How to fix the AI terminology gap
Reading Time: 5 minutes
Authors:
Trilateral Research |
Date: 25 September 2025
Ask five experts to define “AI regulation” or “AI risk” and you may hear ten different answers. This points to the difficulty of establishing a shared vocabulary between the different stakeholders involved in AI. While the issue may seem trivial, it can act as a significant hindrance both in AI enablement and AI governance.
For data protection and cybersecurity officers, the AI terminology gap can result in mismatched labels slowing down work on DPIAs and leading to muddled incident responses, as well as making audits more complicated than they need to be. Recent research underscores the point: risk frameworks often use different words for the same thing (or omit key risks entirely), and jurisdictions bundle very different approaches under the single banner of “AI regulation”.
This article identifies the gap, shows how to mitigate it and provides practical guidance that can adopt.
The governance gap starts with words
When AI models move from the lab to production, words become the way through which shared understanding is established. If engineers discuss “interpretability,” policy teams demand “explainability,” and regulators reference “transparency obligations,” you’ll struggle to prove accountability even if everyone broadly means the same thing. Fragmented vocabularies scatter risk across documents and dashboards. The same issue may appear under three headings or vanish entirely.
A helpful way to cut through the noise is to group risks by where they arise. One widely used approach maps risks to five zones: training data (collection bias, consent, provenance), inference (runtime security, model misuse, prompt injection), output (hallucination, harmful content, fairness), non‑technical (governance, procurement, workforce), and agentic behaviours (autonomy, goal misalignment, tool access). Whether you adopt that exact set or a tailored variant, the key is consistency. Choose a small set of categories, define them clearly, and use them everywhere. In an ideal world, a consistent vocabulary should be used across DPIAs and model cards to SOC runbooks and supplier questionnaires.
Clear definitions reduce duplication, expose gaps, and make control ownership unambiguous. Here are some common situations that may indicate you are experiencing this issue:
- Risk logs repeat the same issue under different names.
- DPIAs “pass”, yet security reviews still flag the model.
- Suppliers “comply” but cannot map their controls to your policies.
- Incident reports use terms senior stakeholders do not recognise.
- Training materials conflict with frontline procedures.
Regulation is not one‑size‑fits‑all
The terminology problem encompasses external requirements alongside internal ones. For example, “AI regulation” is a catch‑all that represents a spectrum of different approaches. In practice it can refer to at least three different models: binding, risk‑based legislation; principles‑led guidance; and content or platform rules that prioritise information control. If teams don’t distinguish these, a ticked box in one jurisdiction can create a false sense of safety elsewhere.
What “AI regulation” might mean in practice:
- Risk‑based, binding law: classifications, conformity assessment, documentation, market surveillance.
- Principles‑based guidance: non‑statutory expectations, regulator toolkits, soft‑law duties.
- Content‑focused rules: provenance/watermarking, moderation obligations, disclosure requirements.
- Sector overlays: health, finance, and critical infrastructure standards that interact with AI rules.
For data protection and cybersecurity officers, the practical implication is straightforward: map obligations to your controls using your glossary, not the law’s headline.
If a regulation speaks in terms of “explainability,” translate that to the technical artefacts your engineers actually produce (for example, feature attribution, counterfactuals, or traceable prompts). If another jurisdiction emphasises “robustness,” connect it to adversarial testing, model monitoring, and red‑teaming. This translation layer converts legislation into implementable control evidence, avoids apples‑to‑oranges audits, and increases confidence at board and regulator level.
From confusion to control: your practical playbook for AI Terminology
You don’t need a 200‑page manual to get started. You need a small, living lexicon and a way to embed it.
A pragmatic, repeatable approach:
- Draft a minimal glossary (10–20 terms). Start with the most common items: explainability/interpretability/transparency, robustness, bias/fairness, safety, monitoring, red‑teaming, human oversight. Keep definitions short and operational.
- Adopt a simple risk taxonomy. Use five zones (training data, inference, output, non‑technical, agentic) or a variant that fits your context. Label every risk and control with one zone.
- Create a translation table. Map legal phrases and regulator guidance to your glossary. Example: “transparency obligation” → data provenance disclosures + model card + user notice.
- Get involve early in the process. Ideally, any sort of AI enablement or adoption should involve a sociotech approach where data protection and cybersecurity officers are involved in the entire AI lifecycle. This provides the chance to notify other stakeholders of the issues around language and establish a common vocabulary early in the process.
- Bake it into artefacts. Update DPIA templates, model cards, threat models, and supplier questionnaires to reference the glossary explicitly. Version‑control those templates.
