Agent-Based Reboot Selection for Criticality-Tiered Updates
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Solution Overview
Problem
Managed networks face challenges in efficiently managing product updates due to difficulties in coordinating reboots across endpoints, leading to security vulnerabilities and operational interruptions.
Innovation Solution
Implementing an agent-based reboot policy that identifies the criticality of update operations and determines appropriate reboot behaviors, including advised, mandatory, or critical states, to minimize reboots and reduce vulnerability persistence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional patch management is used without agent-based criticality assessment, then update distribution can be performed, but reboot coordination becomes inefficient and security vulnerabilities persist longer
Solution Approach 1:
The system changes the parameter of reboot timing by assessing criticality levels of update operations and transitioning endpoint states based on these assessments. Critical updates trigger immediate reboots while non-critical updates allow deferred reboots, optimizing both security response time and operational continuity.
Solution Approach 2:
The agent performs preliminary assessment of update criticality before executing reboots. By evaluating the importance of each update operation in advance, the system can prioritize security-critical updates and schedule them for immediate execution, while deferring less critical updates to minimize disruption.
2Reliability
If frequent reboots are performed to ensure timely updates, then security is improved, but operational interruptions increase
Solution Approach 1:
The system applies different reboot behaviors to different update operations based on their local criticality characteristics. Each update is assessed individually, and the agent transitions the endpoint to appropriate states (advised, mandatory, or critical reboot states) based on the specific update's importance, rather than applying a uniform reboot policy to all updates.
Solution Approach 2:
The reboot policy becomes dynamic rather than static. The agent continuously assesses update criticality and adjusts reboot timing and behavior accordingly. This dynamic approach allows the system to respond to security threats when necessary while maintaining operational productivity during low-risk periods.
3Ease of operation
If manual reboot coordination is used in BYOD and extensive networks, then some control is maintained, but management efficiency decreases and products remain un-patched
Solution Approach 1:
The endpoint agent performs self-service by autonomously assessing update criticality, determining appropriate reboot behavior, and executing state transitions without requiring manual administrator intervention. This enables automated patch management across diverse environments including BYOD devices, significantly improving deployment efficiency while maintaining security standards.
Solution Approach 2:
The system implements feedback loops where the agent continuously monitors update status, assesses criticality, and adjusts reboot timing based on the current state of the endpoint and the importance of pending updates. This feedback mechanism ensures that security-critical updates are deployed promptly while less critical updates are managed efficiently to minimize disruption.
Data Source
AI summary
A method of automated software management may include: identifying, at an agent located on the managed endpoint, a reboot policy in which the reboot policy may indicate one or more reboot behaviors initiated after update operations are performed at the managed endpoint; identifying, at the agent, a first update operation associated with a first level of criticality based on metadata associated with an instruction implemented to locally perform the first update operation on the managed endpoint; comparing, at the agent, the first level of criticality of the first update operation with the reboot policy to determine a first reboot behavior; and transitioning, at the agent, an endpoint state to a first reboot state to actuate the first reboot behavior in which the first reboot state comprises one or more of an advised reboot state, a mandatory reboot state, or a critical reboot state.