What data for this category looks like
Cybersecurity research report data sources include national cybersecurity notification center alert documents, third-party security vendor technical reports, archived content from open-source vulnerability disclosure platforms, and internal enterprise security audit logs. Update frequency varies by content type: public vulnerability disclosures are synced in real time. Industry technical research reports are updated quarterly or on major security event milestones. Internal security logs are synced daily. Documents fall into two categories: structured and unstructured. Structured documents include fields such as CVE ID, CVSS score, and affected asset type. Unstructured documents include attack method descriptions and defense suggestions. Some documents come with standardized threat level tags.
What constraints do these characteristics impose on workflow orchestration
Decentralized data sources require workflows to configure multi-source access nodes to pull data from sources with different interface formats. Differences in update frequency require splitting full sync and incremental sync branches, and triggering corresponding processes on different cycles. Coexisting structured and unstructured documents require separate configuration of field extraction rules and text splitting rules to avoid parsing errors. Standardized threat level and CVSS score fields need numerical verification and range filtering nodes to ensure compliant input for subsequent processing. Sensitive internal security log data requires embedding data desensitization nodes to prevent sensitive information leaks.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Core technical paragraphs of cybersecurity research reports are lengthy. This range adapts to long text input requirements and avoids truncation of critical information |
retrieval_limit | Top 8–12 entries | Cybersecurity research reports have high core information density. Too many retrieved results will exceed the context window. Too few will miss critical vulnerability details |
chunk_size | 1000–1500 characters | Technical descriptions in security research reports have logical coherence. Splitting too finely will disrupt the complete expression of attack methods and remediation solutions |
sync_schedule | Daily incremental sync + quarterly full sync | Public vulnerability disclosures require high real-time performance. Industry research reports are updated quarterly. This schedule matches the update rhythms of both data types |
data_masking_enabled | Enabled | Internal security logs contain sensitive asset information. This setting automatically desensitizes fields such as IP addresses and account names |
input_length_intercept | Enabled, threshold set to 15000 characters | Adapts to the length of merged research report text. This avoids input upper limit errors from AI conversation nodes |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: A "file parsing failed" error displays in the interface when uploading a cybersecurity research report PDF via the workflow file upload node. Cause: The
chunk_sizeparameter adapted for long documents is not configured, or multi-format parsing support is not enabled. This results in failure to split and parse large-volume documents normally. - Phenomenon: The AI conversation node returns an "input length exceeded limit" error, and excessively long input is not intercepted as expected. Cause: The
input_length_interceptconfiguration is not enabled, or the threshold is set lower than the total length of merged research report text. The interception is not triggered in advance. - Phenomenon: Knowledge base retrieved research report results include irrelevant content, and the number of results does not match the configured
retrieval_limit. Cause: No data source filtering rules are configured, the pull is not limited to only cybersecurity-related documents, or the similarity threshold is set unreasonably, introducing non-target content.
How to confirm the configuration is complete
- Manually upload a standard cybersecurity research report PDF, check the parsed text splitting results, and confirm that the splitting length matches the configured range of
chunk_size. - Trigger a full sync process, check the data source pull logs, confirm that only cybersecurity-related documents are pulled, and the sync cycle matches the
sync_schedulesetting. - Pass a variable containing vulnerability type into the workflow, check the knowledge base retrieval results, and confirm that the results match the preset variable filtering conditions.
- Simulate input text that exceeds the threshold, confirm that the workflow triggers
input_length_interceptinterception, returns a clear input over-limit prompt, and does not directly throw an error.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.