	US 12,326,923 B2
	Artificial intelligence impersonation detector
Manu Kurian, Dallas, TX (US); Ana Maxim, Arlington, VA (US); Vinesh Young Patel, London (GB); and Michael Young, Davidson, NC (US)
Assigned to Bank of America Corporation, Charlotte, NC (US)
Filed by Bank of America Corporation, Charlotte, NC (US)
Filed on Jul. 20, 2023, as Appl. No. 18/224,187.
Prior Publication US 2025/0028798 A1, Jan. 23, 2025
Int. Cl. G06F 21/31 (2013.01)

CPC G06F 21/316 (2013.01)

20 Claims

1. A method for detecting artificial intelligence (AI) impersonation, the method comprising:

in a pre-detection stage, training a first AI model, said training comprising:

receiving:

a first dataset, the first dataset comprising a communication, the communication between two or more human users;

a second dataset, the second dataset comprising an impersonation of the communication by a second AI model, the second AI model receiving data from a private database; and

a third dataset, the third dataset comprising an impersonation of the communication by a third AI model, the third AI model receiving data from a public database;

comparing the first, second and third datasets;

based on the comparing, identifying:

a first set of identifiers in the first dataset;

a second set of identifiers in the second dataset; and

a third set of identifiers in the third dataset;

creating a parameter range reference, said creating comprising:

using the first set of identifiers to determine a first parameter range;

using the second set of identifiers to determine a second parameter range; and

using the third set of identifiers to determine a third parameter range;

in a detection stage, monitoring a production communication, said monitoring using the first AI model, said monitoring comprising:

comparing the production communication to the first parameter range, said comparing outputting a first comparison value;

comparing the production communication to the second parameter range, said comparing outputting a second comparison value;

comparing the production communication to the third parameter range, said comparing outputting a third comparison value;

identifying a smallest comparison value from the first, second, and third comparison values;

identifying a parameter range from the first, second, and third parameter ranges corresponding the identified smallest comparison value;

identifying a dataset from the first, second and third datasets corresponding to the identified parameter range; and

based on the identified dataset, detecting that the production communication is a human communication, a private AI communication or a public AI communication.