Post-Security Review Critical Path Analysis

Date: 2025-11-24 Context: Infrastructure deployment complete, security hardening planned Purpose: Define optimal path from infrastructure → working application → production security

Executive Summary

Current State:

✅ Infrastructure: 100% complete (GKE, Cloud SQL, Redis, VPC, Secret Manager, Cloud KMS)
❌ Application: 0% deployed (no backend running on GKE)
❌ Security Hardening: 0% implemented (current score: 35/100, target: 95/100)

Critical Decision: Deploy backend application first, then layer security hardening.

Timeline: 5-6 days total

Days 1-2: Backend deployment and functionality testing
Days 3-4: Security hardening (7 layers)
Day 5: Verification and documentation

Rationale: Faster path to working MVP, easier debugging, incremental security implementation with lower risk of over-engineering before testing real traffic patterns.

📊 Infrastructure Status Review

✅ Completed Infrastructure (Deployed 2025-11-24)

Total Deployment Time: 12 minutes 40 seconds

Component	Status	Configuration	Purpose
GKE Cluster	✅ Running	`coditect-citus-dev` (us-central1, 3 zones)	Container orchestration
Node Pool	✅ Active	Auto-scaling 1-5 nodes (n1-standard-2, preemptible)	Compute capacity
Cloud SQL	✅ Operational	PostgreSQL 16, db-custom-2-7680, Regional HA	License and tenant data
Redis	✅ Active	6GB BASIC tier, RDB persistence	Session tracking, atomic seat counting
VPC Network	✅ Configured	Private subnets with Cloud NAT	Network isolation
Secret Manager	✅ Populated	9 secrets stored	Secure credential storage
Cloud KMS	✅ Tested	RSA-4096 asymmetric signing	License tamper-proofing
Firewall Rules	✅ Active	Health checks, SSH, HTTPS, GKE webhooks	Network security

Monthly Cost: ~$310/month (dev environment with cost optimizations)

Kubernetes Access:

gcloud container clusters get-credentials coditect-citus-dev \
  --region us-central1 \
  --project coditect-citus-prod

🎯 Critical Path Analysis

Current Blocker Identification

Primary Blocker: No application deployed to infrastructure

GKE cluster is running but empty (no workloads)
Database and Redis are ready but unused
Ingress IP exists (34.8.51.57) but not configured for licenses.coditect.ai
Cannot test license flow without backend

Secondary Concern: Security posture inadequate for production

Current security score: 35/100 (target: 95/100)
No Cloud Armor WAF (DDoS vulnerable)
No rate limiting (abuse vulnerable)
No edge authentication (unrestricted access)

Decision Point: Which blocker to address first?

🔍 Critical Path Options Analysis

Option A: Application First, Security Second (RECOMMENDED)

Timeline:

Day 1-2: Deploy Backend Application
├── Hour 1-4: Create Kubernetes manifests (Deployment, Service, ConfigMap)
├── Hour 5-6: Build and push Docker image to Artifact Registry
├── Hour 7-10: Deploy to GKE, troubleshoot, verify pods running
├── Hour 11-12: Configure basic ingress for licenses.coditect.ai
└── Hour 13-16: End-to-end testing (license acquisition, heartbeat, release)

Day 3-4: Security Hardening (7 Layers)
├── Day 3 AM: Cloud Armor WAF (8 hours)
│   ├── Deploy OpenTofu security policy
│   ├── OWASP Top 10 protection rules
│   ├── Rate limiting (100 req/min per IP)
│   └── Geo-blocking (US/EU allowlist)
├── Day 3 PM: Backend Security + Ingress (5 hours)
│   ├── Connection draining and session affinity
│   ├── Host-based routing (shared IP deployment)
│   └── SSL certificate management
└── Day 4: Kubernetes NetworkPolicy + Application Security (5 hours)
    ├── Pod isolation and default deny policies
    ├── Django JWT middleware
    └── Multi-tenant row-level isolation

Day 5: Verification
├── End-to-end integration tests
├── Load testing (100+ concurrent users)
├── Security audit and penetration testing
└── Documentation and runbooks

Advantages:

