498 lines
20 KiB
Python
498 lines
20 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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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 AI risk assessor. Based on the following company details and list of known risks,
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identify the 10 most critical risks for this company. Respond only with risk IDs.
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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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Provide only the 10 most critical risk IDs in a simple comma-separated format, e.g "1,3,7,12,..."
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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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risk_ids = response.choices[0].message.content.strip().split(",")
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return [int(risk_id) for risk_id in risk_ids if risk_id.isdigit()]
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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.name}" 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: control.name 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 an expert in cybersecurity risk management. Given the risk "{risk.risk_name}" and its associated organization details "{organization_details}",
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your task is to select **exactly 10 unique controls** from the provided list that best mitigate this risk. Each control should be assigned:
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- A weight between **1 and 5** (1 = low impact, 5 = high impact).
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- A likelihood score between **1 and 5** (1 = rare occurrence, 5 = highly likely).
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### Rules:
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1. **Each control ID must be unique** (no duplicates).
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2. **Only return control IDs, weights, and likelihood scores** in the exact format below.
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3. **Weights must be between 1 and 5** (1 = low impact, 5 = high impact).
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4. **Likelihood scores must be between 1 and 5** (1 = rare occurrence, 5 = highly likely).
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5. **Do NOT add explanations, descriptions, or extra text.**
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6. **Ensure that control IDs are randomly distributed and diverse across different categories.**
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### Available Controls:
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{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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<control_id> : <weight> : <likelihood>
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### Example Correct Response (NO DUPLICATES):
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12 : 5 : 2
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45 : 4 : 1
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⚠️ **If you provide duplicate control IDs, your response will be rejected. Ensure all control IDs are unique.**
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⚠️ **Follow the response format exactly. Any deviation will be considered invalid.**
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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 an expert in cybersecurity risk management. Given the risk "{risk.risk_name}" and the organization's details "{organization_details}",
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your task is to select **exactly {missing_count} unique controls** from the provided list that best mitigate this risk. Each control should be assigned:
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- A **weight** between **1 and 5** based on its effectiveness in reducing the risk.
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- A likelihood score between **1 and 5** (1 = rare occurrence, 5 = highly likely).
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### Rules:
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1. **Each control ID must be unique** (no duplicates).
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2. **Only return control IDs, weights, and likelihood scores** in the exact format below.
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3. **Weights must be between 1 and 5** (1 = low impact, 5 = high impact).
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4. **Likelihood scores must be between 1 and 5** (1 = rare occurrence, 5 = highly likely).
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5. **Do NOT add explanations, descriptions, or extra text.**
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6. **Ensure that control IDs are diverse and well-distributed across different categories.**
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### Available Controls:
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{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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<control_id> : <weight> : <likelihood>
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### Example Correct Response (NO DUPLICATES):
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12 : 4 : 5
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45 : 5 : 3
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⚠️ **If you provide duplicate control IDs, your response will be rejected. Ensure all control IDs are unique.**
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⚠️ **Follow the response format exactly. Any deviation will be considered invalid.**
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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__name, 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__name for reference—do NOT include or reference control IDs, years (e.g., 2024), or quarter references (Q1, Q2, Q3, Q4) anywhere in the output.
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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(
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(x - 0.5, y - 0.5), 1, 1,
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facecolor=score_color(score), edgecolor='#dddddd', linewidth=1.0, zorder=0
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)
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ax.add_patch(rect)
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text_color = '#000000' if 5 <= score <= 9 else '#ffffff'
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font_weight = 'bold' if score >= 15 else 'normal'
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ax.text(
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x, y, str(score),
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ha='center', va='center', fontsize=9,
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color=text_color, alpha=0.95, zorder=1, fontweight=font_weight
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)
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ax.set_xlabel('Likelihood', labelpad=10)
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ax.set_ylabel('Impact', labelpad=10)
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ax.set_xticks([])
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ax.set_yticks([])
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ax.tick_params(length=0)
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ax.grid(False)
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ax.spines['top'].set_visible(False)
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ax.spines['right'].set_visible(False)
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def generate_risk_graph(risks_with_controls):
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impacts = [risk['impact'] for risk in risks_with_controls]
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likelihoods = [risk['likelihood'] for risk in risks_with_controls]
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risk_ids = [risk['risk']['id'] for risk in risks_with_controls]
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fig, ax = plt.subplots(figsize=(10, 8))
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_draw_risk_matrix_background(ax)
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scatter = ax.scatter(
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likelihoods, impacts,
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c="#1f6feb", edgecolors="white", linewidths=1.5, s=420, alpha=0.95, zorder=3
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)
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for i, risk_id in enumerate(risk_ids):
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ax.annotate(
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str(risk_id),
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(likelihoods[i], impacts[i]),
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color="white",
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fontsize=12,
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ha="center",
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va="center",
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weight="bold",
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zorder=4,
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)
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buffer = io.BytesIO()
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plt.savefig(buffer, format="png", transparent=True, bbox_inches='tight', pad_inches=0.1)
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buffer.seek(0)
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image_png = buffer.getvalue()
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buffer.close()
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plt.close()
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return base64.b64encode(image_png).decode("utf-8")
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def generate_residual_risk_graph(risks_with_controls):
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impacts = [risk.get('residual_impact', 0) for risk in risks_with_controls]
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likelihoods = [risk.get('residual_likelihood', 0) for risk in risks_with_controls]
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risk_ids = [risk['risk']['id'] for risk in risks_with_controls]
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bg_img_path = find('img/graph_matrix.png')
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bg_img = mpimg.imread(bg_img_path)
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fig, ax = plt.subplots(figsize=(10, 8))
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ax.imshow(bg_img, extent=[0.0, 5.4, 0.0, 5.4], aspect='auto')
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scatter = ax.scatter(
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likelihoods, impacts,
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c="blue", edgecolors="white", s=500, alpha=0.9
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)
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for i, risk_id in enumerate(risk_ids):
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ax.annotate(
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str(risk_id),
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(likelihoods[i], impacts[i]),
|
||
color="white",
|
||
fontsize=12,
|
||
ha="center",
|
||
va="center",
|
||
weight="bold",
|
||
)
|
||
|
||
ax.set_xticks([])
|
||
ax.set_yticks([])
|
||
ax.set_xticklabels([])
|
||
ax.set_yticklabels([])
|
||
|
||
ax.spines['top'].set_visible(False)
|
||
ax.spines['right'].set_visible(False)
|
||
ax.spines['left'].set_visible(False)
|
||
ax.spines['bottom'].set_visible(False)
|
||
|
||
buffer = io.BytesIO()
|
||
plt.savefig(buffer, format="png", transparent=True, bbox_inches='tight', pad_inches=0)
|
||
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))
|
||
|