Standard taxonomy translates AI threats into monetary risk
Enterprise
The AI System Threat Vector Taxonomy described in the paper addresses a specific and practical problem: teams speak different languages. Technical teams focus on algorithmic vulnerabilities, auditors care about clauses and penalties, and finance wants dollars. The taxonomy bridges that gap by organising risks into nine domains (Misuse, Poisoning, Privacy, Adversarial, Biases, Unreliable Outputs, Drift, Supply Chain and IP Threat) and linking each to business loss categories: confidentiality, integrity, availability, legal and reputation. It also maps 52 operational sub‑threats and aligns with ISO/IEC 42001 controls and the NIST AI Risk Management Framework (RMF), and the authors validate coverage against 133 documented incidents from 2025.
What this means for operations and security
At a practical level the taxonomy stops being an academic chart and starts being a checklist for the run book. It lets you translate a technical finding into a potential balance‑sheet impact and therefore ask the right questions of procurement, insurance and leadership. For SREs and security engineers the immediate value is clarity: if a model endpoint is classified under Misuse and Confidentiality, that tells you to treat it like an exposed data asset, not a benign API.
Think in terms of a simple data path diagram in plain words: User -> Model endpoint -> Inference host (GPU) -> Vector database -> Model artefacts -> Training data store. Each hop has distinct failure modes. Model endpoints invite misuse and credential theft. Inference hosts and GPUs raise concerns about secrets in memory and multi‑tenant leakage. Vector stores can leak sensitive embeddings when attackers probe similarity. Training pipelines are where poisoning and drift take root. Supply chain ties back to third‑party models, libraries and container images.
Quick checklist for urgent triage:
- Inventory and classify every model endpoint and where it maps to business loss categories.
- Segment inference workloads and isolate GPUs and vector stores from general compute and from public networks.
- Instrument telemetry for input distribution, query rates and embedding similarity probing so you can detect probes and drift early.
If you have sixty minutes, do these steps in order: verify you have an up‑to‑date model and endpoint inventory; confirm authentication and per‑user rate limits are in place; ensure keys and model artefacts are not stored on shared persistent volumes in plain text; and enable monitoring for anomalous query patterns and model output distribution shifts. Over the next week, add canary models for high‑risk endpoints, enable encryption at rest and in transit for vector stores, and schedule regular retraining and provenance checks for models that touch regulated data.
There is also a governance angle. Because the taxonomy ties threats to loss categories and aligns to NIST AI RMF and ISO 42001, it supports a defensible argument for monetary reserves and insurance conversations. That is useful when the board asks for expected loss numbers rather than a colour on a heat map. Caveat: the taxonomy relies on incident reports and may under‑represent slow failures such as bias and gradual drift, so treat low incident counts as a noisy signal, not proof of safety.
In short, the taxonomy gives you a repeatable way to map technical controls onto business impact. For ops teams under time pressure it is a pragmatic tool: use it to prioritise containment and monitoring steps first, then plan the longer work around model provenance, vendor controls and audit evidence so you can demonstrate that your risk estimates are not just hand waving but auditable and repeatable.
Additional analysis of the original ArXiv paper
📋 Original Paper Title and Abstract
Standardized Threat Taxonomy for AI Security, Governance, and Regulatory Compliance
🔍 ShortSpan Analysis of the Paper
Problem
The paper investigates fragmentation in AI risk assessment across regulated sectors and the resulting language barrier between technical security teams who focus on algorithmic vulnerabilities and legal and compliance professionals who address regulatory mandates. This disconnect hampers translating technical vulnerabilities into financial liability and leaves organisations unable to answer core economic questions about contingency reserves, control return on investment, and insurance exposure. To bridge this gap it introduces the AI System Threat Vector Taxonomy, a structured ontology designed for Quantitative Risk Assessment. The framework divides AI specific risks into nine domains Misuse, Poisoning, Privacy, Adversarial, Biases, Unreliable Outputs, Drift, Supply Chain and IP Threat, comprising 52 operational sub threat vectors, and links each domain to business loss categories Confidentiality, Integrity, Availability, Legal and Reputation, enabling translation of abstract threats into measurable financial impact. The taxonomy is empirically validated using 133 documented AI incidents from 2025 with full classification coverage and is explicitly aligned with ISO/IEC 42001 controls and NIST AI RMF functions to support auditability and regulatory alignment.
Approach
The study employs a four phase mixed methods design: Taxonomy Development, Quantification Integration, Regulatory Alignment and Empirical Validation. Phase 1 builds the taxonomy from a systematic literature review, domain synthesis and sub threat identification. Phase 2 integrates quantification by mapping threats to loss categories, selecting distributions and adapting the convolved Monte Carlo framework. Phase 3 aligns the taxonomy with regulatory and standards contexts including NIST AI RMF, ISO/IEC 42001 and the EU AI Act. Phase 4 validates the taxonomy through analysis of 133 AI incidents and comparison against four existing AI risk frameworks. The research relies on a four phase process to ensure comprehensive coverage, operational quantifiability, regulatory compatibility and empirical grounding, and provides the inputs necessary for probabilistic modelling such as Monte Carlo simulations to move beyond qualitative heat maps toward monetary risk estimates. The full taxonomy and mapping files are available in an open source repository licensed CC BY 4.0.
Key Findings
- The taxonomy consolidates AI threats into nine domains with 52 sub threats and ties each to business loss categories, enabling structured, quantitative risk modelling across the AI lifecycle.
- Empirical validation using 133 incidents from the AI Incident Database shows 100 per cent classification coverage; Misuse and Unreliable Outputs are the most prevalent failure modes, while Biases and Drift are under represented, a pattern attributed to reporting bias.
- Compared with MITRE ATLAS, OWASP Top 10 for LLMs and ENISA Threat Landscape, the taxonomy offers broader coverage that integrates security, privacy, fairness and reliability within a single framework and directly supports the Map and Measure functions of the NIST AI RMF as well as ISO 42001 controls.
- Practical implications include enabling probabilistic risk assessment, enabling reserve setting and ROI analysis, and supporting structured governance, incident response and vendor risk management; auditors and regulators can use the taxonomy to produce auditable and evidence based documentation.
Limitations
Empirical validation relies on 133 incidents from 2025, with observed biases in the frequency of certain threat types. In particular low counts for Biases and Drift are likely influenced by reporting bias, indicating potential under representation of some threat classes. While the approach provides a rigorous framework for quantification, broader longitudinal validation and continuous updating will be necessary to maintain coverage as AI systems evolve and new threats emerge.
Why It Matters
The AI System Threat Vector Taxonomy provides a standardised, business oriented threat language that translates technical vulnerabilities into measurable financial risk, supporting quantitative risk assessment and regulatory auditability. Its alignment with ISO 42001 and the EU AI Act, together with its explicit mapping to NIST AI RMF functions, offers organisations a practical governance tool for risk communication, audit readiness, and vendor risk management. By enabling convolved Monte Carlo based loss modelling, it helps organisations move from qualitative heat maps to defensible monetary exposure, supports insurance considerations and helps address societal and security concerns around privacy, surveillance, misuse and regulatory penalties.