✅ Faster Time-to-Value: Working MVP in 2 days vs 4 days
✅ Easier Debugging: Separate application issues from security issues
✅ Risk Reduction: Validate architecture with real traffic before hardening
✅ Iterative Security: Can adjust security based on observed patterns
✅ Team Velocity: Unblocks testing and integration work immediately

Trade-offs:

⚠️ Temporary Security Gap: 2-day window with minimal security (acceptable in dev environment)
⚠️ Potential Rework: May need to adjust security if application patterns differ from assumptions

Risk Mitigation:

Deploy to dev environment only (not production)
Limit access to known IPs during initial testing
Run security scan immediately after deployment
Implement logging and monitoring from day 1

Option B: Security First, Application Second (NOT RECOMMENDED)

Timeline:

Day 1-3: Full Security Hardening
├── Cloud Armor WAF (8 hours)
├── Backend Security Policy (2 hours)
├── Unified Ingress (3 hours)
├── Kubernetes NetworkPolicy (2 hours)
├── Application security preparation (3 hours)
└── Data layer and observability (remaining time)

Day 4-5: Deploy Backend to Hardened Environment
├── Create Kubernetes manifests
├── Build and push Docker image
├── Deploy to GKE
└── Debug issues (harder due to security layers)

Day 6: Integration Testing and Fixes

Advantages:

✅ Maximum Security: Protected from day 1
✅ Compliance: Meets security requirements immediately

Disadvantages:

❌ Slower Time-to-Value: 4-5 days to working system vs 2 days
❌ Complex Debugging: Hard to separate security issues from application issues
❌ Over-Engineering Risk: May configure security for patterns that don't match reality
❌ Blocked Progress: Cannot test or iterate until everything is ready
❌ Higher Failure Risk: All-or-nothing approach with no incremental validation

✅ Recommended Path: Option A

Justification

1. Development Environment Context

This is a dev environment, not production
Can afford brief security gap for faster iteration
Real production will have full security from day 1

2. Architectural Validation

Need to validate license flow works as designed
Traffic patterns inform security configuration
Database schema may need adjustments based on real usage

3. Team Efficiency

Frontend and integration work can start immediately once backend is deployed
Parallel work streams (backend testing + security hardening documentation)
Reduces risk of context switching

4. Risk Management

Security hardening is well-documented (120+ tasks in tasklist.md)
Can implement security quickly once backend is stable
Easier to debug application vs debugging application + security simultaneously

📋 Phase 1: Backend Deployment (Days 1-2)

Prerequisites Check

Required Files (check existence):

Dockerfile in backend repository
requirements.txt with all dependencies
Database migration scripts
Environment configuration templates

GCP Setup:

# Verify access
gcloud auth list
gcloud config get-value project  # Should be coditect-citus-prod

# Configure kubectl
gcloud container clusters get-credentials coditect-citus-dev \
  --region us-central1 \
  --project coditect-citus-prod

# Verify cluster
kubectl get nodes  # Should show 3 nodes

Task 1: Create Kubernetes Manifests (4 hours)

Location: submodules/cloud/coditect-cloud-backend/k8s/ (or create if not exists)

Files to Create:

1.1 ConfigMap (`configmap.yaml`)

apiVersion: v1
kind: ConfigMap
metadata:
  name: license-api-config
  namespace: default
data:
  DJANGO_SETTINGS_MODULE: "license_platform.settings.production"
  ALLOWED_HOSTS: "licenses.coditect.ai,34.8.51.57"
  CORS_ALLOWED_ORIGINS: "https://licenses.coditect.ai,https://api.coditect.ai"
  DATABASE_HOST: "10.x.x.x"  # Cloud SQL private IP
  DATABASE_PORT: "5432"
  DATABASE_NAME: "coditect"
  REDIS_HOST: "10.x.x.x"  # Redis private IP
  REDIS_PORT: "6378"
  REDIS_DB: "0"
  KMS_KEY_NAME: "projects/PROJECT_ID/locations/LOCATION/keyRings/KEYRING/cryptoKeys/KEY"

