Which of the following strategies best aligns with the defense-in-depth security approach for generative AI applications on AWS?

Which of the following strategies best aligns with the defense-in-depth security approach for generative AI applications on AWS?
A . Relying solely on data encryption to protect the AI training data
B . Applying multiple layers of security measures including input validation, access controls, and continuous monitoring to address vulnerabilities
C . Using a single authentication mechanism for all users and services accessing the AI models
D . Implementing a single-layer firewall to block unauthorized access to the AI models

View Answer

Answer: B

Explanation:

Correct option:

Applying multiple layers of security measures including input validation, access controls, and continuous monitoring to address vulnerabilities

Architecting a defense-in-depth security approach involves implementing multiple layers of security to protect generative AI applications. This includes input validation to prevent malicious data inputs, strict access controls to limit who can interact with the AI models, and continuous monitoring to detect and respond to security incidents. These measures can help address common vulnerabilities and meet the best practices for securing generative AI applications on AWS.

Incorrect options:

Implementing a single-layer firewall to block unauthorized access to the AI models – While a firewall is an important security measure, relying on a single layer of defense is insufficient for comprehensive security. Defense-in-depth requires multiple, overlapping layers of protection.

Relying solely on data encryption to protect the AI training data – Data encryption is crucial for protecting data at rest and in transit, but it does not address other vulnerabilities such as input validation or unauthorized access. A holistic security strategy is needed.

Using a single authentication mechanism for all users and services accessing the AI models – Employing a single authentication mechanism is a weak security practice. Multiple authentication and authorization mechanisms should be used to ensure robust access control.

Reference: https://aws.amazon.com/blogs/machine-learning/architect-defense-in-depth-security-for-generative-ai-ap

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