Patent Description:
<CIT> discloses booting a machine in a secure fashion in a potentially unsecure environment.

Described herein is a system for protecting an artificial intelligence model, comprising: a client device comprising a processor and a memory having computer-executable instructions stored thereupon which, when executed by the processor, cause the client device to: receive an encrypted artificial intelligence model in a virtual secure mode instance of the client device; decrypt the encrypted artificial intelligence model in the virtual secure mode instance using a decryption secret; store the decrypted artificial intelligence model in the virtual secure mode instance; and execute an application that utilizes the decrypted artificial intelligence model in the virtual secure mode instance to infer an output.

The scope of the invention is defined by the independent claims.

Various technologies pertaining to protecting an artificial intelligence model using a virtual secure mode instance (e.g., of a client device) are now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more aspects. Further, it is to be understood that functionality that is described as being carried out by certain system components may be performed by multiple components. Similarly, for instance, a component may be configured to perform functionality that is described as being carried out by multiple components.

The subject disclosure supports various products and processes that perform, or are configured to perform, various actions regarding protecting an artificial intelligence model on a client device. What follows are one or more exemplary systems and methods.

Aspects of the subject disclosure pertain to the technical problem of protecting an artificial intelligence model on a client device. The technical features associated with addressing this problem involve receiving an encrypted artificial intelligence model in a virtual secure mode instance of the client device; decrypting the encrypted artificial intelligence model in the virtual secure mode instance using a decryption secret; storing the decrypted artificial intelligence model in the virtual secure mode instance; and executing an application that utilizes the decrypted artificial intelligence model in the virtual secure mode instance. Accordingly, aspects of these technical features exhibit technical effects of more efficiently and effectively increasing security of artificial intelligence model(s) and/or improving security of executing (e.g., inference and/or training) of artificial intelligence model(s).

As used herein, the terms "component" and "system," as well as various forms thereof (e.g., components, systems, sub-systems, etc.) are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an instance, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a computer and the computer can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. Further, as used herein, the term "exemplary" is intended to mean serving as an illustration or example of something, and is not intended to indicate a preference.

The design and/or training of an artificial intelligence (AI) model generally involves unique domain knowledge and/or significant engineering resources. Thus, a well-designed and/or well-trained AI model can be considered and protected as a proprietary asset of the developer/publisher. Conventionally, AI model(s) have been executed in a secure environment such as a cloud-based service. While user(s) are able to utilize the AI model(s) to infer an output in accordance with a submitted input, the AI model(s) themselves have not been accessible to user(s) for inspection, copying, etc. Providing and/or executing AI model(s) on a client device (e.g., outside of a secure cloud-based environment) can lead to unauthorized access (e.g., inspection, copying) during training and/or inference since the AI model(s) may be visible to a user of a host operating system of the client device.

Described herein is a system and method for utilizing a virtual secure mode (VSM) instance to protect an AI model from unauthorized access (e.g., inspection, copying) during execution of an application utilizing the AI model (e.g., training and/or inference) on a client device. An encrypted AI model is received by the host operating system and decrypted in the VSM instance, where task(s) such as training and/or inference can be securely performed. Since the VSM instance and the host operating system are treated as two separate virtual machines, the host operating system cannot access the decrypted AI model running on the VSM instance. In some embodiments, the VSM instance can leverage a trusted platform module to store and retrieve a secret protecting the AI model, thus gaining both operating system isolation and in-transit/in-use protection of the AI model.

Referring to <FIG>, a system for protecting an artificial intelligence (AI) model on a client device <NUM> is illustrated. The system <NUM> can utilize a virtual secure mode (VSM) instance to protect the AI model from unauthorized access (e.g., inspection, copying) during execution of an application utilizing the AI model (e.g., training and/or inference) on a client device <NUM>.

In some embodiments, VSM is a feature that leverages virtualization extension(s) of a CPU to sequester process(es) and their memory against viewing/tampering from other, potentially malicious, entity(ies). VSM thus provides added security of data in memory. Further, "client device" refers to a general-purpose computer, processing system, or computing device (e.g., mobile phone, desktop, laptop, tablet, watch, server, hand-held, programmable consumer or industrial electronics, set-top box, game system, compute node, etc.) which, in its simplest form, has one or more processors coupled to memory that execute various computer executable actions, instructions, and or components stored in memory.