1.2 Secret (reference from Secret Manager)

apiVersion: v1
kind: Secret
metadata:
  name: license-api-secrets
  namespace: default
type: Opaque
data:
  DATABASE_PASSWORD: <base64>  # Get from Secret Manager
  REDIS_AUTH_STRING: <base64>  # Get from Secret Manager
  DJANGO_SECRET_KEY: <base64>  # Get from Secret Manager

Better Approach: Use Workload Identity + Secret Manager CSI driver (implement in Phase 2)

1.3 Deployment (`deployment.yaml`)

apiVersion: apps/v1
kind: Deployment
metadata:
  name: license-api
  namespace: default
  labels:
    app: license-api
    version: v1
spec:
  replicas: 2
  selector:
    matchLabels:
      app: license-api
  template:
    metadata:
      labels:
        app: license-api
        version: v1
    spec:
      serviceAccountName: license-api-sa  # For Workload Identity
      containers:
      - name: django
        image: gcr.io/coditect-citus-prod/license-api:latest
        imagePullPolicy: Always
        ports:
        - containerPort: 8000
          name: http
          protocol: TCP
        env:
        - name: DATABASE_PASSWORD
          valueFrom:
            secretKeyRef:
              name: license-api-secrets
              key: DATABASE_PASSWORD
        - name: REDIS_AUTH_STRING
          valueFrom:
            secretKeyRef:
              name: license-api-secrets
              key: REDIS_AUTH_STRING
        envFrom:
        - configMapRef:
            name: license-api-config
        resources:
          requests:
            cpu: 250m
            memory: 512Mi
          limits:
            cpu: 1000m
            memory: 1Gi
        livenessProbe:
          httpGet:
            path: /health/live
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 10
          timeoutSeconds: 5
          failureThreshold: 3
        readinessProbe:
          httpGet:
            path: /health/ready
            port: 8000
          initialDelaySeconds: 10
          periodSeconds: 5
          timeoutSeconds: 3
          failureThreshold: 3

1.4 Service (`service.yaml`)

apiVersion: v1
kind: Service
metadata:
  name: license-api
  namespace: default
  labels:
    app: license-api
spec:
  type: ClusterIP
  selector:
    app: license-api
  ports:
  - port: 80
    targetPort: 8000
    protocol: TCP
    name: http

1.5 Ingress (`ingress.yaml`)

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: license-api-ingress
  namespace: default
  annotations:
    kubernetes.io/ingress.allow-http: "false"
    kubernetes.io/ingress.global-static-ip-name: coditect-ai-ip  # Shared IP
    networking.gke.io/managed-certificates: license-api-ssl
spec:
  rules:
  - host: licenses.coditect.ai
    http:
      paths:
      - path: /
        pathType: Prefix
        backend:
          service:
            name: license-api
            port:
              number: 80
---
apiVersion: networking.gke.io/v1
kind: ManagedCertificate
metadata:
  name: license-api-ssl
  namespace: default
spec:
  domains:
  - licenses.coditect.ai

Validation:

kubectl apply --dry-run=client -f k8s/

Task 2: Build and Push Docker Image (2 hours)

Check for Dockerfile:

cd submodules/cloud/coditect-cloud-backend
ls -la Dockerfile

If Dockerfile doesn't exist, create it:

# Dockerfile for Django License API
FROM python:3.11-slim

# Set environment variables
ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PIP_NO_CACHE_DIR=1 \
    PIP_DISABLE_PIP_VERSION_CHECK=1

# Install system dependencies
RUN apt-get update && apt-get install -y \
    postgresql-client \
    libpq-dev \
    gcc \
    && rm -rf /var/lib/apt/lists/*

# Create app directory
WORKDIR /app

# Copy requirements and install
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application code
COPY . .

# Run database migrations on startup
RUN python manage.py collectstatic --noinput

# Expose port
EXPOSE 8000

# Run gunicorn
CMD ["gunicorn", "license_platform.wsgi:application", "--bind", "0.0.0.0:8000", "--workers", "4", "--timeout", "60", "--access-logfile", "-", "--error-logfile", "-"]

Build and Push:

# Authenticate Docker to GCR
gcloud auth configure-docker gcr.io

# Build image
docker build -t gcr.io/coditect-citus-prod/license-api:latest .

# Test locally first
docker run -p 8000:8000 \
  -e DATABASE_HOST=localhost \
  -e DATABASE_PASSWORD=test \
  gcr.io/coditect-citus-prod/license-api:latest

# Push to Google Container Registry
docker push gcr.io/coditect-citus-prod/license-api:latest

Task 3: Deploy to GKE (4 hours)

Deployment Commands:

# Create namespace (if needed)
kubectl create namespace default  # Usually already exists

# Apply manifests in order
kubectl apply -f k8s/configmap.yaml
kubectl apply -f k8s/secret.yaml  # Or use Secret Manager
kubectl apply -f k8s/deployment.yaml
kubectl apply -f k8s/service.yaml
kubectl apply -f k8s/ingress.yaml

# Monitor deployment
kubectl rollout status deployment/license-api
kubectl get pods -l app=license-api
kubectl get svc license-api
kubectl get ingress license-api-ingress

Troubleshooting:

# Check pod logs
kubectl logs -l app=license-api --tail=100 -f

# Describe pod for events
kubectl describe pod -l app=license-api

# Check service endpoints
kubectl get endpoints license-api

# Test service internally
kubectl run -it --rm debug --image=curlimages/curl --restart=Never -- \
  curl http://license-api/health/live

Common Issues:

ImagePullBackOff: Check GCR permissions, image exists
CrashLoopBackOff: Check logs, database connectivity, migrations
Pending Pods: Check resource availability, node selectors
Ingress not working: Wait 5-10 minutes for provisioning, check DNS

Task 4: Configure DNS and SSL (1 hour)

DNS Configuration:

# Get ingress IP (should be 34.8.51.57 - shared IP)
kubectl get ingress license-api-ingress

# Add DNS A record
# licenses.coditect.ai → 34.8.51.57

Using Cloudflare (if applicable):

Log into Cloudflare dashboard
Select coditect.ai domain
Add A record: licenses → 34.8.51.57
Set to "DNS only" (orange cloud off) for Google-managed SSL
Wait for propagation (1-5 minutes)

SSL Certificate:

ManagedCertificate will auto-provision (5-15 minutes)
Check status: kubectl describe managedcertificate license-api-ssl
Wait for Status: Active

Task 5: End-to-End Testing (4 hours)

Health Check:

curl https://licenses.coditect.ai/health/live
curl https://licenses.coditect.ai/health/ready

License Acquisition Flow:

# 1. Register user (if not using Identity Platform yet)
curl -X POST https://licenses.coditect.ai/api/v1/auth/register \
  -H "Content-Type: application/json" \
  -d '{"email":"test@example.com","password":"SecurePass123!"}'

# 2. Login and get JWT
curl -X POST https://licenses.coditect.ai/api/v1/auth/login \
  -H "Content-Type: application/json" \
  -d '{"email":"test@example.com","password":"SecurePass123!"}'

# 3. Acquire license
JWT_TOKEN="<from login>"
curl -X POST https://licenses.coditect.ai/api/v1/licenses/acquire \
  -H "Authorization: Bearer $JWT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"license_key":"test-key-123","hardware_id":"test-hw-456"}'

# 4. Send heartbeat
curl -X POST https://licenses.coditect.ai/api/v1/licenses/heartbeat \
  -H "Authorization: Bearer $JWT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"session_id":"<from acquire>"}'

# 5. Release license
curl -X POST https://licenses.coditect.ai/api/v1/licenses/release \
  -H "Authorization: Bearer $JWT_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"session_id":"<from acquire>"}'

Database Verification:

# Connect to Cloud SQL via proxy
cloud_sql_proxy -instances=coditect-citus-prod:us-central1:coditect-citus-dev=tcp:5432 &

# Check data
psql -h localhost -U app_user -d coditect

SELECT * FROM licenses;
SELECT * FROM sessions;
SELECT * FROM tenants;

Redis Verification:

# Connect to Redis
redis-cli -h <REDIS_PRIVATE_IP> -p 6378 -a <AUTH_STRING>

# Check session keys
KEYS session:*
GET session:<session_id>

# Check seat counts
GET tenant:<tenant_id>:active_seats

📋 Phase 2: Security Hardening (Days 3-4)

Reference: See /docs/project-management/tasklist-with-checkboxes.md for complete 120+ task list.

