A local explanation describes why a model produced a specific output for one prediction or a small set of similar predictions. Unlike global explanations, it focuses on the factors behind an individual decision and is often used to investigate anomalies, build trust, and support human review.
Expanded Definition
A local explanation is the narrow, decision-level view of model behaviour: it describes why a specific prediction, ranking, or classification was produced for one input or a small cluster of similar inputs. In practice, it is used to attribute influence to features, tokens, or other inputs at the point where a decision was made, rather than describing the model’s overall logic. That makes it distinct from a global explanation, which aims to summarise the model’s general behaviour across many cases.
In AI governance, local explanations matter because they help operators test whether an output is plausible, biased, unsafe, or sensitive to irrelevant inputs. Definitions and methods vary across vendors and model types, so no single standard governs this yet. The most useful references are the NIST AI Risk Management Framework and the NIST Cybersecurity Framework 2.0, both of which reinforce transparency, accountability, and risk treatment as governance outcomes. For NHI and agentic AI use cases, local explanations are especially important when an autonomous system is allowed to trigger actions, retrieve data, or recommend decisions that a human must review.
The most common misapplication is treating a local explanation as proof that the model is generally reliable, which occurs when teams confuse a one-off rationale with validated system-wide behaviour.
Examples and Use Cases
Implementing local explanations rigorously often introduces performance and interpretability tradeoffs, requiring organisations to weigh faster decision support against the overhead of generating and validating explanation data.
- A credit risk model flags a single application for manual review, and the explanation shows which income, debt, and transaction signals pushed the score down.
- A fraud detection engine surfaces one payment as suspicious, and analysts use the local explanation to check whether geography, device signals, or velocity rules drove the alert.
- An internal access model denies a privilege request, and the reviewer inspects the explanation to confirm the denial was based on policy-relevant attributes rather than a spurious proxy.
- An agentic AI workflow recommends opening a ticket or calling an API, and a local explanation helps the operator understand which retrieved context and prompt elements influenced that action.
- For model governance, teams may compare a local explanation with guidance from NIST Cybersecurity Framework 2.0 to document how a specific output fits review, logging, and accountability requirements.
Why It Matters for Security Teams
Security teams rely on local explanations to investigate anomalous outputs, justify automated decisions, and detect when a model is overfitting to irrelevant or sensitive signals. That is particularly important where AI affects access decisions, fraud screening, incident triage, or agentic actions with real operational consequences. A local explanation does not replace testing, monitoring, or secure model development, but it gives reviewers a practical lens for case-by-case challenge and escalation.
For identity and NHI-adjacent use cases, local explanations can reveal whether a model is keying off device posture, session context, or other attributes that should not dominate a decision. This becomes a governance issue as soon as an automated system influences who gets access, what gets blocked, or which event gets escalated. Teams should also align explanation workflows with broader AI risk practices, including human oversight, documentation, and change control. Where explanations are used in regulated or high-impact environments, they may need to support audit evidence rather than simply satisfy curiosity. Organisations typically encounter the cost of weak local explanations only after a disputed decision, at which point the term becomes operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 address the attack and risk surface, while NIST AI RMF, NIST CSF 2.0 and NIST AI 600-1 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| NIST AI RMF | NIST AI RMF emphasises transparency, validity, and accountability for AI decisions. | |
| NIST CSF 2.0 | GV.OV-01 | CSF 2.0 supports oversight and accountability for technology decisions and risk. |
| NIST AI 600-1 | The GenAI profile highlights transparency and trustworthy system behaviour expectations. | |
| OWASP Agentic AI Top 10 | Agentic AI guidance stresses understanding tool-use decisions and human review points. |
Use local explanations as part of governance, testing, and human oversight for individual AI outputs.
Related resources from NHI Mgmt Group
- Why are local .env files and config notes risky in Microsoft 365?
- How should teams respond to a local Linux privilege escalation flaw in shared environments?
- What is the difference between global identity strategy and local governance?
- How should security teams handle local accounts in cloud and SaaS apps?