The system <NUM> includes a model execution application <NUM> that receives an encrypted AI model in a virtual secure mode (VSM) instance <NUM> on the client device <NUM>. In some embodiments, as discussed in greater detail with respect to <FIG> below, the VSM instance <NUM> is isolated from a host operating system with memory of the VSM instance <NUM> independent and hardware-regulated to ensure that the host operating system cannot access the memory of the VSM instance <NUM>. Thus, the VSM instance <NUM> isolates the AI model <NUM> and/or utilization of the AI model <NUM> from a host operating system and/or other VSM instances, thus protecting the AI model in-transit and in-use.

The AI model comprises digital information that encompasses a particular artificial intelligence model, for example, developed for a particular purpose and/or inference scenario. In some embodiments, the AI model is a serialized format of a computation graph in accordance with the Open Neural Network Exchange (ONNX) standard. ONNX is an open specification that includes: (<NUM>) a definition of an extensible computation graph model; (<NUM>) definitions of standard data types; and (<NUM>) definition of built-in operators. A serialized graph comprises a set of metadata fields, a list of model parameters, and a list of computation nodes. The AI model is encrypted, for example, by a developer and/or a publisher to protect information included in the AI model from being disclosed.

The model execution application <NUM> can decrypt the encrypted AI model using a decryption secret <NUM> and store the AI model <NUM> within the VSM instance <NUM>. In some embodiments, the decryption secret <NUM> is pre-provisioned into the VSM instance <NUM> by the developer/publisher of the AI model <NUM>. For example, the decryption secret <NUM> can be stored during activation/initialization of the host operating system of the client device <NUM>. In some embodiments, the decryption secret <NUM> can be a private key of a public/private key pair. In some embodiments, the decryption secret <NUM> is a shared key (e.g., symmetric key) known by and kept secret by the VSM instance <NUM> and the developer/publisher of the AI model <NUM>.

Once decrypted, the AI model <NUM> can be utilized by the model execution application <NUM> in the VSM instance <NUM>. In some embodiments, the model execution application <NUM> can further utilize the decrypted AI model <NUM> in the VSM instance <NUM> to infer an output based upon a received input. The model execution application <NUM> can provide the inferred output, for example, to an application executing on a host operating system of the client device <NUM>, as discussed below. In some embodiments, the model execution application <NUM> can utilize the decrypted AI model <NUM> to adapt the AI model <NUM> in accordance with a received input and a received expected output (e.g., training of the AI model <NUM>).

In some embodiments, the model execution application <NUM> interprets the AI model <NUM>. In some embodiments, the VSM instance <NUM> includes a code generator (not shown) that translates the AI model <NUM> to executable code for a target programming language which is then utilized by the model execution application <NUM>.

In some embodiments, the application <NUM> adapts the decrypted AI model <NUM> in accordance with a received input and a received output. The model execution application <NUM> can encrypt the adapted AI model using an encryption secret <NUM> and provide the encrypted AI model to the host operating system of the client device <NUM> (e.g., to be provided to a cloud-based service), as discussed below. In some embodiments, the encryption secret <NUM> is public key of a public/private key pair, with the private key known to the developer/publisher of the AI mode. In some embodiments, the encryption secret <NUM> is a shared key (e.g., symmetric key) known by and kept secret by the VSM instance <NUM> and the developer/publisher of the AI model <NUM>. In some embodiments, the encryption secret <NUM> and the decryption secret <NUM> are the same. In some embodiments, the encryption secret <NUM> and the decryption secret <NUM> are different.

Turning to <FIG>, an exemplary client device <NUM> is illustrated. The client device <NUM> includes hardware <NUM> and a hypervisor <NUM>. The client device <NUM> further includes a host operating system <NUM> having a kernel <NUM>, service <NUM>, and/or application(s) <NUM>. The client device <NUM> further includes the VSM instance <NUM>.

