What are key defenses for securing AI inference endpoints?

• A. Only model retraining on a regular basis.
• B. Disabling all logging.
• C. Using unencrypted HTTP to speed traffic.
• D. Authentication, authorization, rate limiting, input validation, encryption in transit, secure API gateways, and anomaly detection.

Answer: (D)

Defending AI inference endpoints requires a layered approach that protects who can access the service, how requests are handled, and how data moves. The strongest set of defenses includes authentication and authorization to verify who can call the endpoint and what they’re allowed to do, plus rate limiting to prevent abuse and denial-of-service. Input validation is essential to ensure only well-formed, safe data reaches the model, reducing the risk of exploits via crafted requests. Encryption in transit protects the data as it travels between client and service, and a secure API gateway helps enforce policies, manage access, and provide centralized logging and threat protection. Anomaly detection adds a runtime guard by flagging unusual patterns that could indicate probing or manipulation, enabling quick responses to emerging threats.

Relying on retraining alone doesn’t address runtime security or access controls, disabling logging removes critical visibility for detecting incidents, and using unencrypted HTTP exposes data to interception and tampering. The comprehensive combination in place best secures AI inference endpoints.