website/templates/organization/organization_analytics.html (2)

342-346: Consider adding ARIA attributes for accessibility.

The color-coding for risk scores improves visual understanding, but users with visual impairments might miss this information. Consider adding appropriate ARIA attributes or additional text indicators to ensure the severity level is accessible to all users.

<span class="text-3xl font-bold inline-block py-1 px-2 rounded-full
     {% if threat_intelligence.risk_score >= 75 %}text-red-600
     {% elif threat_intelligence.risk_score >= 50 %}text-yellow-600
     {% else %}text-green-600{% endif %}"
+    aria-label="Risk score: {{ threat_intelligence.risk_score }} out of 100, {% if threat_intelligence.risk_score >= 75 %}High risk{% elif threat_intelligence.risk_score >= 50 %}Medium risk{% else %}Low risk{% endif %}"
>
    {{ threat_intelligence.risk_score }}/100
</span>

---

372-373: Add tooltips for truncated alert descriptions.

Since you're using truncate class on alert descriptions, some text might be cut off. Consider adding tooltips to show the full description on hover.

<span class="w-2 h-2 rounded-full {% if alert.cve_score >= 8 %}bg-red-500{% elif alert.cve_score >= 5 %}bg-yellow-500{% else %}bg-orange-500{% endif %}"></span>
-<span class="text-sm text-gray-600 truncate">{{ alert.description }}</span>
+<span class="text-sm text-gray-600 truncate" title="{{ alert.description }}">{{ alert.description }}</span>

website/views/company.py (2)

246-246: Risk score calculation could be more robust.

The current risk score calculation uses a simple ratio of critical issues to total issues. Consider implementing a weighted calculation that factors in multiple severity levels.

-risk_score = min(100, (critical_issues / total_issues * 100) if total_issues > 0 else 0)
+# Calculate weighted risk score factoring in multiple severity levels
+high_severity = security_issues.filter(cve_score__gte=8).count()
+medium_severity = security_issues.filter(cve_score__gte=5, cve_score__lt=8).count()
+low_severity = security_issues.filter(cve_score__gt=0, cve_score__lt=5).count()
+
+# Apply weights: high=1.0, medium=0.5, low=0.25
+weighted_score = (high_severity * 1.0 + medium_severity * 0.5 + low_severity * 0.25)
+weighted_total = total_issues if total_issues > 0 else 1
+risk_score = min(100, int((weighted_score / weighted_total) * 100))

---

462-462: Consider using context.update with multiple key-value pairs.

For better readability when adding multiple context variables, consider using the update() method with a dictionary containing all new entries rather than updating with a single item.

-context.update({"threat_intelligence": self.get_threat_intelligence(id)})
+context.update({
+    "threat_intelligence": self.get_threat_intelligence(id),
+    # Add other future context variables here
+})

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

website/templates/organization/organization_analytics.html (2)

363-363: Minor typo in class name attribute.

There's a typo in the class name where "semibold" is misspelled as "semibold".

-    <h3 class="text-lg font-semibold text-gray-800 mb-2">Recent Security Alerts</h3>
+    <h3 class="text-lg font-semibold text-gray-800 mb-2">Recent Security Alerts</h3>

---

350-350: Same typo in another class name attribute.

The same "semibold" typo appears in this heading as well.

-    <h3 class="text-lg font-semibold text-gray-800 mb-2">Top Issue Categories</h3>
+    <h3 class="text-lg font-semibold text-gray-800 mb-2">Top Issue Categories</h3>

website/views/company.py (1)

455-455: Consider initializing the context with all values.

Instead of updating the context after initialization, consider including the threat intelligence data directly in the initial context dictionary for better readability.

        context = {
            "organization": id,
            "organizations": organizations,
            "organization_obj": organization_obj,
            "total_info": self.get_general_info(id),
            "bug_report_type_piechart_data": self.get_bug_report_type_piechart_data(id),
            "reports_on_domain_piechart_data": self.get_reports_on_domain_piechart_data(id),
            "get_current_year_monthly_reported_bar_data": self.get_current_year_monthly_reported_bar_data(id),
            "bug_rate_increase_descrease_weekly": self.bug_rate_increase_descrease_weekly(id),
            "accepted_bug_rate_increase_descrease_weekly": self.bug_rate_increase_descrease_weekly(id, True),
            "spent_on_bugtypes": self.get_spent_on_bugtypes(id),
            "security_incidents_summary": self.get_security_incidents_summary(id),
+           "threat_intelligence": self.get_threat_intelligence(id),
        }
-       context.update({"threat_intelligence": self.get_threat_intelligence(id)})

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro

website/views/company.py (2)

245-246: Consider adding comments explaining score calculation.

The risk score calculation logic could benefit from additional comments explaining why cve_score >= 8 is chosen as the threshold for critical issues and how the score formula was determined.

# Calculate risk score (0-100)
total_issues = security_issues.count()
critical_issues = security_issues.filter(cve_score__gte=8).count()  # Use cve_score instead of severity
+ # Calculate risk score as percentage of critical issues (CVE score ≥ 8), capped at 100
risk_score = min(100, (critical_issues / total_issues * 100) if total_issues > 0 else 0)

---

228-262: Consider optimizing database queries.

The method makes multiple database queries to fetch security issues. Consider retrieving the data once and reusing it to improve performance.

def get_threat_intelligence(self, organization):
    """Gets threat intelligence data for the organization."""
    security_issues = Issue.objects.filter(
        domain__organization__id=organization,
        label=4,  # Security label
    )
+    
+    # Calculate counts once to avoid multiple queries
+    total_issues = security_issues.count()
+    critical_issues = security_issues.filter(cve_score__gte=8).count()
+    
+    # Pre-fetch recent alerts in a single query
+    recent_alerts = security_issues.filter(
+        created__gte=timezone.now() - timedelta(days=7),
+        cve_score__gte=7,  # Use cve_score for severity
+    ).order_by("-created")[:5]

    # Get trending attack types based on issue labels/tags instead
    attack_vectors = (
        security_issues.filter(created__gte=timezone.now() - timedelta(days=90))
        .values("label")  # Use label instead of vulnerability_type
        .annotate(count=Count("id"))
        .order_by("-count")[:5]
    )

    # Calculate risk score (0-100)
-    total_issues = security_issues.count()
-    critical_issues = security_issues.filter(cve_score__gte=8).count()  # Use cve_score instead of severity
    risk_score = min(100, (critical_issues / total_issues * 100) if total_issues > 0 else 0)

    return {
        "attack_vectors": [
            {
                "vulnerability_type": self.get_label_name(vector["label"]),  # Convert label to readable name
                "count": vector["count"],
            }
            for vector in attack_vectors
        ],
        "risk_score": int(risk_score),
-        "recent_alerts": security_issues.filter(
-            created__gte=timezone.now() - timedelta(days=7),
-            cve_score__gte=7,  # Use cve_score for severity
-        ).order_by("-created")[:5],
+        "recent_alerts": recent_alerts,
    }

Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro