Providing flexible service access using identity provider

Patent metadata

Field	Value
Publication number	US-11689537-B2
Application number	US-202017076760-A
Country	US
Kind code	B2
Filing date	Oct 21, 2020
Priority date	Oct 21, 2020
Publication date	Jun 27, 2023
Grant date	Jun 27, 2023

Abstract

A service provider provides flexible access to services using an identity provider. The service provider is associated with a custom access policy used by the identity provider to authenticate access requests associated with client devices for services of the client system. The custom access policy describes a set of access levels corresponding to variable levels of access to services of the service provider. The identity provider authenticates access requests by client devices using one or more device signals from the client devices. In some embodiments, the identity provider determines a device trust score for the client device using the one or more device signals. The identity provider provides an authentication response to the client system based on the custom access policy. The client system uses the authentication response to determine an access level for the client device from the set of access levels described by the custom access policy.

First claim

What is claimed is:

1. A computer-implemented method executed by a service provider device, the computer-implemented method comprising:
receiving, from a client device, an access request for one or more services of a service provider;
requesting, by the service provider device, authentication of the access request by an identity provider using a custom access policy, the custom access policy describing a plurality of access levels including at least a limited access level and a full access level;
receiving, from the identity provider, an authentication response for the access request, the authentication response including a device trust score for the client device determined by a machine learning device trust score model that receives using one or more device signals of the client device as input, the machine learning device trust score model trained using at least one of: supervised neural networks, support vector machines, linear regression, logistic regression, decision trees, or unsupervised neural networks;
determining an access level, of the plurality of access levels, for the client device using the authentication response; and
determining whether to provide the client device access to the one or more services in accordance with the determined access level.

2. The computer-implemented method of claim 1 , wherein the one or more device signals are a subset of device signals of the client device available to the identity provider, and the custom access policy specifies the one or more device signals.

3. The computer-implemented method of claim 1 , wherein determining the access level of the plurality of access levels comprises:
comparing the device trust score to a plurality of access level score thresholds corresponding to respective access levels of the plurality of access levels; and
determining the access level for the client device based on the device trust score being below an access level score threshold corresponding to the access level.

4. The computer-implemented method of claim 1 , wherein the custom access policy includes one or more custom weights corresponding to the one or more device signals and for use by the identity provider to determine the device trust score.

5. The computer-implemented method of claim 1 , wherein the authentication response includes context information for the client device, and wherein determining the access level of the plurality of access levels comprises:
determining the access level for the client device based on the context information.

6. The computer-implemented method of claim 1 , wherein the custom access policy is provided to the identity provider by an administrator of the service provider device via an interface associated with the identity provider.

7. The computer-implemented method of claim 1 , wherein the determined access level is the limited access level, and further comprising:
providing the client device access to a subset of services of the service provider device corresponding to the limited access level.

8. The computer-implemented method of claim 1 , wherein the determined access level is the full access level, and further comprising:
providing the client device access to all services of the service provider device.

9. The computer-implemented method of claim 1 , wherein the authentication response is represented using a Security Assertion Markup Language (SAML).

10. The computer-implemented method of claim 1 , wherein the one or more device signals include one or more of a location of the client device, an Internet Protocol (IP) address of the client device, a version of anti-malware software installed on the client device, an operating system version of the client device, an identity provider management status of the client device, an authentication credential type of the client device, a hardware attestation type of the client device, or a multi-factor enrollment (MFA) status of the client device.

11. A non-transitory computer-readable storage medium comprising executable instructions that, when executed by a computer processor of a service provider device, perform actions comprising:
receiving, from a client device, an access request for one or more services of the service provider;
requesting, by the service provider device, authentication of the access request by an identity provider using a custom access policy, the custom access policy describing a plurality of access levels including at least a limited access level and a full access level;
receiving, from the identity provider, an authentication response for the access request, the authentication response including a device trust score for the client device determined by a machine learning device trust score model that receives one or more device signals of the client device as input, the machine learning device trust score model trained using at least one of: supervised neural networks, support vector machines, linear regression, logistic regression, decision trees, or unsupervised neural networks;
determining an access level, of the plurality of access levels, for the client device using the authentication response; and
determining whether to provide the client device access to the one or more services in accordance with the determined access level.

12. The non-transitory computer-readable storage medium of claim 11 , wherein the one or more device signals are a subset of device signals of the client device available to the identity provider, and the custom access policy specifies the one or more device signals.

13. The non-transitory computer-readable storage medium of claim 11 , wherein determining the access level of the plurality of access levels comprises:
comparing the device trust score to a plurality of access level score thresholds corresponding to respective access levels of the plurality of access levels; and
determining the access level for the client device based on the device trust score being below an access level score threshold corresponding to the access level.

14. The non-transitory computer-readable storage medium of claim 11 , wherein the custom access policy includes one or more custom weights corresponding to the one or more device signals and for use by the identity provider to determine the device trust score.

15. The non-transitory computer-readable storage medium of claim 11 , wherein the authentication response includes context information for the client device, and wherein determining the access level of the plurality of access levels comprises:
determining the access level for the client device based on the context information.

16. The non-transitory computer-readable storage medium of claim 11 , wherein the custom access policy is provided to the identity provider by an administrator of the service provider device via an interface associated with the identity provider.

Assignees

• Okta Inc

Inventors

• Shiralkar Dipti Vivek
• Suresh Arun Thotta
• Rahimi Mohammad
• Garg Ankit

Classifications

• G06N20/00

  Machine learning · CPC title

• H04L63/105 Primary

  Multiple levels of security · CPC title

• H04L63/0876

  based on the identity of the terminal or configuration, e.g. MAC address, hardware or software configuration or device fingerprint · CPC title

• H04L63/20

  for managing network security; network security policies in general (filtering policies H04L63/0227) · CPC title

• H04L2463/082

  applying multi-factor authentication · CPC title