (1-5):** This score represents the control's
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**effectiveness in reducing the potential impact** of the risk.
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* **1 (Low Impact Reduction):** A supplementary control with a
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minor effect.
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* **3 (Moderate Impact Reduction):** A standard control that
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significantly reduces impact.
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* **5 (High Impact Reduction):** A critical control that is
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highly effective at minimizing the damage from this risk.
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* **Likelihood (1-5):** This score represents the
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control's **effectiveness in reducing the likelihood** that the risk
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event will occur.
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* **1 (Low Likelihood Reduction):** The control has a minimal
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effect on preventing the event.
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* **3 (Moderate Likelihood Reduction):** The control makes the
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event considerably less likely.
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* **5 (High Likelihood Reduction):** The control is a primary
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defense that makes the event much less likely to happen.
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**Rules:**
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1. You must select **exactly 10 unique controls**. No duplicates.
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2. Your output must **only** contain the control ID, Weight, and
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Likelihood Reduction score.
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3. Strictly adhere to the specified format. Do not add any
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explanations or extra text.
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---
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**Risk to Analyze:** {risk.risk_name}
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**Organization Details:** {organization_details}
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**Available Controls:** {control_list}
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**Expected Response Format (STRICTLY FOLLOW THIS FORMAT):**
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<control_id> : <Weight> : <Likelihood>
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**Example Correct Response:**
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12 : 5 : 4
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45 : 4 : 5
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"""
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selected_controls = []
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control_ids_seen = set()
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result = fetch_controls(prompt)
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for line in result.split("\n"):
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line = line.strip()
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parts = line.split(":")
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if len(parts) == 3:
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control_id_str = parts[0].replace("ID:", "").replace("id:", "").replace("Id:", "").strip()
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weight_str = parts[1].strip().replace("Weight:", "").replace("weight:", "").strip()
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likelihood_str = parts[2].strip().replace("Likelihood:", "").replace("likelihood:", "").strip()
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control_id_str = ''.join(filter(str.isdigit, control_id_str))
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weight_str = ''.join(filter(str.isdigit, weight_str))
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likelihood_str = ''.join(filter(str.isdigit, likelihood_str))
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if control_id_str and weight_str and likelihood_str:
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try:
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control_id = int(control_id_str)
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weight = int(weight_str)
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likelihood = int(likelihood_str)
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if control_id in valid_control_ids and 1 <= weight <= 5 and 1 <= likelihood <= 5 and control_id not in control_ids_seen:
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selected_controls.append((control_id, weight, likelihood))
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control_ids_seen.add(control_id)
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except ValueError:
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continue
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if len(selected_controls) == 10:
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return selected_controls
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while len(selected_controls) < 10:
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missing_count = 10 - len(selected_controls)
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remaining_controls = valid_control_ids - control_ids_seen
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remaining_controls_list = [f"Control ID: {cid}, Control Name: {control_map[cid]}" for cid in remaining_controls]
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retry_prompt = f"""
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You are a senior cybersecurity risk consultant. Your objective is to
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analyze the risk "{risk.risk_name}" in the context of the
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organization's profile and recommend the {missing_count} umost effective mitigating
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controls from the provided list.
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For each of the {missing_count} selected controls, you must assign two scores from 1 to 5:
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* **Weight (1-5):** This score represents the control's
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**effectiveness in reducing the potential impact** of the risk.
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* **1 (Low Impact Reduction):** A supplementary control with a
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minor effect.
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* **3 (Moderate Impact Reduction):** A standard control that
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significantly reduces impact.
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* **5 (High Impact Reduction):** A critical control that is
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highly effective at minimizing the damage from this risk.
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* **Likelihood (1-5):** This score represents the
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control's **effectiveness in reducing the likelihood** that the risk
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event will occur.
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* **1 (Low Likelihood Reduction):** The control has a minimal
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effect on preventing the event.
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* **3 (Moderate Likelihood Reduction):** The control makes the
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event considerably less likely.
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* **5 (High Likelihood Reduction):** The control is a primary
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defense that makes the event much less likely to happen.
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**Rules:**
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1. You must select **exactly 10 unique controls**. No duplicates.
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2. Your output must **only** contain the control ID, Weight, and
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Likelihood score.
