SentinelIQ is a cybersecurity platform, so the system itself must follow security-aware design principles.

This document summarizes the current security posture and planned hardening work.

SentinelIQ uses environment variables for runtime configuration.

Sensitive values should be provided through:

• Local .env files
• Docker Compose environment variables
• GitHub Actions secrets
• Cloud provider secret managers

The project must never commit:

.env
API keys
JWT secrets
Production database credentials
Webhook signing secrets

The committed .env.example file contains placeholders only.

FastAPI request bodies use Pydantic schemas.

This provides:

• Type validation
• Required field validation
• Structured request parsing

Important API areas using schemas include:

• Log ingestion
• Alerts
• Incidents
• Analyst notes
• Webhooks

SentinelIQ records audit-relevant activity.

Current audit coverage includes:

• AI alert explanation
• AI incident summarization
• AI report generation
• Webhook delivery attempts

Automation delivery attempts are recorded in:

webhook_deliveries

AI audit records are stored in:

audit_logs

Webhook integration is supported through configured destinations.

Current controls:

• Webhooks can be enabled or disabled
• Delivery attempts are logged
• Failed deliveries preserve error details
• Webhook URLs are stored as configuration

Planned improvements:

• Webhook signing
• Replay protection
• Retry limits
• Delivery backoff
• Destination allowlisting

Production containers include:

• Non-root users
• Docker ignore files
• Production-specific compose file
• Health checks

Planned improvements:

• Image vulnerability scanning
• Minimal runtime images
• Explicit resource limits

The project includes a user model with password hash storage.

Planned improvements:

• JWT authentication enforcement
• Role-based access control
• Admin, Analyst, Viewer roles
• Route-level authorization checks
• Token expiration and refresh strategy

Planned rate limits:

POST /api/ingest/log
POST /api/auth/login
Webhook creation endpoints
AI generation endpoints

Redis can be used as the backing store for distributed rate limiting.

The following items are planned for future hardening:

• Full authentication flow
• RBAC enforcement
• Rate limiting
• Webhook request signing
• Structured security logs
• Prompt injection defenses for future real LLM integration
• Test database for integration tests
• Production deployment secrets management

SentinelIQ currently demonstrates strong security engineering foundations:

• Validation
• Audit logging
• Secrets hygiene
• Container hardening
• Webhook delivery tracking
• Environment-based configuration

Further work should focus on access control, rate limiting, and production monitoring.