Leveraging generative artificial intelligence (AI) to prevent data exfiltration
AssigneeFortinet, Inc.
InventorsTheron Tock, Vimalkumar Jeyakumar, Ying Xie, Harish Kumar Bharat Singh, Weifei Zeng, Vikram Kapoor, Trevor A. Welsh, Richard Rich, Yijou Chen
Life of this patent
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Abstract
Leveraging generative artificial intelligence (AI) to prevent data exfiltration, including: monitoring network activity of a device; and determining, based on at least one generative artificial intelligence (AI) model, whether the network activity indicates a data exfiltration.
Claims20
A method of leveraging generative artificial intelligence (AI) to prevent data exfiltration, the method comprising:
- monitoring network activity of a device, wherein the monitoring is performed by an intermediary positioned between the device and one or more resources, the intermediary configured to inspect network traffic;
- and
- determining, by providing a structured natural-language prompt including data describing the network activity to a large language model configured to generate a textual inference responsive to the prompt, whether the network activity indicates a data exfiltration, wherein determining whether the network activity indicates the date exfiltration comprises dynamically classifying, based on the textual inference generated by the large language model, a recipient of the data according to an inferred functional capability of the recipient independent of network address or domain identity, wherein the classification is performed without reliance on pre-defined feature vectors or signature-based clustering of the network activity.
A non-transitory computer readable medium having stored thereon a computer program product for leveraging generative artificial intelligence (AI) to prevent data exfiltration, the computer program product including computer program configurable to carry out a computer implemented method of:
- monitoring network activity of a device, wherein the monitoring is performed by an intermediary positioned between the device and one or more resources, the intermediary configured to inspect network traffic;
- and
- determining, by providing a structured natural-language prompt including data describing the network activity to a large language model configured to generate a textual inference responsive to the prompt, whether the network activity indicates a data exfiltration, wherein determining whether the network activity indicates the date exfiltration comprises dynamically classifying, based on the textual inference generated by the large language model, a recipient of the data according to an inferred functional capability of the recipient independent of network address or domain identity, wherein the classification is performed without reliance on pre-defined feature vectors or signature-based clustering of the network activity.
Dependencies are inferred from the claim text.