Secure architecture for enterprise AI systems (SafeAIs) encompasses several key aspects:

1. Data Security: Ensure that all data collected, processed, and stored by the AI system is encrypted, both in transit and at rest.

2. Access Control and Authentication: Implement strict access control mechanisms to ensure only authorized personnel can interact with AI systems, including multi-factor authentication and role-based access control (RBAC).

3. Privacy by Design: Integrate privacy considerations at every stage of AI development, from data collection to model deployment, to minimize the amount of personal data used and ensure compliance with laws like GDPR or CCPA.

4. Threat Modeling and Mitigation: Identify potential security vulnerabilities in the AI system through threat modeling exercises and implementing countermeasures to prevent, detect, and respond to cyber attacks.

5. Model Security and Integrity: Protect AIs from adversarial attacks, which are deliberate attempts to deceive the model by providing misleading input.

6. Auditing and Monitoring: Maintain and regularly monitor detailed logs of system activities to identify suspicious behavior.

7. Compliance: Ensure the AI solution adheres to industry-specific standards and regulatory requirements, such as HIPAA for healthcare data, or ISO 27001 for information security management.

8. Resilience and Disaster Recovery: Build resilience into the AI infrastructure to withstand failures and attacks, with backup and recovery plans to minimize downtime and data loss.

9. Vendor and Supply Chain Security: Assess the security practices of third-party providers involved in the AI solution, which can introduce additional risks if not properly secured.

10. Continuous Improvement and Security Awareness: Regularly update security measures based on new threats and technologies, and educate employees on security best practices to maintain a strong security culture.