Adaptive Data Confidence Security Policies for Edge Data Streams
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Solution Overview
Problem
Current security policies in edge environments are static and do not adapt to changing data confidence levels, leading to compromised data spreading throughout the network without awareness of issues at downstream nodes.
Innovation Solution
Implementing a DCF-aware dynamic node security policy that monitors data streams, fetches confidence information, determines data confidence levels, and applies adaptive security policies in real-time based on these levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a static security policy is applied at every node in the network, then security coverage is provided at all nodes, but the system cannot adapt to changing data confidence levels and compromised data spreads throughout the network
Solution Approach 1:
The patent implements dynamic security policies that automatically adjust based on real-time data confidence levels. When confidence levels change due to node failures or attacks, the system dynamically modifies security measures (such as encryption strength, validation requirements, or data routing) without manual intervention, allowing the network to adapt to changing conditions while maintaining integrity
Solution Approach 2:
The system incorporates feedback mechanisms where nodes report their confidence levels and security status back to the network controller. This feedback loop enables the controller to detect compromised nodes, assess the impact on data integrity, and automatically adjust security policies across the network accordingly, ensuring that reliability is maintained despite dynamic conditions
2Reliability
If confidence information is collected and processed for each data stream, then data confidence levels can be accurately determined and security policies adapted, but system complexity increases due to additional monitoring and processing requirements
Solution Approach 1:
The patent segments the security policy management function into separate modular components: a confidence information collector at each node, a central policy decision point that processes confidence data, and local policy enforcement agents that apply decisions. This segmentation distributes complexity across the network while maintaining centralized control, allowing reliable confidence assessment without overwhelming any single component
Solution Approach 2:
The system introduces a confidence information broker or mediator that collects, validates, and aggregates confidence data from multiple nodes before passing it to the policy decision engine. This intermediary layer simplifies the complexity by pre-processing and filtering confidence information, reducing the burden on downstream components while ensuring accurate data trustworthiness assessment
Data Source
AI summary
One example method may be performed in the context of a data confidence fabric (DCF) and may include monitoring a data stream handled by a node, obtaining confidence information concerning the node, determining a data confidence score for the node, mapping the data confidence score to a security policy, and applying the security policy to the data stream. The particular security policy applied to a data stream can be varied in real time based on conditions at the node.