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3. Strictly adhere to the specified format. Do not add any
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explanations or extra text.
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---
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**Risk to Analyze:** {risk.risk_name}
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**Organization Details:** {organization_details}
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**Available Controls:** {remaining_controls_list}
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**Expected Response Format (STRICTLY FOLLOW THIS FORMAT):**
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<control_id> : <Weight> : <Likelihood>
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**Example Correct Response:**
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12 : 5 : 4
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45 : 4 : 5
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"""
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result = fetch_controls(retry_prompt)
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for line in result.split("\n"):
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line = line.strip()
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parts = line.split(":")
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if len(parts) == 3:
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control_id_str = parts[0].replace("ID:", "").replace("id:", "").replace("Id:", "").strip()
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weight_str = parts[1].strip().replace("Weight:", "").replace("weight:", "").strip()
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likelihood_str = parts[2].strip().replace("Likelihood:", "").replace("likelihood:", "").strip()
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control_id_str = ''.join(filter(str.isdigit, control_id_str))
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weight_str = ''.join(filter(str.isdigit, weight_str))
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likelihood_str = ''.join(filter(str.isdigit, likelihood_str))
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if control_id_str and weight_str and likelihood_str:
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try:
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control_id = int(control_id_str)
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weight = int(weight_str)
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likelihood = int(likelihood_str)
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if control_id in valid_control_ids and 1 <= weight <= 5 and 1 <= likelihood <= 5 and control_id not in control_ids_seen:
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selected_controls.append((control_id, weight, likelihood))
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control_ids_seen.add(control_id)
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except ValueError:
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continue
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if not remaining_controls:
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break
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return selected_controls if len(selected_controls) == 10 else []
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def generate_recommendations(risks_with_controls, organization):
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client = OpenAI(api_key=settings.OPENAI_API_KEY)
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organization_details = extract_organization_details(organization)
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prompt = f"""
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You are an AI assistant tasked with generating the Recommendations section for a cybersecurity assessment report. Use the organization’s context and the list of risks with their proposed controls to produce concise, actionable, and prioritized guidance.
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Inputs:
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- Organization details:
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{organization_details}
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- Risks with controls (Python-like list of dicts). Each item includes:
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risk: id, name, category, risk_description (or similar)
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r_impact (inherent impact 1–5), r_likelihood (inherent likelihood 1–5), risk_score
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residual_impact, residual_likelihood, residual_risk_score (may be present)
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controls: list of controls, each with control__subcategory and control__function, weight (1–5 effectiveness), likelihood (1–5 occurrence modifier)
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Task:
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1) Compute a priority score per control = weight × likelihood. Aggregate scores across all risks and cluster into 3–5 thematic areas that best match the actual controls and risk names (e.g., Access Control & MFA, Patch & Vulnerability Management, Vendor/Third-Party Risk Management, Network Security & Segmentation, Logging/Monitoring/Detection, Incident Response & BCDR, Ransomware Prevention & Recovery, Cryptography & Key Management). Do not invent themes without support in the inputs.
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2) For each chosen theme, produce 3–5 concrete actions derived from the highest-priority controls. Tailor to the organization_details where appropriate. Prefer steps that reduce both likelihood and impact.
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3) Each bullet should be 1–2 sentences: start with a clear, imperative recommendation, and (optionally) add a brief explanation or context. Still keep it concise and actionable.
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4) Use only the control label (i.e., "subcategory - function") for reference—do NOT include or reference control IDs, years, or quarter references (Q1, Q2, Q3, Q4).
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5) Do not introduce controls that are not represented in the provided controls list.
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Output format (STRICT):
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<2–3 sentence paragraph explaining that recommendations are prioritized by expected risk reduction based on the provided controls and aligned to the organization’s context.>
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<h3>Theme Title</h3>
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- Bullet 1 (1–2 sentences, no IDs, years, or quarters)
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- Bullet 2
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- Bullet 3
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- Bullet 4 (optional)
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- Bullet 5 (optional)
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Constraints:
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- 3–5 themed subsections, each with 3–5 bullets.
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- No preamble or postscript beyond the sections above.
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- Do NOT reference or display control IDs, years, or quarters in any form.
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Now produce the final Recommendations section using the actual inputs above.
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Risks with controls:
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{risks_with_controls}
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"""
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=[{"role": "system", "content": prompt}]
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)
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recommendations = response.choices[0].message.content.strip()
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return recommendations
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def generate_key_findings(document, top_10_risks):
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client = OpenAI(api_key=settings.OPENAI_API_KEY)
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def extract_organization_details(organization):
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excluded_fields = {"email"}
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risk_data = {}
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for field in organization._meta.get_fields():
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if field.name not in excluded_fields and hasattr(organization, field.name):
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value = getattr(organization, field.name)
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if value:
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help_text = getattr(field, 'help_text', '').strip()
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key = help_text if help_text else field.name
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risk_data[key] = value
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return risk_data
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organization_details = extract_organization_details(document.organization)
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prompt = f"""
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You are an AI assistant tasked with generating a "Key Findings" section for a cybersecurity assessment report. Your output must be structured precisely, extracting and presenting the top 3 risks.
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From the following list of risks, select the 3 most critical for the organization and generate the as specified.
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List of risks:
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{top_10_risks}
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Organization details:
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{organization_details}
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Introduction: The description field must begin with the following exact text:
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"The assessment revealed several areas where { document.organization.name } faces heightened cybersecurity risks. These risks pose significant threats to operational continuity, sensitive data, and regulatory compliance. The top risks identified are:"
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Risk Presentation:
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Identify the top 3 risks from the list above.
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For each of these top 3 risks, present it as a bulleted item within the description field, following this format:
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"- [Risk Name]: [Concise, professionally phrased description of the risk's significance in context of the organization, likelihood, or impact.]"
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Description Derivation:
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The [Risk Name] part should be the actual name of the risk from the input data (e.g., {{ item.risk.name }}).
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The [Concise, professionally phrased description] part must be synthesized from the provided risk_description field (e.g., {{ item.risk_description }}) associated with that risk. Aim to create a polished, impactful summary that clearly explains the risk's context, severity, or contributing factors.
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Return it as plain text in the following format:
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Output Format(STRICT):
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Introduction
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- <b> Risk 1 </b> : Brief description of Risk 1
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- <b> Risk 2 </b> : Brief description of Risk 2
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- <b> Risk 3 </b> : Brief description of Risk 3
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"""
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=[{"role": "system", "content": prompt}]
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)
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key_findings = response.choices[0].message.content.strip()
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return key_findings
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def generate_pdf(document):
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document_link = f"{site_domain}/document/{document.id}/"
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pdf_content = HTML(url=document_link).write_pdf()
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response = HttpResponse(pdf_content, content_type='application/pdf')
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response['Content-Disposition'] = f'inline; filename=document_{document.id}.pdf'
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return response
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def generate_first_page_image(document):
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document_link = f"{site_domain}/document/{document.id}/"
