Provenance Audit Trails for Microservices via Service Mesh
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Solution Overview
Problem
In microservices architectures, it is challenging to provide a secure and accurate provenance audit trail due to the lack of capabilities in conventional systems to manage metadata travel with compute communications, ensuring IP confidentiality, secure revocation, and on-boarding/off-boarding based on provenance reputation scores, especially with the increasing elasticity and heterogeneity of deployment models.
Innovation Solution
Implementing provenance audit trails that generate metadata for transactions performed by microservices using telemetry data from heterogeneous XPU blocks, homomorphically encrypting and tracking this metadata via blockchain, and enforcing policies within a trusted execution environment to ensure secure and confidential computing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If microservices are deployed across heterogeneous hardware devices to increase elasticity and resource utilization, then productivity and resource efficiency are improved, but device complexity and difficulty of detecting and measuring provenance increase
Solution Approach 1:
A service mesh is introduced as an intermediary layer between microservices and heterogeneous hardware devices. The service mesh includes a data plane for intercepting communications and a control plane for managing policies, creating a standardized interface that abstracts away hardware heterogeneity. This allows microservices to run on diverse hardware (CPUs, GPUs, FPGAs, ASICs) without direct complexity management, as the service mesh handles the variations and provides unified provenance tracking capabilities.
2Productivity
If microservices are deployed across heterogeneous hardware devices to increase elasticity, then productivity is improved, but the ability to provide secure audit trail for confirming provenance deteriorates
Solution Approach 1:
The service mesh implements continuous feedback mechanisms for provenance tracking. The data plane intercepts communications between microservices and hardware devices, collecting metadata about service executions, data flows, and resource usage. This information is fed back to the control plane, which maintains provenance records and enables audit trails. The feedback loop ensures that even as microservices dynamically migrate across heterogeneous hardware, their provenance is continuously tracked and verified.
3Device complexity
If conventional systems are used without metadata travel capabilities, then device complexity is reduced, but the ability to ensure IP confidentiality and secure revocation deteriorates
Solution Approach 1:
The service mesh implements preliminary actions for security by establishing encryption and authentication mechanisms before data flows between microservices and hardware devices. Policies are pre-configured in the control plane, including encryption keys, access control rules, and revocation mechanisms. When a microservice communicates with hardware, the service mesh data plane automatically applies these pre-established security measures, ensuring IP confidentiality is maintained from the outset without requiring complex real-time decision-making.
Data Source
AI summary
An apparatus to facilitate provenance audit trails for microservices architectures is disclosed. The apparatus includes one or more processors to obtain provenance metadata for a microservice from a local blockchain of provenance metadata maintained for the hardware resource executing a task performed by the microservice, the provenance metadata comprising identification of the microservice, operating state of at least one of a hardware resource or a software resource used to execute the microservice and the task, and an operating state of a sidecar of the microservice during the task; access one or more policies established for the microservice; analyze the provenance metadata with respect to the one or more policies to identify if there is a violation of the one or more policies; and generate one or more evaluation metrics based on whether the violation of the one or more policies is identified.