The hypervisor <NUM> is in communication with the hardware <NUM> and serves to abstract the host operating system <NUM> and the VSM instance <NUM> from the underlying hardware <NUM> itself, providing control and scheduling functions that allow the hardware <NUM> to be securely shared between the host operating system <NUM> and the VSM instance <NUM>. During booting of the client device <NUM>, the hypervisor <NUM> loads and later calls loaders of the host operating system <NUM> and the VSM instance <NUM>. In this manner, multiple security boundaries are generated. A security boundary refers to a manner of operation of the client device <NUM>, or a portion of the client device <NUM>, where a program executing in one security boundary is prohibited from accessing data or programs in another security boundary. A security boundary, also referred to as a security isolation boundary or an isolation boundary, is a manner of operation of the client device <NUM> or a portion of the client device <NUM>.

The hypervisor <NUM> thus establishes and enforces the security boundaries, prohibiting a program executing in one security boundary from accessing data and programs in another security boundary. In some embodiments, an application running in the VSM instance <NUM> is prohibited from accessing data in and application(s) running in the host operating system <NUM>, and an application <NUM> running in the host operating system <NUM> is prohibited from accessing data in and programs running in the VSM instance <NUM>.

In some embodiments, the client device <NUM> can include an optional secure cryptoprocessor that is a trusted platform module (TPM) <NUM>. The TPM <NUM> can be a physical device or a virtual device. In some embodiments, the TPM <NUM> is provided by the hypervisor <NUM>. In some embodiments, the TPM <NUM> comprises dedicated hardware (e.g., dedicated integrated circuit and/or component part of an integrated circuit) that prevents the host operating system <NUM> from accessing the virtual secure mode instance <NUM> and, more specifically, the AI model <NUM>. In some embodiments, the TPM <NUM> comprises firmware. In some embodiments, the TPM <NUM> comprises a software emulator.

The TPM <NUM> includes various functionality, such as key generation, encryption, decryption, secure storage (e.g., platform configuration registers), and so forth. In one or more embodiments, the TPM <NUM> conforms to the Trusted Computing Group Trusted Platform Module specification version <NUM>, published as the ISO/IEC <NUM> Information technology - Trusted Platform Module specification, Parts <NUM> - <NUM>. Although discussed herein with reference to TPM <NUM>, in some embodiments, the client device <NUM> can alternatively use one or more other cryptoprocessors that provide functionality similar to the TPM.

Once the host operating system <NUM> has been loaded, the kernel <NUM> can provide core functionality including low-level operations such as scheduling threads and/or routing hardware interrupts. The host operating system <NUM> can further include services <NUM>, for example, memory management, power management, creation and coordination of multiple threads of execution.

In some embodiments, the VSM instance <NUM> executes securely and independently of the host operating system <NUM>. An encrypted AI model can be received in the VSM instance <NUM> from the host operating system <NUM> and decrypted only in the VSM instance <NUM>. Once decrypted in the VSM instance <NUM>, the AI model can be utilized by an application (e.g., model execution application <NUM>) to perform inference based upon a received input and/or training of the AI model in accordance with received input(s) and output(s). Since the VSM instance <NUM> and the host operating system <NUM> are treated as two separate virtual machines, the host operating system <NUM> cannot access the AI model running on the VSM instance <NUM>. Encryption of the AI model facilitates in-transit protection of the AI model. Decryption of the AI model and utilization of the AI model in the VSM instance <NUM> facilitates in-use protection of the AI model.

<FIG> illustrate exemplary methodologies relating to protecting an artificial intelligence (AI) model on a client device. While the methodologies are shown and described as being a series of acts that are performed in a sequence, it is to be understood and appreciated that the methodologies are not limited by the order of the sequence. For example, some acts can occur in a different order than what is described herein. In addition, an act can occur concurrently with another act. Further, in some instances, not all acts may be required to implement a methodology described herein.

Moreover, the acts described herein may be computer-executable instructions that can be implemented by one or more processors and/or stored on a computer-readable medium or media. The computer-executable instructions can include a routine, a sub-routine, programs, a thread of execution, and/or the like. Still further, results of acts of the methodologies can be stored in a computer-readable medium, displayed on a display device, and/or the like.

Referring to <FIG>, a method of protecting an artificial intelligence (AI) model on a client device <NUM> is illustrated. In some embodiments, the method <NUM> is performed by the system <NUM>.