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pdf_bytes = HTML(url=document_link).write_pdf()
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from pdf2image import convert_from_bytes
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images = convert_from_bytes(pdf_bytes, first_page=1, last_page=1)
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img_io = io.BytesIO()
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images[0].save(img_io, format="JPEG", quality=90)
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img_io.seek(0)
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return img_io
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def calculate_aggregate_weight(controls):
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total_weight = sum(control['weight']for control in controls)
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return total_weight
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def calculate_aggregate_likelihood(controls):
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total_likelihood = sum(control['likelihood'] for control in controls)
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return total_likelihood
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def map_weight_to_impact_likelihood(total_weight, total_likelihood, max_weight):
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impact = min(5.0, max(1.0, total_weight / 10.0))
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likelihood = min(5.0, max(1.0, total_likelihood / 10.0))
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return impact, likelihood
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def _draw_risk_matrix_background(ax):
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ax.set_xlim(0.5, 5.5)
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ax.set_ylim(0.5, 5.5)
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ax.set_aspect('equal')
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def score_color(score: int) -> str:
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if score <= 2:
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return '#1abc9c'
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if score <= 4:
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return '#2ecc71'
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if score <= 9:
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return '#f1c40f'
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if score <= 15:
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return '#f39c12'
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return '#e74c3c'
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for y in range(1, 6):
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for x in range(1, 6):
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score = x * y
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rect = plt.Rectangle(
|
||
(x - 0.5, y - 0.5), 1, 1,
|
||
facecolor=score_color(score), edgecolor='#dddddd', linewidth=1.0, zorder=0
|
||
)
|
||
ax.add_patch(rect)
|
||
|
||
|
||
text_color = '#000000' if 5 <= score <= 9 else '#ffffff'
|
||
font_weight = 'bold' if score >= 15 else 'normal'
|
||
ax.text(
|
||
x, y, str(score),
|
||
ha='center', va='center', fontsize=9,
|
||
color=text_color, alpha=0.95, zorder=1, fontweight=font_weight
|
||
)
|
||
|
||
ax.set_xlabel('Likelihood', labelpad=10)
|
||
ax.set_ylabel('Impact', labelpad=10)
|
||
|
||
ax.set_xticks([])
|
||
ax.set_yticks([])
|
||
|
||
ax.tick_params(length=0)
|
||
ax.grid(False)
|
||
ax.spines['top'].set_visible(False)
|
||
ax.spines['right'].set_visible(False)
|
||
|
||
|
||
def generate_risk_graph(risks_with_controls):
|
||
impacts = [risk['impact'] for risk in risks_with_controls]
|
||
likelihoods = [risk['likelihood'] for risk in risks_with_controls]
|
||
risk_ids = [risk['risk']['id'] for risk in risks_with_controls]
|
||
|
||
fig, ax = plt.subplots(figsize=(10, 8))
|
||
|
||
_draw_risk_matrix_background(ax)
|
||
|
||
scatter = ax.scatter(
|
||
likelihoods, impacts,
|
||
c="#1f6feb", edgecolors="white", linewidths=1.5, s=420, alpha=0.95, zorder=3
|
||
)
|
||
|
||
for i, risk_id in enumerate(risk_ids):
|
||
ax.annotate(
|
||
str(risk_id),
|
||
(likelihoods[i], impacts[i]),
|
||
color="white",
|
||
fontsize=12,
|
||
ha="center",
|
||
va="center",
|
||
weight="bold",
|
||
zorder=4,
|
||
)
|
||
|
||
buffer = io.BytesIO()
|
||
plt.savefig(buffer, format="png", transparent=True, bbox_inches='tight', pad_inches=0.1)
|
||
buffer.seek(0)
|
||
image_png = buffer.getvalue()
|
||
buffer.close()
|
||
plt.close()
|
||
|
||
return base64.b64encode(image_png).decode("utf-8")
|
||
|
||
def generate_residual_risk_graph(risks_with_controls):
|
||
impacts = [max(risk.get('impact', 0) - 1.0, 1.0) if risk.get('impact') else 2 for risk in risks_with_controls]
|
||
likelihoods = [max(risk.get('likelihood', 0) - 1.0, 1.0) if risk.get('likelihood') else 2 for risk in risks_with_controls]
|
||
risk_ids = [risk['risk']['id'] for risk in risks_with_controls]
|
||
|
||
fig, ax = plt.subplots(figsize=(10, 8))
|
||
|
||
_draw_risk_matrix_background(ax)
|
||
|
||
scatter = ax.scatter(
|
||
likelihoods, impacts,
|
||
c="#000000", edgecolors="white", linewidths=1.5, s=420, alpha=0.95, zorder=3
|
||
)
|
||
|
||
for i, risk_id in enumerate(risk_ids):
|
||
ax.annotate(
|
||
str(risk_id),
|
||
(likelihoods[i], impacts[i]),
|
||
color="white",
|
||
fontsize=12,
|
||
ha="center",
|
||
va="center",
|
||
weight="bold",
|
||
zorder=4,
|
||
)
|
||
|
||
buffer = io.BytesIO()
|
||
plt.savefig(buffer, format="png", transparent=True, bbox_inches='tight', pad_inches=0.1)
|
||
buffer.seek(0)
|
||
image_png = buffer.getvalue()
|
||
buffer.close()
|
||
plt.close()
|
||
|
||
return base64.b64encode(image_png).decode("utf-8")
|
||
|
||
def generate_demo_code(length=6):
|
||
chars = 'ABCDEFGHJKLMNPQRSTUVWXYZ23456789'
|
||
return ''.join(random.choices(chars, k=length))
|
||
|