IP Library Granted Patent US 12,425,443
Granted Patent B2
US 12,425,443 · App. 18/744,554 · Granted Sep 23, 2025

Cloud resource prioritization for data security posture management based on detection of cloud data peak signals

Inventors: Ravishankar Ganesh Ithal (Los Altos, CA); Yang Zhang (Fremont, CA); Mummoorthy Murugesan (Gilroy, CA); Gautam Kanaparthi (Los Altos, CA)
Assignee: Normalyze, Inc.
H04L63/1433G06F21/577H04L63/205
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Quick Facts
Patent No.
US 12,425,443
App. No.
18/744,554
Granted
Sep 23, 2025
Kind
B2
Abstract

The technology disclosed relates to analysis of data posture of a cloud environment. In particular, disclosed technology relates to a system and method for analyzing cloud assets, such as storage resources, compute resources, etc. to detect peak signals based on occurrences of sensitive data types or other data classifications in cloud assets. A computing system is configured to access data in plurality of cloud resources and, on a cloud resource-by-cloud resource basis, attribute a plurality of data sensitivity parameters to the data in a given cloud resource of the plurality of cloud resources, and generate a peak value indicating an appraisal of the data in given cloud resource based on the plurality of data sensitivity parameters attributed to the data. A graphical interface includes graphical objects configured to visually represent plurality of cloud resources, plurality of data sensitivity parameters, and the peak values generated for the plurality of cloud resources.

Claims (73)

1. A computing system comprising:

at least one processor; and

memory storing instructions executable by the at least one processor, wherein the instructions, when executed, cause the computing system to:

access data in a plurality of cloud resources;

on a cloud resource-by-cloud resource basis,

attribute a plurality of data sensitivity parameters to the data in a given cloud resource of the plurality of cloud resources,

determine a plurality of peak values for the given cloud resource, each respective peak value, of the plurality of peak values, indicating an appraisal of the data in the given cloud resource based on a respective data sensitivity parameter of the plurality of data sensitivity parameters, and

aggregate the plurality of peak values to generate an aggregate peak value for the given cloud resource; and

display a graphical interface including graphical objects that visually represent the plurality of cloud resources and the aggregate peak values generated for the plurality of cloud resources, wherein the graphical interface is configured to amplify and/or attenuate a display priority of the plurality of cloud resources in dependence upon the aggregate peak values.

2. The computing system of claim 1 , wherein the graphical interface is configured to display the graphical objects in accordance with a display priority, and wherein the graphical interface is configured to at least one of:

amplify the display priority of a first cloud resource, of the plurality of cloud resources, having a higher peak value over a second cloud resource, of the plurality of cloud resources, having a lower peak value, or

attenuate the display priority of the first cloud resource having a lower peak value over a third cloud resource of the plurality of cloud resources, having a higher peak value.

3. The computing system of claim 1 , wherein the graphical interface is configured to visually order the graphical objects in at least one of:

a descending order in dependence upon decreasing peak values, or

an ascending order in dependence upon increasing peak values.

4. The computing system of claim 1 , wherein the peak value is determined by a cost function that takes into account one or more data sensitivity parameters in the plurality of data sensitivity parameters.

5. The computing system of claim 4 , wherein the cost function is weighted by one or more data sensitivity parameters in the plurality of data sensitivity parameters.

6. The computing system of claim 1 , wherein the instructions, when executed, cause the computing system to:

determine the plurality of peak values for the given cloud resource by summing the plurality of peak values to generate the aggregate peak value for the given cloud resource.

7. The computing system of claim 1 , wherein the instructions, when executed, cause the computing system to determine the plurality of peak values for the given cloud resource by matching sensitivity profiles to data objects in the given cloud resource.

8. The computing system of claim 4 , wherein the cost function determines the peak value by taking into account one or more costs associated with the plurality of data sensitivity parameters.

9. The computing system of claim 8 , wherein the one or more costs include at least one of:

a data breach cost,

a data acquisition cost,

a data management cost, or

a data processing cost.

10. The computing system of claim 1 , wherein the plurality of data sensitivity parameters includes at least one of:

a cloud resource type of the given cloud resource,

a volume measure of the data in the given cloud resource,

an impact measure of impact of potential breach of the data in the given cloud resource,

a likelihood measure of likelihood of potential breach of the data in the given cloud resource,

data sensitivity entity categories attributed to the data in the given cloud resource,

data sensitivity profile categories attributed to the data in the given cloud resource, or

data governance categories attributed to the data in the given cloud resource.

