ArenaMind AI is a browser-based experience for simulated stadium operations. The repository is intentionally designed to be safe and explainable for demo use, while also demonstrating the controls that would be expected in an enterprise-grade deployment.

• Keep the browser client stateless and non-authoritative for critical operations.
• Treat all user prompts as untrusted input.
• Prefer safe defaults over permissive behavior.
• Fail closed when validation or remote service conditions are compromised.
• Avoid exposing implementation details, secrets, or provider errors to end users.

The most relevant risks for this application are:

• Prompt injection or role-confusion attempts
• Oversized, malformed, or empty inputs
• Remote AI service failures or timeout abuse
• Secret leakage through logs or client-side storage
• Dependency drift or vulnerable third-party packages

ArenaMind validates prompts before routing them to local or remote AI services. This includes checks for empty input, excessive length, malformed control characters, and suspicious instruction-override patterns. The remote Gemini prompt is wrapped in a system context that explicitly instructs the model to treat the user request as untrusted stadium data rather than privileged instructions.

• The Gemini API key is expected to be supplied through environment variables.
• Local development secrets should remain in .env.local and never be committed.
• The client does not persist provider credentials, tokens, or sensitive prompts in browser storage.
• Production deployments should use provider-managed secrets and rotate them as part of standard operational hygiene.

• Remote AI requests are time-bounded through AbortController.
• Retry behavior is limited to avoid amplifying load or delaying the user experience.
• Failures return structured fallback responses rather than blank output or raw provider errors.
• The application never exposes stack traces, hidden prompts, or internal implementation details to the user.

• The application does not use unsafe HTML rendering patterns.
• User-generated text is rendered through React text nodes to preserve standard escaping behavior.
• Display text is sanitized and length-limited before presentation.

Before release, the project should be reviewed with:

npm audit --audit-level=high
npm run build
npm test

This ensures that known vulnerabilities, build regressions, and runtime issues are addressed before release.

For a production-grade version of ArenaMind, the following controls should be added:

• Authentication and authorization for staff and organizer workflows
• Audit logging for critical actions and AI-assisted decisions
• Rate limiting and abuse protection for public AI endpoints
• Secure content delivery and environment segmentation
• Monitoring and incident response integration