MAE

Expanded Definition

MAE, or Mean Absolute Error, is a model evaluation metric that measures the average magnitude of prediction errors in the original unit of the target variable. In NHI and agentic AI governance, MAE is useful when a team needs to know the typical miss size rather than the direction of the miss. It is commonly used for forecasting, anomaly scoring, capacity planning, and operational models where interpretability matters more than error decomposition.

Definitions vary across vendors when MAE is embedded in broader model-quality dashboards, so practitioners should treat it as a performance measure, not a governance control by itself. It complements metrics such as RMSE, which penalises larger errors more heavily, and it is often easier to explain to risk owners because it preserves native units. For broader control framing, the NIST Cybersecurity Framework 2.0 emphasises measurable oversight and continuous monitoring, which is the right lens for interpreting MAE in operational AI settings. The most common misapplication is using MAE as the sole success criterion for a model whose failures are asymmetric, which occurs when high-impact underestimates and overestimates are treated as equivalent.

Examples and Use Cases

Implementing MAE rigorously often introduces a tradeoff between interpretability and sensitivity to outliers, requiring organisations to weigh a clear business-facing metric against the risk of underestimating rare but severe misses.

• A service desk capacity model reports MAE in predicted ticket counts so operators can see the typical daily forecasting miss in concrete terms.
• An NHI detection model estimates API key usage volume, and MAE helps analysts judge whether predictions are close enough to support alert tuning and threshold setting.
• A secrets exposure prioritisation model uses MAE to measure how far predicted risk scores deviate from observed incident impact, helping security teams validate ranking stability. The broader NHI context is important because the Ultimate Guide to NHIs shows how often secrets and service accounts become operational risk surfaces.
• A cloud cost forecast compares actual spend to predicted spend in dollars, using MAE to communicate average budget miss to finance and security stakeholders.
• An anomaly baseline model for agent activity uses MAE to test whether predicted behaviour remains close to observed behaviour before tighter response rules are applied.

In practice, MAE becomes most useful when teams need a stable, unit-based measure that non-specialists can understand. For technical guidance on operational monitoring and model lifecycle discipline, teams often pair this with NIST Cybersecurity Framework 2.0 as a management reference rather than a metric definition.

Why It Matters in NHI Security

MAE matters in NHI security because many defensive models make decisions about service account behaviour, secret exposure, or agent activity before humans review the output. If MAE is too high, a model may appear acceptable in aggregate while still missing the operational patterns that matter most, such as short-lived credential abuse or unusual tool invocation. NHIMG research shows that 79% of organisations have experienced secrets leaks, with 77% of those incidents resulting in tangible damage, which underscores why weak model evaluation can cascade into real compromise when used for detection or prioritisation. The same guide also reports that only 5.7% of organisations have full visibility into their service accounts, making precise measurement even more important when telemetry is incomplete.

MAE should be used alongside governance checks that ask whether the model is accurate enough for the decision it supports, not just whether it looks statistically sound. The Ultimate Guide to NHIs highlights how often NHI controls fail because organisations cannot see or manage the identities involved, and evaluation gaps can hide that failure. Organisations typically encounter the consequences of a poor MAE decision only after a model misses a misuse pattern or misranks a high-risk identity, at which point MAE becomes operationally unavoidable to address.

Related resources from NHI Mgmt Group

• What is the difference between MAPE and MAE in model evaluation?