High-Level Security Phases

Day 3 Morning: Layer 1 - Cloud Armor WAF (8 hours)

Objective: Deploy Cloud Armor security policy with comprehensive protection.

Key Tasks:

Create OpenTofu module for Cloud Armor (opentofu/modules/cloud-armor/)
Configure geo-blocking (US/EU allowlist: US, CA, GB, DE, FR, NL, SE, DK, NO, FI)
Set up rate limiting (100 req/min per IP, 10-minute ban)
Implement OWASP Top 10 rules (SQLi, XSS, malicious IPs, scanners)
Enable adaptive protection (ML-based DDoS detection)
Deploy with tofu apply
Attach to backend service
Test blocking rules

Deliverable: Cloud Armor policy deployed and active.

Day 3 Afternoon: Layers 2-3 - Backend Security + Unified Ingress (5 hours)

Layer 2: Backend Security Policy

Connection draining (300s)
Session affinity (CLIENT_IP)
Health check optimization
Cloud Logging integration

Layer 3: Unified Ingress

Update ingress for host-based routing
- licenses.coditect.ai → Django License API
- api.coditect.ai → FastAPI Cloud IDE (existing)
Shared IP configuration (34.8.51.57)
SSL certificate per hostname
Cloud Armor policy attachment

Deliverable: Unified ingress with security policies.

Day 4: Layers 4-5 - Kubernetes Security + Application Hardening (5 hours)

Layer 4: Kubernetes NetworkPolicy

# Default deny all ingress
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: default-deny-ingress
spec:
  podSelector: {}
  policyTypes:
  - Ingress

# Allow license-api ingress from ingress controller only
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: license-api-allow-ingress
spec:
  podSelector:
    matchLabels:
      app: license-api
  policyTypes:
  - Ingress
  ingress:
  - from:
    - namespaceSelector:
        matchLabels:
          name: ingress-nginx
    ports:
    - protocol: TCP
      port: 8000

Layer 5: Django Application Security

JWT authentication middleware
CSRF protection (Django built-in)
Multi-tenant row-level security (django-multitenant)
XSS protection (template auto-escaping)
SQL injection prevention (ORM, no raw SQL)

Deliverable: Pod isolation + application-level security.

Layers 6-7: Data Security + Observability (Included Throughout)

Layer 6: Data Layer Security

Cloud KMS license signing (already tested ✅)
CMEK encryption at rest (already configured ✅)
TLS 1.3 for all transit (GKE default ✅)
Database encryption (Cloud SQL default ✅)

Layer 7: Observability & Incident Response

Cloud Logging (already enabled ✅)
Prometheus metrics export (add in deployment)
Alerting rules (create in Phase 2)
Incident response runbooks (document)

📋 Phase 3: Verification & Production Readiness (Day 5)

Integration Testing

End-to-End Flow:

# Automated test script
./tests/integration/test_license_flow.sh

Load Testing:

# Use Apache Bench or k6
ab -n 10000 -c 100 https://licenses.coditect.ai/api/v1/licenses/acquire

# Or k6
k6 run tests/load/license_acquisition.js

Expected Results:

99.9% success rate
p95 latency <500ms
No rate limit errors at 80 req/min
Rate limit blocks at >100 req/min

Security Audit

Automated Scans:

# OWASP ZAP
docker run -t owasp/zap2docker-stable zap-baseline.py \
  -t https://licenses.coditect.ai

# Nikto
nikto -h https://licenses.coditect.ai

# SSL Labs
curl -X POST "https://api.ssllabs.com/api/v3/analyze?host=licenses.coditect.ai"

Manual Checks:

Test SQL injection attempts (should be blocked by Cloud Armor + ORM)
Test XSS attempts (should be blocked by Cloud Armor + Django escaping)
Test rate limiting bypass attempts
Test geo-blocking (VPN to non-allowed country)
Test DDoS simulation (gradually increase to 1000+ req/s)

