AI Risk Taxonomy Benchmark 2026
A comparative assessment of four leading AI risk frameworks across five governance dimensions, evaluated against publicly available documentation.
No single framework leads across all five dimensions. Security-origin frameworks (MITRE, OWASP) score well on taxonomy depth and operational controls but show limited coverage for BFSI-specific harms and enterprise governance workflows. Zytra Risk Atlas is the only framework in this benchmark built explicitly for financial services, leading on BFSI specificity, operational controls, and enterprise governance.
Assessment framework
Five dimensions · Independent review · August 2026| Dimension | What it measures |
|---|---|
| 1Taxonomy depth | Named, classified risks with deployment context tags, not broad categories. Specificity and ontological precision weighted over raw count. |
| 2Agentic AI coverage | Explicit treatment of autonomous agents, multi-agent orchestration, MCP tool risks, memory manipulation, and goal-proxy attacks. |
| 3BFSI specificity | Named financial-services harm categories and clause-level mapping to RBI, SEBI, DORA, or EU AI Act. Generic mentions excluded. |
| 4Operational controls | Each risk paired with an implementable control, defined owner, evidence type, and validation cadence. Gap between risk coverage and control coverage scored. |
| 5Enterprise governance | Support for residual risk calculation, board-level heat map output, control effectiveness scoring, and GRC platform integration. |
Figure 1, Framework comparison matrix
Bar length encodes relative strength within each dimension. Scores derived from public documentation; sources listed in Appendix A.
| Framework | Type | Taxonomy Depth |
Agentic Coverage |
BFSI Specificity |
Operational Controls |
Enterprise Governance |
|---|---|---|---|---|---|---|
| Zytra Risk Atlas | Enterprise Product | Highest | Highest | Highest | ||
| MIT AI Risk Repository | Research | |||||
| NIST AI RMF | Standards | |||||
| MITRE ATLAS | Threat Taxonomy | |||||
| OWASP LLM/Agentic Top 10 | Security Reference |
Appendix A, Scoring methodology
All scores are independent assessments based on public documentation as of August 2026. Corrections welcomed, email the research team via the website.
Dimensions
- Taxonomy depth
- Agentic AI coverage
- BFSI specificity
- Operational controls
- Enterprise governance
Source documentation
- 1Taxonomy depth
- Named risk categories and sub-categories counted from published documentation. Specificity assessed against deployment context tagging (inference-time vs. training-time), harm type, and affected stakeholder. Frameworks with rich ontological structure and deployment tagging score higher than broad, undifferentiated category lists.
- 2Agentic AI coverage
- Frameworks searched for explicit treatment of: autonomous agents, multi-agent orchestration, model context protocol (MCP) tool risks, memory injection and manipulation, goal-proxy attacks, and tool-use side-effects. Partial coverage (e.g. prompt injection without tool-call risks) scored proportionally.
- 3BFSI specificity
- Checked for named BFSI harm categories (KYC bypass, credit decisioning bias, transaction fraud, regulatory misrepresentation) and clause-level mapping to RBI Master Directions, SEBI AI Framework, DORA Articles, or EU AI Act Annex III. Generic financial services mentions without clause-level mapping received lower scores.
- 4Operational controls
- Each risk assessed for a paired implementable control, including ownership definition, evidence type, and validation cadence. Frameworks scored against the gap between risk coverage and control coverage; detection-only controls (logging, alerting) weighted lower than preventive controls.
- 5Enterprise governance
- Evaluated for: residual risk calculation (inherent risk × (1 − control effectiveness)), board-level heat map output capability, control effectiveness scoring across multiple dimensions, and GRC integration (ServiceNow, Archer, or equivalent). Standards and research taxonomies requiring significant custom tooling to produce board-level output scored lower than enterprise products with built-in governance workflows.
Zytra Risk Atlas, 141 risks, 157 controls, BFSI-specific
Clause-level regulatory mapping · Residual risk scoring · Board-ready heat maps