Adopting AI and implementing a machine learning (ML) engine can have a huge positive impact on an organization. The ability to draw analytical conclusions from large data sets and make informed decisions is key to increasing mission velocity. But with great velocity comes great responsibility, and cybersecurity of the AI/ML ecosystem must be at the forefront.
One common misconception is that using a cloud-based ML engine absolves the organization of concern about its security. Nothing could be further from the truth. Just like internal infrastructure, all cloud-consumed services must be wrapped in a comprehensive cybersecurity program.
At its core, cybersecurity rests on the triad of confidentiality, integrity, and availability. This triad yields specific considerations when applied to ML environment security:
- Confidentiality. Ensure that the ML environment doesn’t expose the organization’s sensitive data sets to unauthorized access vectors. This includes ensuring sensitive data is handled in a secure manner throughout its ML journey, including controls such as encryption, audit trails, role-based access control (RBAC), and retention management.
- Integrity. Can the ML output be trusted? Validate the integrity of the entire ML ecosystem to ensure that any tampering or unexpected behavior change is detectable.
- Availability. Once ML becomes essential for business operations, ensure the platform meets the defined service-level agreement (SLA) for availability.
Rule4 takes a complete approach to securing AI/ML environments based on decades of experience with full-stack infrastructure and cybersecurity best practices. Contact us for help securing your AI/ML ecosystem from every angle.