Monitoring digital assets

1 . A computer-implemented method for performing real-time context-aware digital assessment management to identify and prevent misuse of a digital asset, the method comprising:

training a digital asset artificial intelligence (AI) model to automatically tag digital assets with descriptive tags corresponding to relevant contextual information associated with the digital assets;

detecting usage of at least one digital asset of the digital assets;

analyzing, using the digital asset AI model, the usage by analyzing the descriptive tags corresponding to relevant contextual information associated with the at least one of the digital assets detected as being used to identify anomalies associated with the at least one digital asset;

responsive to identifying an anomaly associated with the at least one digital asset, determining, using the digital asset AI model, whether the anomaly exceeds a predetermined sensitivity threshold; and

responsive to determining that the anomaly associated with the at least one digital asset exceeds the predetermined sensitivity threshold, generating, using the digital asset AI model, a real-time remediation action based on the anomaly.

2 . The computer-implemented method of claim 1 , wherein the anomaly is digital content manipulation of the digital asset.

3 . The computer-implemented method of claim 1 , wherein the real-time remediation action is selected from a group of remediation actions consisting of relocating the digital asset, altering digital content of the digital asset, temporarily removing the digital asset, and permanently removing the digital asset.

4 . The computer-implemented method of claim 1 , wherein generating the real-time remediation action is based on the anomaly and a degree of sensitivity associated with the anomaly.

5 . The computer-implemented method of claim 1 , wherein the digital asset AI model is an attention general adversarial network (attention GAN).

6 . The computer-implemented method of claim 1 , wherein the digital asset AI model is self-refining based on learned auto-tagging and an in-context learning approach coupled with user interactions and predictive intents.

7 . The computer-implemented method of claim 1 , further comprising associating an additional descriptive tag with the at least one digital asset being used based on the usage.

8 . The computer-implemented method of claim 1 , wherein the digital asset AI model generates a sensitivity value associated with the anomaly, wherein the sensitivity value is compared to the predetermined sensitivity threshold to determine whether the anomaly associated with the at least one digital asset exceeds the predetermined sensitivity threshold.

9 . A system comprising:

a memory comprising computer readable instructions; and

a processing device for executing the computer readable instructions, the computer readable instructions controlling the processing device to perform operations for performing real-time context-aware digital assessment management to identify and prevent misuse of a digital asset, the operations comprising:

training a digital asset artificial intelligence (AI) model to automatically tag digital assets with descriptive tags corresponding to relevant contextual information associated with the digital assets;

detecting usage of at least one digital asset of the digital assets;

analyzing, using the digital asset AI model, the usage by analyzing the descriptive tags corresponding to relevant contextual information associated with the at least one of the digital assets detected as being used to identify anomalies associated with the at least one digital asset;

responsive to identifying an anomaly associated with the at least one digital asset, determining, using the digital asset AI model, whether the anomaly exceeds a predetermined sensitivity threshold; and

responsive to determining that the anomaly associated with the at least one digital asset exceeds the predetermined sensitivity threshold, generating, using the digital asset AI model, a real-time remediation action based on the anomaly.

10 . The system of claim 9 , wherein the anomaly is digital content manipulation of the digital asset.

11 . The system of claim 9 , wherein the real-time remediation action is selected from a group of remediation actions consisting of relocating the digital asset, altering digital content of the digital asset, temporarily removing the digital asset, and permanently removing the digital asset.

12 . The system of claim 9 , wherein generating the real-time remediation action is based on the anomaly and a degree of sensitivity associated with the anomaly.

13 . The system of claim 9 , wherein the digital asset AI model is an attention general adversarial network (attention GAN).

14 . The system of claim 9 , wherein the digital asset AI model is self-refining based on learned auto-tagging and an in-context learning approach coupled with user interactions and predictive intents.

15 . The system of claim 9 , the operations further comprising associating an additional descriptive tag with the at least one digital asset being used based on the usage.

16 . The system of claim 9 , wherein the digital asset AI model generates a sensitivity value associated with the anomaly, wherein the sensitivity value is compared to the predetermined sensitivity threshold to determine whether the anomaly associated with the at least one digital asset exceeds the predetermined sensitivity threshold.

17 . A computer program product for performing real-time context-aware digital assessment management to identify and prevent misuse of a digital asset, the computer program product comprising:

a set of one or more computer-readable storage media;

program instructions, collectively stored in the set of one or more storage media, for causing a processor set to perform the following computer operations:

training a digital asset artificial intelligence (AI) model to automatically tag digital assets with descriptive tags corresponding to relevant contextual information associated with the digital assets;

detecting usage of at least one digital asset of the digital assets;

analyzing, using the digital asset AI model, the usage by analyzing the descriptive tags corresponding to relevant contextual information associated with the at least one of the digital assets detected as being used to identify anomalies associated with the at least one digital asset;

responsive to identifying an anomaly associated with the at least one digital asset, determining, using the digital asset AI model, whether the anomaly exceeds a predetermined sensitivity threshold; and

responsive to determining that the anomaly associated with the at least one digital asset exceeds the predetermined sensitivity threshold, generating, using the digital asset AI model, a real-time remediation action based on the anomaly.

18 . The computer program product of claim 17 , wherein the anomaly is digital content manipulation of the digital asset.

19 . The computer program product of claim 17 , wherein the real-time remediation action is selected from a group of remediation actions consisting of relocating the digital asset, altering digital content of the digital asset, temporarily removing the digital asset, and permanently removing the digital asset.

20 . The computer program product of claim 17 , wherein generating the real-time remediation action is based on the anomaly and a degree of sensitivity associated with the anomaly.