Network Segmentation Change Safe Rollback and Replanning Trajectory Dataset
#risk detection #rollback verification #adaptive replanning #network segmentation management #network change failure recovery #network security operations
- 5000
- 0.3GB
- JSON
- CATL
MOBIUSI INC
Updated:2026-11-02
AI Analysis & Value Prop
This dataset captures agent decision trajectories after network segmentation policy changes encounter anomalies, including incident context, risk assessment, rollback or partial-reversal choices, recovery actions, and network-state verification. The trajectories also record residual risks and replacement change plans, showing the decision chain from failure detection through safe replanning. They support training and evaluation of network segmentation agents for incident recovery, self-correction, and change planning.
Dataset Insights
Sample Examples
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Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| incident_input | object | Change context, observed anomalies, and post-failure network state from the original incident record. |
| risk_assessment | object | The agent's identification of risks and assessment of their impact based on the original incident information. |
| recovery_actions | array | Rollback or partial-reversal actions planned by the agent, along with their expected effects. |
| replacement_plan | object | A safer alternative segmentation policy plan proposed after recovery and state verification. |
| recovery_decision | object | The agent's choice to roll back, partially reverse, or defer action in response to the failed change. |
| trajectory_summary | string | A summary of the decision chain covering anomaly detection, recovery selection, state verification, and replanning. |
| verification_result | object | Network state confirmation, check results, and residual risks recorded after recovery actions. |
Compliance Statement
| Authorization Type | Proprietary - Commercial AI Training License (No Redistribution) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What does the dataset contain?
- It records the decision process after anomalous network segmentation changes, including the original incident, risk assessment, rollback or partial-reversal decision, recovery actions, post-recovery state verification, and replacement change plan.
- Which capabilities can it support?
- The data can support training or evaluation of agent risk assessment, safe recovery decisions, recovery verification, self-correction, and replanning after failed network changes. It can also be used to analyze agent decision trajectories.
- What are the dataset size and quality specifications?
- The available information does not specify sample count, file format, or quality metrics. The schema separates incident input, decisions, actions, verification, and planning; consult the product listing for exact size, delivery format, and quality details.
- How can I obtain the data and confirm its license?
- Check the product listing for available access methods, delivery formats, and license terms. Before use, confirm the permitted scope, use cases, and whether commercial use is allowed; this description does not imply a specific license.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2026,
title={Network Segmentation Change Safe Rollback and Replanning Trajectory Dataset},
author={MOBIUSI INC},
year={2026},
url={https://www.mobiusi.com/datasets/1e27573493ae3f4e5870cffcbf3e7fa5?dataset_task_cate_id=8},
urldate={2026-10-19},
keywords={network segmentation dataset,microsegmentation policy,network change rollback,agent error recovery,network security operations,change risk assessment,network state verification,agent replanning},
version={1.0}
}Using this in research? Please cite us.
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