Local Explainability

Expanded Definition

Local explainability is the case-level explanation layer used to justify one model output for one entity, event, or transaction. In NHI operations, that might mean showing why an AI agent was denied a tool call, why a service account was flagged, or why a fraud score changed for a single request. It is narrower than global explainability, which tries to describe model behavior overall, and it is usually the version auditors ask for when they need a decision trail tied to the exact input set. Guidance in NIST Cybersecurity Framework 2.0 and the broader NIST Cybersecurity Framework 2.0 supports transparent governance, but no single standard yet fully defines local explainability for agentic systems. Definitions vary across vendors because some tools explain feature contribution, while others surface rules, prompts, or retrieval traces.

The most common misapplication is treating a generic model summary as a local explanation, which occurs when teams present aggregate feature importance instead of the exact factors that influenced one specific decision.

Examples and Use Cases

Implementing local explainability rigorously often introduces latency and logging overhead, requiring organisations to weigh reviewability against operational simplicity.

• A bank explains a declined transfer by showing that the destination was new, the amount exceeded historic patterns, and the sender’s NHI lacked the required privilege context.
• A SOC analyst reviews why an AI agent blocked a secrets lookup after correlating tool intent, source identity, and an abnormal request chain, then compares that result with findings in DeepSeek breach.
• A fraud team uses per-transaction explanations to separate a true positive from a false positive, especially when a model reacts to one-off device, location, or session signals.
• An IAM reviewer traces why a privileged automation account was denied JIT elevation, using the explanation to confirm that the request violated policy rather than model drift.
• A compliance officer records why a recommendation engine suppressed a result, then maps the evidence trail to NIST Cybersecurity Framework 2.0 governance expectations.

In mature NHI environments, local explanations are most useful when they are attached to the exact identity, prompt, policy, and tool context that produced the outcome. They are less useful when they arrive after the decision has already been consumed by downstream automation.

Why It Matters in NHI Security

Local explainability matters because NHI failures are often diagnosed only after an agent, workflow, or automated control has already made a consequential decision. When a model denies access, approves a request, or leaks a sensitive pattern, teams need to know whether the cause was the prompt, the identity posture, the policy, or the retrieval context. That distinction becomes especially important when secrets are involved: according to The State of Secrets in AppSec, organisations take an average of 27 days to remediate a leaked secret, which means explanation records can shape how quickly the blast radius is understood. The same issue appears in agentic workflows, where hidden reasoning gaps can mask unsafe tool access, policy bypasses, or an overconfident model recommendation. For governance teams, local explainability turns a black-box event into evidence that can support incident response, access review, and policy tuning, especially when paired with the control discipline encouraged by NIST Cybersecurity Framework 2.0.

Organisations typically encounter the need for local explainability only after a disputed denial, a policy exception, or a harmful agent action, at which point the explanation becomes operationally unavoidable to address.

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?
• What is the difference between explainability and auditability in agentic AI?