At NeuroLab AI (AI STEM Lab Assistant), we recognize that safeguarding the privacy and security of students, educators, and institutions is paramount. As an advanced AI-powered interactive STEM laboratory platform utilizing real-time computer vision and digital twin technologies, we are uncompromising in our commitment to security, data protection, and responsible educational AI development.

This document outlines our security posture, vulnerability reporting procedures, and the ethical frameworks that govern our platform.

We strongly recommend always running the latest stable release of the NeuroLab AI platform. Security updates are prioritized for the current major version.

We deeply value the work of the security research community in helping us maintain a safe educational environment. If you believe you have discovered a security vulnerability in the NeuroLab AI platform, we ask that you disclose it to us responsibly.

Please email your findings to security@neurolab.ai. Do not open public GitHub issues for security vulnerabilities.

• A detailed description of the vulnerability and its potential impact.
• Steps to reproduce the issue (including any necessary configuration or payloads).
• Information about the affected component (e.g., Computer Vision pipeline, AI Tutor API, Frontend dashboard).
• Your contact information for follow-up.

1. Acknowledgement: We will acknowledge receipt of your report within 24 hours.
2. Investigation: Our security engineers will triage and validate the vulnerability.
3. Remediation: We will develop and deploy a patch based on our Security Response Timeline.
4. Resolution: We will notify you when the issue has been resolved and patched.

We triage all reports based on their severity (CVSS score) and potential impact on student data privacy.

The following components of the NeuroLab AI ecosystem are in scope for responsible security testing:

• Web Application: The React/Vite educational frontend and dashboard.
• Backend APIs: FastAPI endpoints and WebSocket services.
• AI Inference Services: The AI Tutor (Professor Nova) and language model integrations.
• Computer Vision Systems: Real-time object tracking and digital twin synchronization modules.
• Authentication: Supabase-powered authentication and user session management.
• Database & Cloud: Supabase PostgreSQL and secure cloud storage configurations.
• Reporting Services: Automated PDF Lab Report generation pipelines.

To encourage responsible security research, we require that researchers adhere to the following guidelines:

• Conduct security research without impacting other users, students, or educators.
• Test against accounts and resources you explicitly own or have permission to access.
• Report findings privately to allow us time to patch the vulnerability.

• Privacy Violations: Accessing, downloading, or modifying data belonging to other students or educators.
• Service Disruption: Performing Denial of Service (DoS/DDoS) attacks or aggressive automated scanning.
• Data Destruction: Deleting or altering educational records, experiment logs, or database schemas.
• Social Engineering: Phishing or social engineering against NeuroLab AI staff, students, or educators.
• Physical Security: Attempting physical access to our data centers or offices.

As an AI-driven educational platform, securing our models and computer vision systems is critical:

• Prompt Injection Resilience: The AI Tutor (Professor Nova) implements strict system prompts and input sanitization to prevent jailbreaking and ensure responses remain educationally appropriate.
• Computer Vision Integrity: Our tracking algorithms validate kinematics against physical laws (e.g., gravity) to detect anomalies or adversarial inputs in the webcam feed.
• Hallucination Mitigation: The AI Tutor is grounded in verified physics engines and digital twin data, minimizing the risk of generating factually incorrect scientific explanations.
• Data Poisoning Prevention: Educational telemetry and visual data used for platform improvements are heavily sanitized and anonymized.

We treat educational data with the highest level of care:

• Encryption in Transit: All communications (HTTP and WebSockets) are secured using TLS 1.3.
• Encryption at Rest: Databases, user credentials, and saved lab reports are encrypted at rest using AES-256.
• Secure Credential Management: Integration with enterprise-grade authentication providers ensuring secure token lifecycles and hashed passwords.
• Access Control: Strict Role-Based Access Control (RBAC) separates student, educator, and administrative privileges.
• Audit Logging: Comprehensive logging of authentication events and critical system state changes.

• Cloud Security: Hosted on secure, compliant cloud infrastructure with isolated VPCs.
• Network Segmentation: Complete isolation between public-facing web servers, backend AI workers, and the primary database.
• Monitoring Systems: 24/7 automated monitoring of API latency, error rates, and unauthorized access attempts.
• Disaster Recovery: Automated daily backups of all critical databases and configuration states, stored securely across multiple regions.

NeuroLab AI is deeply committed to ethical technology use in education:

• FERPA & COPPA Alignment: Our architecture is designed with student privacy regulations in mind.
• Data Minimization: We collect only the telemetry and visual data strictly necessary for the live lab simulation.
• Privacy-by-Design: Computer vision processing heavily favors client-side execution to prevent sensitive webcam footage from being transmitted unnecessarily to the cloud.
• Ethical AI: We ensure our AI models are unbiased, constructive, and strictly focused on STEM education.

For institutions deploying or integrating with NeuroLab AI, we recommend:

• Enforce strong password policies or utilize SSO/SAML integrations.
• Enable Multi-Factor Authentication (MFA) for educator and administrator accounts.
• Regularly review active sessions and rotate API keys.
• Ensure client devices (student laptops) maintain updated browsers and secure network connections.

In the event of a security incident, our team follows a rigorous protocol:

1. Detection: Identifying the threat via automated alerts or bug bounty reports.
2. Investigation: Determining the scope, impact, and root cause of the breach.
3. Containment: Isolating affected systems (e.g., revoking tokens, blocking IPs, disabling specific API routes).
4. Eradication: Removing the vulnerability and applying hardened patches.
5. Recovery: Restoring services to full operational capacity safely.
6. Post-Incident Review: Conducting a blameless post-mortem to improve future defenses and notifying affected users transparently.

We continually align our architecture and security practices with industry-leading frameworks:

• SOC 2 Type II conceptual alignment (in progress).
• ISO 27001 principles for information security management.
• NIST Cybersecurity Framework.
• OWASP Top 10 secure coding practices for modern web applications.
• FERPA & COPPA guidelines for protecting student and children's data privacy.

NeuroLab AI supports safe harbor for security researchers. If you conduct vulnerability research and disclosure in good faith and in accordance with this policy, we consider your actions authorized. We will not initiate or support legal action against you regarding activities explicitly covered by this policy.

For all security-related inquiries, please contact our security team:

✉️ security@neurolab.ai

Our team operates in UTC and guarantees an initial response to critical reports within 24 hours.

At NeuroLab AI, we believe that innovation in STEM education must be built on a foundation of absolute trust. We are dedicated to maintaining security excellence, absolute transparency, and continuous improvement. We thank the global cybersecurity community for partnering with us to build a safer future for students everywhere.