Enterprise AI systems require security controls that protect models, data, applications, and users from misuse. Security should be considered throughout the AI lifecycle, from planning and development to deployment and ongoing monitoring.
Protect Sensitive Data
Identify confidential information, limit its use, and apply appropriate access controls. Avoid sending sensitive data to unapproved tools or services, and keep records of how information is processed.
Reduce Common Risks
Limit access, protect credentials, validate inputs, monitor unusual activity, and keep software dependencies updated. Test systems for unauthorized access, data leakage, and unreliable outputs.
Secure Models and Applications
Use controlled deployment processes, review integrations, and separate testing environments from production systems. Regular updates and security assessments help reduce vulnerabilities.
Prepare for Incidents
Create response procedures for data exposure, inaccurate outputs, unauthorized access, and service failures. Regular testing helps teams react quickly and restore safe operations.
Maintain Human Oversight
Important actions should include approval, logging, and a clear way to investigate unexpected behavior. Security is strongest when technical controls are supported by trained employees.