At <NUM>, an encrypted artificial model is received in a virtual secure mode instance of a client device. At <NUM>, in the virtual secure mode instance, the encrypted artificial intelligence model is decrypted using a decryption secret.

At <NUM>, the decrypted artificial intelligence model is stored in the virtual secure mode instance. At <NUM>, an application that utilizes the decrypted artificial intelligence model is executed in the virtual secure mode instance.

Turning to <FIG>, a method of protecting an artificial intelligence (AI) model on a client device <NUM> is illustrated. In some embodiments, the method <NUM> is performed by the system <NUM>.

At <NUM>, the decrypted artificial intelligence model is stored in the virtual secure mode instance. At <NUM>, an input is received, for example, from an application executing on a host operating system. At <NUM>, in the virtual secure mode instance, an application is executed that utilizes the decrypted artificial intelligence model to infer an output based upon the received input. At <NUM>, the inferred output is provided, for example, to the application executing on the host operation system.

At <NUM>, the decrypted artificial intelligence model is stored in the virtual secure mode instance. At <NUM>, an input and an expected output to use for adapting the artificial intelligence model are received. At <NUM>, in the virtual secure mode instance, an application is executed that adapts the decrypted artificial intelligence model in accordance with the received input and received expected output.

At <NUM>, in the virtual secure mode instance, the adapted artificial intelligence model is encrypted using an encryption secret. At <NUM>, the encrypted adapted artificial intelligence model is provided. In some embodiments, the encrypted artificial intelligence model is provided to a host operating system, for example, for transmission to a cloud-based service that aggregates a plurality of instances of the artificial intelligence model. The aggregated artificial intelligence model can then be encrypted and redeployed to client device(s). In some embodiments, the encrypted artificial intelligence model can be transmitted directly from the virtual secure mode instance, for example, to the cloud-based service.

Described herein is a system for protecting an artificial intelligence model, comprising: a client device comprising a processor and a memory having computer-executable instructions stored thereupon which, when executed by the processor, cause the client device to: receive an encrypted artificial intelligence model in a virtual secure mode instance of the client device; decrypt the encrypted artificial intelligence model in the virtual secure mode instance using a decryption secret; store the decrypted artificial intelligence model in the virtual secure mode instance; and execute an application that utilizes the decrypted artificial intelligence model in the virtual secure mode instance.

The system can further include the memory having further computer-executable instructions stored thereupon which, when executed by the processor, cause the processing system to: receive an input and an expected output by the application in the virtual secure mode instance, wherein executing the application in the virtual secure mode comprises adapting the decrypted artificial intelligence model in accordance with the received input and the expected output. The system can further include the memory having further computer-executable instructions stored thereupon which, when executed by the processor, cause the processing system to: encrypt the adapted artificial intelligence model using an encryption secret in the virtual secure model instance; and provide the encrypted adapted artificial intelligence model.

The system can further include wherein the encryption secret and the decryption secret are different from each other. The system can further include wherein the encryption secret and the decryption secret are the same. The system can further include the memory having further computer-executable instructions stored thereupon which, when executed by the processor, cause the processing system to: receive an input by the application in the virtual secure mode instance, wherein executing the application in the virtual secure mode comprises inferring an output using the decrypted artificial intelligence model in accordance with the received input; and provide the inferred output. The system can further include wherein a host operating system of the client device cannot access memory of the virtual secure mode instance.

The system can further include wherein the artificial intelligence model is a serialized format of a computation graph in accordance with the Open Neural Network Exchange standard. The system can further include wherein the decryption secret is pre-provisioned into the virtual secure mode instance by a developer of the artificial intelligence model. The system can further include wherein a hypervisor of the client device abstracts a host operating system of client device and the virtual secure mode instance from hardware of the client device.

Described herein is a method protecting an artificial intelligence model on a client device, comprising: receiving an encrypted artificial intelligence model in a virtual secure mode instance of the client device; decrypting the encrypted artificial intelligence model in the virtual secure mode instance using a decryption secret; storing the decrypted artificial intelligence model in the virtual secure mode instance; and executing an application that utilizes the decrypted artificial intelligence model in the virtual secure mode instance.

