How can organisations compare identity or analytics datasets more fairly?

Why This Matters for Security Teams

Fair comparison sounds simple until different datasets are being judged for different jobs. One source may be strong at entity grouping, another at attribution, and a third at operational proof. If teams compare only raw counts, they can overrate noisy analytics or underrate a smaller dataset with stronger evidence quality. That distorts incident response, control validation, and executive reporting.

For NHI-focused programs, this is not theoretical. NHIMG research shows that 80% of identity breaches involved compromised non-human identities such as service accounts and API keys, which means weak comparison methods can hide the sources most likely to matter operationally. The 52 NHI Breaches Analysis is a useful reminder that repeated patterns often look different across evidence sets, even when they describe the same threat class.

Current guidance suggests comparing datasets by claim type, confidence, and evidence quality before looking at volume. That aligns better with how security decisions are actually made, especially when comparing inventories, detections, or exposure findings against each other. In practice, many security teams discover the bias in their comparisons only after a false sense of completeness has already shaped prioritisation.

How It Works in Practice

The practical method is to create a shared evaluation frame before any comparison begins. Start by defining what each dataset is allowed to claim: grouping, naming, ownership, activity, risk, or verified compromise. Then score the dataset on whether it provides direct evidence, inferred evidence, or unverified enrichment. This is where fair comparison starts, because two datasets can disagree while still both being useful.

Next, compare confidence and evidence quality separately from scale. A small set of confirmed records may be more trustworthy than a large set of weakly supported matches. That distinction matters when analysts are deciding whether to triage, automate, or escalate. The Ultimate Guide to NHIs is helpful here because it frames NHI governance as a lifecycle problem, not just a data problem. For broader measurement discipline, the NIST Cybersecurity Framework 2.0 supports the idea that evidence should be mapped to the control outcome it can actually support.

• Separate claim categories before comparing counts.
• Record confidence levels and the source of evidence for each row.
• Distinguish direct observations from enrichment or inference.
• Test whether duplicate removal changes the conclusion.
• Measure accuracy against a small verified sample, not just the full dataset.

If the goal is comparing identity inventories, one dataset may be better at discovering objects while another is better at proving ownership or last use. If the goal is analytics, one dataset may detect more events while another produces fewer false positives. These controls tend to break down when data is merged across tools with incompatible identifiers, because mismatched schemas make confidence and evidence quality hard to preserve.

Common Variations and Edge Cases

Tighter comparison rules often increase analyst effort, requiring organisations to balance analytical fairness against speed and reporting simplicity. That tradeoff is real, especially when stakeholders want a single score or leader-board view.

There is no universal standard for this yet, so best practice is evolving. For some teams, the right comparison is between raw detection coverage and confirmed precision. For others, it is between attribution quality and remediation usefulness. The key is to avoid pretending those are the same metric. When comparing breach research, for example, a dataset with fewer entries may still be more actionable if it includes stronger operational proof, better linkage, or clearer source validation.

Another edge case is when one dataset is deliberately partial, such as a curated investigation sample or a high-confidence export from a control plane. In those cases, raw volume can be misleading by design. Fair comparison should document scope, sampling method, and time window alongside the results, then use a common rubric to prevent apples-to-oranges conclusions. Teams that skip that step usually end up rewarding noisy completeness instead of decision-grade accuracy.

Related resources from NHI Mgmt Group

• When does a machine identity become a compliance problem?
• Why is it important to integrate identity and data governance?
• When does secret exposure become a broader identity risk?
• How do organisations operationalise NHI ownership at scale?