IP Library › Granted Patent US 12,537,839
Granted Patent B1
US 12,537,839 · App. 18/200,286 · Granted Jan 27, 2026

Identification by a data platform of secrets misuse by cloud workloads

Inventors: Theodore M. Reed (Berkeley Heights, NJ); Nolan K. Karpinski (San Francisco, CA); Xiaofei Guo (Sunnyvale, CA); Naveen Kumar Bibinagar (Austin, TX); Jacob A. Kilby (San Francisco, CA); Yijou Chen (Cupertino, CA)
Assignee: Fortinet, Inc.
H04L63/1425G06F9/455G06F9/545G06F16/9024G06F16/9038G06F16/9535G06F16/9537G06F21/57H04L43/045H04L43/06H04L63/10H04L67/306H04L67/535G06F16/2456
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Quick Facts
Patent No.
US 12,537,839
App. No.
18/200,286
Granted
Jan 27, 2026
Kind
B1
Abstract

An illustrative method for performing an identification of secrets misuse may include accessing runtime workload data associated with one or more keys configured to be accessed by a workload deployed in a compute environment, detecting, based on the runtime workload data, an anomaly associated with the one or more keys, and determining, based on the anomaly, that secrets misuse has possibly occurred within the compute environment.

Claims (37)

1 . A method comprising:

accessing, by a data platform, runtime workload data associated with one or more keys configured to be accessed by a workload deployed in a compute environment;

detecting, by the data platform and based on the runtime workload data, an anomaly associated with the one or more keys; and

determining, by the data platform and based on the anomaly, that secrets misuse has possibly occurred within the compute environment, wherein detecting of the anomaly further comprises:

determining that the one or more keys accessed new data not previously accessed by the one or more keys.

2 . The method of claim 1 , wherein the one or more keys are configured to provide a credential for the workload to access data outside of the workload.

3 . The method of claim 1 , wherein the detecting the anomaly comprises:

generating a fingerprint representative of key usage associated with the one or more keys; and

determining that the key usage deviates from the fingerprint more than a threshold amount.

4 . The method of claim 3 , wherein the fingerprint is generated using a machine learning algorithm.

5 . The method of claim 1 , wherein the detecting the anomaly further comprises detecting a new key not previously included in the one or more keys configured to be accessed by the workload.

6 . The method of claim 1 , further comprising performing, based on the secrets misuse possibly occurring, a remediation operation associated with the one or more keys.

7 . The method of claim 6 , wherein the performing the remediation operation includes providing a notification associated with the one or more keys that indicates that the secrets misuse has possibly occurred.

8 . The method of claim 6 , wherein the performing the remediation operation includes disabling at least one of the one or more keys.

9 . The method of claim 6 , wherein the performing the remediation operation includes providing a recommendation to disable at least one of the one or more keys.

10 . The method of claim 6 , wherein the performing the remediation operation includes removing at least one of the one or more keys from the compute environment.

11 . The method of claim 6 , wherein the performing the remediation operation includes providing a recommendation to remove at least one of the one or more keys from the compute environment.

12 . The method of claim 1 , wherein the runtime workload data is collected using an agent configuration deployed within the compute environment.

13 . The method of claim 1 , wherein the runtime workload data include one or more of a configuration of the one or more keys, a type of the one or more keys, a customer identification of the one or more keys, an identity of a user accessing the one or more keys, a service associated with the one or more keys, an action performed by the one or more keys, a response from the one or more keys, a size of the one or more keys, a time associated with the one or more keys, one or more events associated with the one or more keys, a process execution history associated with the one or more keys, security-related data associated with the one or more keys, or an access code associated with the one or more keys.

14 . The method of claim 1 , further comprising:

constructing, by the data platform and based on the runtime workload data, a graph comprising a plurality of nodes connected by a plurality of edges, wherein each node of the plurality of nodes represents a logical entity associated with the runtime workload data and each edge of the plurality of edges represents a behavioral relationship between nodes connected by the edge;

wherein the determining that the secrets misuse has possibly occurred is further based on the graph.

15 . A system comprising:

a memory storing instructions; and

one or more processors communicatively coupled to the memory and configured to execute the instructions to perform a process comprising:

accessing runtime workload data associated with one or more keys configured to be accessed by a workload deployed in a compute environment;

detecting, based on the runtime workload data, an anomaly associated with the one or more keys; and

determining, based on the anomaly, that secrets misuse has possibly occurred within the compute environment wherein detecting of the anomaly further comprises:

determining that the one or more keys accessed new data not previously accessed by the one or more keys.

16 . The system of claim 15 , wherein the detecting the anomaly comprises: generating a fingerprint representative of key usage associated with the one or more keys; and determining that the key usage deviates from the fingerprint more than a threshold amount.

17 . The system of claim 16 , wherein the fingerprint is generated using a machine learning algorithm.

18 . The system of claim 15 , wherein the process further comprises performing, based on the secrets misuse possibly occurring, a remediation operation associated with the one or more keys.

19 . A computer program product embodied in a non-transitory computer readable storage medium and comprising computer instructions for:

accessing runtime workload data associated with one or more keys configured to be accessed by a workload deployed in a compute environment;

detecting, based on the runtime workload data, an anomaly associated with the one or more keys; and

determining, based on the anomaly, that secrets misuse has possibly occurred within the compute environment wherein detecting of the anomaly further comprises:

determining that the one or more keys accessed new data not previously accessed by the one or more keys.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 7, 2024
From: LACEWORK, INC.
To: FORTINET, INC.
Reel/Frame 069301/0327 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 22, 2023
From: REED, THEODORE M.; KARPINSKI, NOLAN K.; GUO, XIAOFEI; BIBINAGAR, NAVEEN KUMAR; KILBY, JACOB A.; CHEN, YIJOU
To: LACEWORK, INC.
Reel/Frame 063718/0584 →
Continuity (7)
Continuation In Part 18119045 · Mar 8, 2023
Continuation 17510179 · Oct 25, 2021
Continuation 16786822 · Feb 10, 2020
Continuation 16134806 · Sep 18, 2018
Provisional Application 63400523 · Aug 24, 2022
Provisional Application 62650971 · Mar 30, 2018
Provisional Application 62590986 · Nov 27, 2017
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