Documentation Updates

Files to Update:

README.md - Add deployment instructions
docs/guides/DEPLOYMENT-GUIDE.md - Complete deployment walkthrough
docs/reference/API-REFERENCE.md - Document all endpoints
docs/security/SECURITY-POSTURE.md - Document achieved security score
docs/workflows/INCIDENT-RESPONSE.md - Runbooks for common incidents

📊 Success Metrics

Application Metrics (Phase 1)

Metric	Target	Measurement
Deployment Success	100% pods healthy	`kubectl get pods`
Health Check	200 OK	`curl /health/live`
License Acquisition	<1s p95 latency	Load test results
Database Connectivity	100% success	Integration tests
Redis Connectivity	100% success	Integration tests
SSL Certificate	Active, valid	`curl -v https://licenses.coditect.ai`

Security Metrics (Phase 2)

Metric	Current	Target	Achievement
Security Score	35/100	95/100	+171%
Cloud Armor WAF	❌ Missing	✅ Deployed	DDoS protection
Rate Limiting	❌ Missing	✅ 100 req/min	Abuse prevention
Geo-Blocking	❌ Open	✅ US/EU only	Attack surface reduction
Authentication	❌ Optional	✅ JWT required	Access control
Network Isolation	❌ Open	✅ NetworkPolicy	Lateral movement prevention
Encryption	⚠️ Partial	✅ Full stack	Data protection
Audit Logging	⚠️ Basic	✅ Comprehensive	Forensics capability

Operational Metrics (Phase 3)

Metric	Target	Measurement
Uptime	99.9%	GCP monitoring
Error Rate	<0.1%	Application logs
Mean Time to Detect (MTTD)	<5 min	Alerting system
Mean Time to Respond (MTTR)	<15 min	Incident logs
Load Capacity	100+ concurrent users	Load test

🚧 Known Risks and Mitigation

Phase 1 Risks

Risk	Impact	Probability	Mitigation
Docker image build fails	High (blocks deployment)	Low	Test build locally first, fix dependencies
Database migrations fail	High (blocks functionality)	Medium	Review migrations, test on staging DB first
Ingress provisioning slow	Medium (delays testing)	Medium	Provision ManagedCertificate early, wait 15 min
DNS propagation delay	Low (minor delay)	High	Use DNS checker, wait 5-10 min

Phase 2 Risks

Risk	Impact	Probability	Mitigation
Cloud Armor blocks legitimate traffic	High (breaks functionality)	Medium	Test rules carefully, monitor logs, adjust thresholds
NetworkPolicy too restrictive	High (breaks pod communication)	Medium	Test incrementally, start with logging mode
SSL certificate renewal fails	Medium (HTTPS outage)	Low	Use GCP-managed certs, set up alerting
Rate limiting too aggressive	Medium (legitimate users blocked)	Medium	Start conservative (100 req/min), adjust based on metrics

🎯 Decision Points

Critical Decisions Required

Decision 1: Django vs FastAPI Backend?

CLAUDE.md references suggest Django backend exists
Verify which framework is actually implemented
Action: Check submodules/cloud/coditect-cloud-backend/ for framework

Decision 2: Identity Platform Now or Later?

Full security plan includes Identity Platform OAuth2
Can defer to Phase 3 if using JWT-only initially
Recommendation: Defer to Phase 3, use Django JWT for Phase 1-2

Decision 3: Production Deployment Timeline?

This analysis assumes dev environment deployment
Production requires additional approvals, testing
Recommendation: Complete dev deployment + security, then plan production

📝 Next Actions

Immediate (Next 1 Hour)

✅ Document created - This critical path analysis
⏸️ Check backend repository - Verify Dockerfile, framework, current state
⏸️ Review Kubernetes manifests - Check if any exist in backend repo
⏸️ Get approval - Confirm Option A approach with stakeholder

Short Term (Next 24 Hours)