The method can further include receiving an input and an expected output by the application in the virtual secure mode instance, wherein executing the application in the virtual secure mode comprises adapting the decrypted artificial intelligence model in accordance with the received input and the expected output. The method can further include encrypting the adapted artificial intelligence model using an encryption secret in the virtual secure model instance; and providing the encrypted adapted artificial intelligence model.

The method can further include wherein the encryption secret and the decryption secret are different from each other. The method can further include receiving an input by the application in the virtual secure mode instance, wherein executing the application in the virtual secure mode comprises inferring an output using the decrypted artificial intelligence model in accordance with the received input; and providing the inferred output. The method can further include wherein a host operating system of the client device cannot access memory of the virtual secure mode instance.

Described herein is a computer storage media storing computer-readable instructions that when executed cause a computing device to: receive an encrypted artificial intelligence model in a virtual secure mode instance of the client device; decrypt the encrypted artificial intelligence model in the virtual secure mode instance using a decryption secret; store the decrypted artificial intelligence model in the virtual secure mode instance; and execute an application that utilizes the decrypted artificial intelligence model in the virtual secure mode instance.

The computer storage media can include storing further computer-readable instructions that when executed cause a computing device to: receive an input and an expected output by the application in the virtual secure mode instance, wherein executing the application in the virtual secure mode comprises adapting the decrypted artificial intelligence model in accordance with the received input and the expected output. The computer storage media can include storing further computer-readable instructions that when executed cause a computing device to: encrypt the adapted artificial intelligence model using an encryption secret in the virtual secure model instance; and provide the encrypted adapted artificial intelligence model. The computer storage media can include storing further computer-readable instructions that when executed cause a computing device to: receive an input by the application in the virtual secure mode instance, wherein executing the application in the virtual secure mode comprises inferring an output using the decrypted artificial intelligence model in accordance with the received input; and provide the inferred output.

With reference to <FIG>, illustrated is an example general-purpose computer, processing system, or computing device <NUM> (e.g., mobile phone, desktop, laptop, tablet, watch, server, hand-held, programmable consumer or industrial electronics, set-top box, game system, compute node, etc.). For instance, the computing device <NUM> may be used in a system for protecting an artificial intelligence (AI) model on a client device <NUM>.

The computer <NUM> includes one or more processor(s) <NUM>, memory <NUM>, system bus <NUM>, mass storage device(s) <NUM>, and one or more interface components <NUM>. The system bus <NUM> communicatively couples at least the above system constituents. However, it is to be appreciated that in its simplest form the computer <NUM> can include one or more processors <NUM> coupled to memory <NUM> that execute various computer executable actions, instructions, and or components stored in memory <NUM>. The instructions may be, for instance, instructions for implementing functionality described as being carried out by one or more components discussed above or instructions for implementing one or more of the methods described above.

The processor(s) <NUM> can be implemented with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The processor(s) <NUM> may also be implemented as a combination of computing devices, for example a combination of a DSP and a microprocessor, a plurality of microprocessors, multi-core processors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In one embodiment, the processor(s) <NUM> can be a graphics processor.

The computer <NUM> can include or otherwise interact with a variety of computer-readable media to facilitate control of the computer <NUM> to implement one or more aspects of the claimed subject matter. The computer-readable media can be any available media that can be accessed by the computer <NUM> and includes volatile and nonvolatile media, and removable and non-removable media. Computer-readable media can comprise two distinct and mutually exclusive types, namely computer storage media and communication media.

Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes storage devices such as memory devices (e.g., random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), etc.), magnetic storage devices (e.g., hard disk, floppy disk, cassettes, tape, etc.), optical disks (e.g., compact disk (CD), digital versatile disk (DVD), etc.), and solid state devices (e.g., solid state drive (SSD), flash memory drive (e.g., card, stick, key drive) etc.), or any other like mediums that store, as opposed to transmit or communicate, the desired information accessible by the computer <NUM>. Accordingly, computer storage media excludes modulated data signals as well as that described with respect to communication media.

Communication media embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.

Memory <NUM> and mass storage device(s) <NUM> are examples of computer-readable storage media. Depending on the exact configuration and type of computing device, memory <NUM> may be volatile (e.g., RAM), non-volatile (e.g., ROM, flash memory, etc.) or some combination of the two. By way of example, the basic input/output system (BIOS), including basic routines to transfer information between elements within the computer <NUM>, such as during start-up, can be stored in nonvolatile memory, while volatile memory can act as external cache memory to facilitate processing by the processor(s) <NUM>, among other things.