- Assign ownership and cadence. Establish who is responsible for maintaining the glossary (for example, it might be jointly led by DPO and CISO, with engineering input). Review quarterly or after major releases.
- Measure and improve. Track rework due to terminology issues, audit queries closed on first pass, and time‑to‑decision in governance forums.
| Legal / Policy phrasing | Engineering / ML phrasing | Security / Operations phrasing | Shared understanding (programme‑wide) |
| Right to explanation; meaningful information | End‑user reasons; rationale texts; counterfactuals | Decision audit trails; case notes | Explainability — Plain‑language reasons for significant outcomes, suitable for users and reviewers. Evidence: user‑facing rationale, examples, FAQs. |
| Technical explainability for assessors | Feature attribution (SHAP, IG), probes | Troubleshooting signals; model introspection | Interpretability — Technical insight into model behaviour for engineers and auditors. Evidence: attribution reports, probe results. |
| Transparency obligations; notices; disclosures | Model cards; data sheets; documentation | Traceability; change logs | Transparency — Disclosures about data, process, and accountability for regulators, customers, and staff. Evidence: model card, lineage, notices. |
| Reliability and safety; “state of the art” | Adversarial/stress tests; guardrails | Resilience to attack; anomaly resistance | Robustness — Resistance to perturbations and misuse within scope. Evidence: test results, red‑team findings, mitigations. |
| Non‑discrimination; equality duties | Bias metrics (TPR parity, calibration) | Harm/abuse risk monitoring | Fairness — Measurable parity across groups with documented trade‑offs. Evidence: metric dashboard, mitigation plan. |
| Lawful basis; licences; retention | Dataset lineage; versioning; hashing | Supply‑chain integrity | Data provenance — Verifiable origin and rights for all data used. Evidence: lineage graph, licence register. |
| Valid consent; DSARs; withdrawals | Consent flags in pipelines; filtering | Access control tied to consent; deletion SLAs | Consent management — Capture, honour, and audit permissions end‑to‑end. Evidence: consent logs, DSAR playbooks. |
| Meaningful human review | Human‑in‑the‑loop checkpoints; override UI | Escalation paths; kill switch | Human oversight — Named roles can review, override, and halt actions with tools to do so. Evidence: RACI, UI screenshots, kill‑switch test. |
| Ongoing evaluation; post‑market monitoring | Drift/performance telemetry; alerts | SOC integration; incident runbooks | Monitoring — Live metrics and alerts for performance, safety, and abuse, with on‑call response. Evidence: dashboards, alert rules, tickets. |
| Independent assessment; documentation | Jailbreak/prompt probes; scenario tests | Threat‑led exercises; purple teaming | Red‑teaming — Structured adversarial exercises pre‑ and post‑release. Evidence: plan, findings, remediation tracker. |
| High/medium/low categories; obligations | Impact/severity labels gating releases | Risk scoring driving controls | Risk classification — Standard criteria determine control depth and sign‑off. Evidence: risk register, criteria doc. |
| DPIA / AI impact assessment | System description; data flows; mitigations | Threat model; controls; residual risk | Impact assessment — One pack covering privacy, safety, and security with traceable mitigations. Evidence: signed assessment, control map. |
| Controller/processor roles; governance | Code owners; reviewers; change control | Control owners; audit trails | Accountability — Named owners and auditable decisions from design to operations. Evidence: RACI, change history, approvals. |
| Duty to prevent harmful outputs | Prompt hygiene; sandboxing; I/O filters | Least‑privilege tool access; abuse detection | Prompt injection — Prevent and detect malicious inputs that redirect tools. Evidence: filter rules, tests, audit logs. |
Figure 1: An example glossary
As your programme matures, bring AI Terminology into security operations. Tag incidents and playbooks with the same glossary terms. Align detection content with model behaviours (for example, alerts for “tool access escalation” under the agentic category). Link tabletop exercises to glossary definitions so decision‑makers practise with the language they will use during real events.
Language is a control surface. If you get it wrong, you waste cycles, duplicate risks, and disappoint auditors. Get it right and you accelerate safe delivery, improve evidence quality, and make better decisions under pressure. The research is clear: frameworks label the same risks differently, and “AI regulation” can mean very different things. Your response should be clear too: define a concise glossary, map it to a stable risk taxonomy, and embed it in DPIAs, security processes, and supplier management. Use AI Terminology as the bridge between law, policy, and code. Start small, iterate, and keep people at the centre.
