Feature dependence means two or more input variables are related in a way that affects how a model learns and predicts. In explainability work, dependence matters because many attribution methods rely on assumptions that can become less reliable when features are strongly correlated.
Expanded Definition
Feature dependence describes the way input variables move together, so the value of one feature can carry information about another. In machine learning and explainability, this matters because many attribution methods assume features are conditionally independent or only weakly related. When that assumption fails, the model may still predict well, but the explanation can become misleading.
In NHI security and agentic AI governance, feature dependence is especially important when investigators interpret signals such as source IP, workload metadata, token age, privilege scope, and tool usage. If those variables are tightly coupled, a feature importance score can overstate one control signal while hiding the real causal driver. Definitions vary across vendors and research communities, but the practical concern is the same: dependent inputs can distort post-hoc explanations and make risk reviews look more certain than they are. For governance teams, this is where explainability must be paired with data lineage, feature review, and threat modeling, not treated as a standalone report. The most common misapplication is assuming correlated telemetry can be interpreted independently, which occurs when teams review model outputs without checking how the features were generated or joined.
Examples and Use Cases
Implementing feature-dependence checks rigorously often introduces extra validation work, requiring organisations to weigh explainability confidence against faster model iteration.
- In anomaly detection for service accounts, token age and refresh frequency may be linked, so a simple attribution score can incorrectly treat one as the dominant cause.
- In agent routing, tool choice and prompt template usage may correlate, making it hard to tell whether the agent selected a risky action because of intent or because of inherited workflow structure.
- For access-risk models, privilege level and resource sensitivity often move together, so an explanation can overemphasise one variable unless dependence is examined explicitly.
- In post-incident reviews, analysts may compare signals from the Ultimate Guide to NHIs with model outputs to see whether service account patterns are being conflated with broader workload behavior.
- For implementation guidance, teams often map explainability checks to the NIST view of measurement and governance in the NIST Cybersecurity Framework 2.0, especially where risk scoring affects control decisions.
Practically, this means testing whether correlated features remain stable across environments, data pipelines, and model versions before relying on a single explanation.
Why It Matters in NHI Security
Feature dependence matters because NHI security decisions are often made from signals that are operationally linked, not independent. Service account identifiers, rotation age, vault location, privilege scope, and workload context can all move together, which makes a weak explanation look authoritative. That is dangerous when teams use model outputs to prioritise secrets rotation, flag abnormal access, or decide whether an agent should be constrained. NHI Mgmt Group reports that 80% of identity breaches involved compromised non-human identities such as service accounts and API keys, which shows why misread signals can have direct security impact. The same concern appears in guidance from the Ultimate Guide to NHIs, where visibility, rotation, and offboarding gaps are central governance issues. In explainability work, the point is not to remove dependence, but to recognise when it can make attribution fragile and incident triage less reliable. Organisations typically encounter the cost of this only after an alert or breach investigation produces a confident but wrong explanation, at which point feature dependence 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 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while NIST AI RMF, NIST AI 600-1 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Agentic AI Top 10 | LLM-03 | Explainability can break when correlated inputs mislead post-hoc reasoning. |
| NIST AI RMF | Calls for measurement validity and contextual understanding of model behavior. | |
| NIST AI 600-1 | GenAI governance requires understanding how inputs influence outputs and explanations. | |
| NIST CSF 2.0 | GV.RM-01 | Risk management requires understanding analytical limitations that affect security decisions. |
| OWASP Non-Human Identity Top 10 | NHI-08 | NHI telemetry and secret signals often overlap, creating misleading attribution paths. |
Treat correlated NHI signals as dependent evidence and validate before remediation decisions.
Related resources from NHI Mgmt Group
- When does browser automation become a governance problem instead of a productivity feature?
- What is the difference between a SaaS feature and a security control?
- When does an AI agent become an NHI risk rather than a usability feature?
- When should security teams retire a feature flag or service credential?