Mass storage device(s) <NUM> includes removable/non-removable, volatile/non-volatile computer storage media for storage of large amounts of data relative to the memory <NUM>. For example, mass storage device(s) <NUM> includes, but is not limited to, one or more devices such as a magnetic or optical disk drive, floppy disk drive, flash memory, solid-state drive, or memory stick.

Memory <NUM> and mass storage device(s) <NUM> can include, or have stored therein, operating system <NUM>, one or more applications <NUM>, one or more program modules <NUM>, and data <NUM>. The operating system <NUM> acts to control and allocate resources of the computer <NUM>. Applications <NUM> include one or both of system and application software and can exploit management of resources by the operating system <NUM> through program modules <NUM> and data <NUM> stored in memory <NUM> and/or mass storage device (s) <NUM> to perform one or more actions. Accordingly, applications <NUM> can tum a general-purpose computer <NUM> into a specialized machine in accordance with the logic provided thereby.

All or portions of the claimed subject matter can be implemented using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to realize the disclosed functionality. By way of example and not limitation, system <NUM> or portions thereof, can be, or form part, of an application <NUM>, and include one or more modules <NUM> and data <NUM> stored in memory and/or mass storage device(s) <NUM> whose functionality can be realized when executed by one or more processor(s) <NUM>.

In accordance with one particular embodiment, the processor(s) <NUM> can correspond to a system on a chip (SOC) or like architecture including, or in other words integrating, both hardware and software on a single integrated circuit substrate. Here, the processor(s) <NUM> can include one or more processors as well as memory at least similar to processor(s) <NUM> and memory <NUM>, among other things. Conventional processors include a minimal amount of hardware and software and rely extensively on external hardware and software. By contrast, an SOC implementation of processor is more powerful, as it embeds hardware and software therein that enable particular functionality with minimal or no reliance on external hardware and software. For example, the system <NUM> and/or associated functionality can be embedded within hardware in a SOC architecture.

The computer <NUM> also includes one or more interface components <NUM> that are communicatively coupled to the system bus <NUM> and facilitate interaction with the computer <NUM>. By way of example, the interface component <NUM> can be a port (e.g., serial, parallel, PCMCIA, USB, FireWire, etc.) or an interface card (e.g., sound, video, etc.) or the like. In one example implementation, the interface component <NUM> can be embodied as a user input/output interface to enable a user to enter commands and information into the computer <NUM>, for instance by way of one or more gestures or voice input, through one or more input devices (e.g., pointing device such as a mouse, trackball, stylus, touch pad, keyboard, microphone, joystick, game pad, satellite dish, scanner, camera, other computer, etc.). In another example implementation, the interface component <NUM> can be embodied as an output peripheral interface to supply output to displays (e.g., LCD, LED, plasma, etc.), speakers, printers, and/or other computers, among other things. Still further yet, the interface component <NUM> can be embodied as a network interface to enable communication with other computing devices (not shown), such as over a wired or wireless communications link.

Claim 1:
A client device (<NUM>) for protecting an artificial intelligence model (<NUM>), the client device (<NUM>) comprising a processor (<NUM>) and a memory (<NUM>) having computer-executable instructions stored thereupon which, when executed by the processor (<NUM>), cause the client device (<NUM>) to:
receive an encrypted artificial intelligence model (<NUM>) in a virtual secure mode instance (<NUM>) of the client device (<NUM>);
decrypt the encrypted artificial intelligence model (<NUM>) in the virtual secure mode instance (<NUM>) using a decryption secret (<NUM>);
store the decrypted artificial intelligence model (<NUM>) in the virtual secure mode instance (<NUM>); and
execute a first application (<NUM>) that utilizes the decrypted artificial intelligence model (<NUM>) in the virtual secure mode instance (<NUM>) to infer an output; and
provide the output to a second application (<NUM>) executing on a host operating system (<NUM>), wherein the second application (<NUM>) is prohibited from accessing data and programs running in the virtual secure mode instance (<NUM>).