Why do dimensionality reduction plots sometimes mislead reviewers?

Why Dimensionality Reduction Plots Can Mislead Reviewers

Dimensionality reduction plots are useful for pattern spotting, but they are not faithful maps of the original feature space. Tools such as t-SNE and UMAP tend to preserve local neighbourhoods better than global distances, so clusters can appear farther apart, closer together, or more distinct than the source embeddings really are. That makes them easy to overread in security review, model governance, and incident analysis.

This matters because reviewers often treat a 2D plot as evidence of separation, bias, or anomalous structure when it is only a projection. NIST’s NIST Cybersecurity Framework 2.0 stresses that decisions should be grounded in validated evidence, not a single indicator. In NHI governance, the same caution applies when visualisations are used to infer identity risk or access behaviour. The Ultimate Guide to Non-Human Identities from NHI Mgmt Group notes that NHIs outnumber human identities by 25x to 50x in modern enterprises, which makes shallow visual interpretation especially risky at scale. In practice, many teams discover a misleading plot only after an executive decision or model sign-off has already been made, rather than through deliberate validation.

How to Read These Plots Without Overstating the Signal

The safest approach is to treat the plot as a hypothesis generator, then confirm the claim against the original embedding space or raw features. Reviewers should ask what the projection preserves, what it distorts, and whether the parameter choices changed the story. Two runs with different random seeds can produce visibly different layouts even when the underlying data has not changed.

Useful checks include:

• Compare the 2D view with nearest-neighbour relationships in the original space.
• Test whether apparent clusters persist across multiple seeds and parameter settings.
• Inspect whether scaling, preprocessing, or class imbalance is driving the shape.
• Validate any operational conclusion with metrics, not just visual separation.

For governance-heavy environments, the same discipline used in NHI reviews should apply to model plots. The Schneider Electric credentials breach shows how quickly identity assumptions can fail when visibility is incomplete, and that lesson carries over to review workflows that rely on a single chart. NIST guidance on risk management and the identity lifecycle reinforces the need to corroborate evidence before acting on it. These controls tend to break down when reviewers extrapolate from a projection to the full feature space, because the visual layout is not a stable measure of true distance.

Common Edge Cases and Review Traps

Tighter visual simplicity often increases analytical risk, requiring organisations to balance interpretability against fidelity. That tradeoff becomes more serious when plots are used for compliance evidence, anomaly triage, or executive reporting.

Some edge cases are especially prone to misinterpretation. Sparse high-dimensional data can create artificial islands that look meaningful but are just projection artefacts. Dense overlapping data can hide separation that exists in the original space. Small sample sizes can exaggerate cluster boundaries, while different preprocessing choices can completely change the apparent geometry. Current guidance suggests documenting the projection method, perplexity or neighbourhood settings, seed value, and the specific decision the plot is meant to support.

The practical rule is simple: a dimensionality reduction plot can support review, but it should not be the sole basis for a conclusion. That is especially true in security workflows where identity, access, or model behaviour decisions carry real operational consequences. In high-stakes environments, reviewers should pair the plot with quantitative validation and trace back to the original data before claiming separation, similarity, or risk.

Related resources from NHI Mgmt Group

• What is the difference between privilege reduction and secret rotation?
• When should organisations prioritise entitlement reduction over secret rotation?
• When does zero trust IAM create more friction than risk reduction?
• How should security teams use PAM to improve both compliance and risk reduction?