⏸️ Create Kubernetes manifests - Deployment, Service, ConfigMap, Ingress
⏸️ Build Docker image - Test locally, push to GCR
⏸️ Deploy to GKE - Apply manifests, monitor rollout
⏸️ Configure DNS - Add A record for licenses.coditect.ai
⏸️ Verify functionality - End-to-end license flow test

Medium Term (Days 2-5)

⏸️ Security hardening - Implement all 7 layers (120+ tasks)
⏸️ Integration testing - Automated test suite
⏸️ Load testing - Validate performance under load
⏸️ Security audit - OWASP ZAP, Nikto, manual pentesting
⏸️ Documentation - Update all guides, runbooks, API docs

📚 References

tasklist-with-checkboxes.md - 120+ security hardening tasks
license-platform-security-hardening.md - Complete security guide (38KB)
security-index.md - Comprehensive security index with 2024-2025 standards
security-manifest.md - Security documentation inventory
critical-path-analysis.md - Original critical path (pre-infrastructure)
gap-analysis.md - Current vs target state analysis
implementation-status.md - Detailed implementation tracking

External Resources

OpenTofu Docs: https://opentofu.org/docs/
GKE Docs: https://cloud.google.com/kubernetes-engine/docs
Cloud Armor Docs: https://cloud.google.com/armor/docs
Django Security Checklist: https://docs.djangoproject.com/en/5.0/howto/deployment/checklist/

🏁 Conclusion

Recommendation: Proceed with Option A (Application First, Security Second)

Timeline Summary:

Days 1-2: Deploy backend application to GKE, verify functionality
Days 3-4: Implement 7-layer security hardening (120+ tasks)
Day 5: Integration testing, security audit, documentation

Key Advantages:

Faster time to working MVP (2 days vs 4 days)
Easier debugging (separate concerns)
Lower risk (incremental validation)
Better security configuration (informed by real traffic patterns)

Next Step: Check backend repository structure and begin Kubernetes manifest creation.

Document Control:

Version: 1.0
Created: 2025-11-24
Author: Claude Code + Hal Casteel
Status: Final Recommendation
Review Date: 2025-11-25 (after Phase 1 completion)

Executive Summary​

📊 Infrastructure Status Review​

✅ Completed Infrastructure (Deployed 2025-11-24)​

🎯 Critical Path Analysis​

Current Blocker Identification​

🔍 Critical Path Options Analysis​

Option A: Application First, Security Second (RECOMMENDED)​

Option B: Security First, Application Second (NOT RECOMMENDED)​

✅ Recommended Path: Option A​

Justification​

📋 Phase 1: Backend Deployment (Days 1-2)​

Prerequisites Check​

Task 1: Create Kubernetes Manifests (4 hours)​

1.1 ConfigMap (configmap.yaml)​

1.2 Secret (reference from Secret Manager)​

1.3 Deployment (deployment.yaml)​

1.4 Service (service.yaml)​

1.5 Ingress (ingress.yaml)​

Task 2: Build and Push Docker Image (2 hours)​

Task 3: Deploy to GKE (4 hours)​

Task 4: Configure DNS and SSL (1 hour)​

Task 5: End-to-End Testing (4 hours)​

📋 Phase 2: Security Hardening (Days 3-4)​

High-Level Security Phases​

Day 3 Morning: Layer 1 - Cloud Armor WAF (8 hours)​

Day 3 Afternoon: Layers 2-3 - Backend Security + Unified Ingress (5 hours)​

Day 4: Layers 4-5 - Kubernetes Security + Application Hardening (5 hours)​

Layers 6-7: Data Security + Observability (Included Throughout)​

📋 Phase 3: Verification & Production Readiness (Day 5)​

Integration Testing​

Security Audit​

Documentation Updates​

📊 Success Metrics​

Application Metrics (Phase 1)​

Security Metrics (Phase 2)​

Operational Metrics (Phase 3)​

🚧 Known Risks and Mitigation​

Phase 1 Risks​

Phase 2 Risks​

🎯 Decision Points​

Critical Decisions Required​

📝 Next Actions​

Immediate (Next 1 Hour)​

Short Term (Next 24 Hours)​

Medium Term (Days 2-5)​

📚 References​

Related Documents​

External Resources​

🏁 Conclusion​