11. The computing system of claim 10 , wherein the plurality of data sensitivity parameters includes at least one of:

counts of the data sensitivity entity categories,

counts of the data sensitivity profile categories, or

counts of the data governance categories.

12. The computing system of claim 1 , wherein the plurality of cloud resources comprise one or more of:

storage resources, or

compute resources.

13. The computing system of claim 1 , wherein the instructions cause the computing system to update one or more of the plurality of peak values in response to user specification.

14. The computing system of claim 13 , wherein the graphical interface is configured to further amplify and/or attenuate a display priority of the plurality of cloud resources in dependence upon the updated one or more of the plurality of peak values.

15. A computer-implemented method comprising:

accessing data in a plurality of cloud resources;

on a cloud resource-by-cloud resource basis,

attributing one or more data sensitivity parameters to a given cloud resource, of the plurality of cloud resources, based on occurrences of classified data objects in the given cloud resource, and

generating a peak value indicating an appraisal of the given cloud resource based on the one or more data sensitivity parameters; and

displaying a graphical interface including graphical objects configured to visually represent the plurality of cloud resources and peak values generated for the plurality of cloud resources, wherein the graphical interface amplifies and/or attenuates a display priority of the plurality of cloud resources in dependence upon the peak values.

16. The computer-implemented method of claim 15 , wherein displaying the graphical interface comprises selecting display locations for the graphical objects on the graphical interface in at least one of:

a descending order in dependence upon decreasing peak values, or

an ascending order in dependent upon increasing peak values.

17. The computer-implemented method of claim 15 , wherein

the given cloud resource comprises a plurality of data objects,

the one or more data sensitivity parameters comprises a target data classification,

attributing the one or more data sensitivity parameters to the given cloud resource comprises determining, from the plurality of data objects, a number of instances of data objects conforming to the target data classification, and

generating the peak value comprises generating the peak value based on the number of instances of data objects conforming to the target data classification.

18. The computer-implemented method of claim 17 , wherein

the one or more data sensitivity parameters comprises a plurality of different target data classifications,

attributing the plurality of different target data classifications to the given cloud resource comprises determining, for each respective target data classification of the plurality of different target data classifications, a respective number of instances of data objects in the given cloud resource conforming to the respective target data classification, and

generating the peak value comprises aggregating the respective number of instances for each respective target data classification of the plurality of different target data classifications.

19. A computing system comprising:

peak signal logic configured to:

access data in a plurality of cloud resources;

on a cloud resource-by-cloud resource basis,

attribute a plurality of data sensitivity parameters to the data in a given cloud resource of the plurality of cloud resources, and

generate a peak value indicating an appraisal of the data in the given cloud resource based on the plurality of data sensitivity parameters attributed to the data; and

interface logic configured to:

generate a plurality of graphical objects, each graphical object, of the plurality of graphical objects, representing a respective cloud resource of the plurality of cloud resources, and

display a graphical interface including the plurality of graphical objects, and peak values generated for the plurality of cloud resources, wherein the graphical interface is configured to amplify and/or attenuate a display priority of the plurality of cloud resources by arranging the graphical objects at display locations on the graphical interface based on the peak values.

20. The computing system of claim 19 , wherein the interface logic is configured to select the display locations on the graphical interface based on at least one of:

a descending order in dependence upon decreasing peak values, or

an ascending order in dependent upon increasing peak values.

Assignments (4)
SECOND LIEN INTELLECTUAL PROPERTY SECURITY AGREEMENT Recorded Dec 8, 2025
From: PROOFPOINT, INC.
To: U.S. BANK TRUST COMPANY, NATIONAL ASSOCIATION, AS COLLATERAL AGENT
Reel/Frame 073889/0677 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 7, 2025
From: NORMALYZE, INC.
To: PROOFPOINT, INC.
Reel/Frame 071618/0634 →
SECURITY INTEREST Recorded Feb 19, 2025
From: NORMALYZE, INC.
To: GOLDMAN SACHS BANK USA, AS COLLATERAL AGENT
Reel/Frame 070254/0844 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 1, 2024
From: ITHAL, RAVISHANKAR GANESH; ZHANG, YANG; MURUGESAN, MUMMOORTHY; KANAPARTHI, GAUTAM
To: NORMALYZE, INC.
Reel/Frame 068741/0991 →
Continuity (3)
Continuation 18243056 · Sep 6, 2023
Provisional Application 63523312 · Jun 26, 2023
Related Publication 20240430290A1 · Dec 26, 2024
References Cited (2)
US 20140222496A1 · Flores · 2014 [cited by examiner]
US 20160162693A1 · Breuer · 2016 [cited by examiner]