Adaptive Network Security Enforcement with Session Embeddings
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Solution Overview
Problem
Existing network security policies are inflexible and often misconfigured, leading to inadequate or overly restrictive access control, with 99% of firewall breaches attributed to misconfiguration, and failing to adapt to changing network conditions and user/device-specific needs.
Innovation Solution
A computing system dynamically determines and implements security policies based on individual user, device, and application characteristics, adapting to real-time network conditions by generating embedding vectors from session attributes to tailor security measures proactively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If static and uniform security policies are implemented across the network, then security management is simplified and easier to enforce, but the security becomes inadequate for specific user needs and overly restrictive for low-risk users
Solution Approach 1:
The patent implements dynamic security policies that automatically adjust based on real-time network conditions, user behavior patterns, and threat assessments. The system transitions from static configuration to dynamic adaptation by continuously monitoring network traffic and modifying security parameters without manual intervention, resolving the contradiction between ease of implementation and adaptability.
Solution Approach 2:
The system changes security parameters dynamically by adjusting policy strictness, access control levels, and monitoring intensity based on assessed risk levels. Different users receive different security parameter configurations automatically generated by the system, maintaining ease of management while achieving user-specific adaptability.
2Reliability
If manual configuration of security policies is used, then policy implementation is straightforward and controllable, but the system cannot adapt to changing network conditions and 99% of firewall breaches occur due to misconfiguration
Solution Approach 1:
The system performs self-configuration by automatically generating, adjusting, and optimizing security policies based on monitored network data and threat intelligence. The automated system eliminates manual configuration errors while adapting to changing conditions, simultaneously improving reliability and adaptability.
Solution Approach 2:
The system implements continuous feedback loops where network traffic patterns, threat detections, and policy effectiveness metrics are monitored and fed back to automatically adjust security configurations. This closed-loop approach ensures accurate configuration that adapts to changing network conditions without manual intervention.
3Ease of operation
If uniform security policies are applied to all users and devices, then enforcement is consistent and simple, but high-value targets receive insufficient protection while low-risk users face unnecessary restrictions
Solution Approach 1:
The patent applies differentiated security measures to different users, devices, and network segments based on their specific risk profiles and value to the organization. High-value targets receive enhanced protection while low-risk users experience minimal restrictions, maintaining operational simplicity through automated differentiation rather than manual configuration.
4Device complexity
If static security policies are maintained, then system complexity is reduced and easier to manage, but the security measures become misconfigured over time and fail to prevent modern threats
Solution Approach 1:
The system maintains low management complexity while achieving high effectiveness by implementing dynamic policy adjustment that occurs automatically without increasing administrative burden. The dynamic system adapts to new threats and network conditions autonomously, preventing the security stagnation that occurs with static policies.
Data Source
AI summary
A system receives one or more ingress data packets from a client device or a user in a network. The system obtains attributes, via packet inspection, from the one or more ingress data packets, and determines one or more embedding vectors from the attributes. The one or more embedding vectors represent a status of a session during which the ingress data packets are obtained. The system transmits the one or more embedding vectors as inputs to a trained machine learning model. The system infers, using the trained machine learning mode, one or more security policies based on the embedding vectors. The system provides or implementing the one or more security policies.