Operational Architectures for Hybrid AI-Based Network Intrusion Detection: A Critical Review and Multi-Axial Taxonomy

Artificial intelligence is widely used in network intrusion detection, but evidence regarding operational architecture remains fragmented across model families, fusion patterns, deployment environments, and evaluation practices. We conducted a PRISMA 2020-compliant systematic review of secondary AI-NIDS reviews published from 1 January 2020 to 22 September 2026. OpenAlex and arXiv were searched on 22 September 2026; deterministic DOI/title deduplication and prespecified text rules preceded title/abstract screening, and a single reviewer assessed the retrieved full texts. Review-level limitations were appraised across eligibility, search reproducibility, selection/data collection, evidence traceability, and synthesis appropriateness; no aggregate quality score or statistical pooling was used. Of 8519 identified records, 1091 duplicates and 2256 automatically ineligible records were removed, 5172 records were screened, 101 reports were sought, 25 full texts were assessed, and 22 reviews were included. The synthesis supports a five-axis taxonomy spanning observation, AI intelligence, fusion, deployment, and trust/operations, but also shows weak search reproducibility in 12 of 22 included reviews and absent selection transparency in 15 of 22. The findings provide a bounded architectural framework rather than a universal performance ranking. Limitations include open-index coverage, 76 unretrieved reports, single-reviewer assessment, and the use of primary studies only as separate illustrative validation sources. The review was not prospectively registered; a revision-stage protocol was frozen before the new search, and its amendment is reported. A worked five-axis example, a decision-level contrast between sequential and hierarchical integration, appraisal-weighted confidence statements, and proposed evaluation criteria for each open challenge make the framework directly usable for design and assessment.