How do teams know whether a knowledge graph is actually improving investigations?

What counts as improvement, not just prettier linking?

A knowledge graph only helps investigations if it changes the analyst’s path, not just the display. The main test is whether it reduces time to the next relevant source, cuts dead-end querying, and makes follow-up moves more repeatable across people. That matters because graph tools can create a false sense of progress when they increase connectedness without improving decision quality.

Security teams often overvalue density: more nodes, more edges, more lineage, more visual context. But an investigation is not improved by added complexity unless the graph helps an analyst choose the next question faster and with less uncertainty. In NHI environments, this is especially important because sprawling service accounts, secrets, and delegated access can hide the real path of misuse. NHIMG research has found that only 5.7% of organisations have full visibility into their service accounts, which is why investigation tooling must prove it improves navigation, not just coverage. For a broader control lens, NIST SP 800-53 Rev 5 Security and Privacy Controls remains useful for thinking about auditability and traceability, while Ultimate Guide to NHIs frames why machine identity visibility is so often the limiting factor.

In practice, many security teams discover a graph is decorative only after analysts keep re-running the same searches in a different layout.

How should teams measure whether the graph is actually helping an investigation?

The cleanest measurement approach is to compare investigation work before and after the graph is introduced, using the same class of cases and the same analyst roles. Start with time-to-next-relevant-source: how long it takes to move from an alert, lead, or suspicious entity to an evidence source that genuinely advances the case. Then measure dead-end queries, which show whether the graph is producing false pivots or low-value branches. Finally, look at path consistency: do different analysts converge on similar investigation routes when given the same starting point?

Those measures work because they reflect investigative usefulness rather than model quality in the abstract. A graph can be structurally correct and still fail operationally if it does not support fast, reproducible reasoning. That is common in identity-heavy environments where analysts need to understand relationships among workloads, tokens, API keys, certificates, and delegated permissions. The graph should surface the relationship that matters for the case, not every possible relation in the graph.

• Measure baseline and post-change cases of the same type, rather than only average query volume.
• Track the proportion of investigations that reach a meaningful decision with fewer pivots.
• Compare analyst-to-analyst variance on the same scenario to test repeatability.
• Check whether exported evidence paths are understandable after the case closes.

Where this breaks down is in very noisy environments with incomplete telemetry, because the graph may reflect source-system gaps rather than analyst behaviour.

When does a knowledge graph stop being useful and start adding friction?

Tighter investigation structure often increases maintenance overhead, so teams have to balance analytical speed against ingestion and curation cost. A graph becomes friction when it is too stale, too broad, or too semantically weak to support a trustworthy next step. That usually shows up when analysts must verify every edge manually, when important relationships are missing, or when the graph is packed with low-signal joins that make real pivots harder to see.

There is no universal standard for a “good” investigative graph, so teams should treat improvement as a local operational question. In one environment, the benefit may be fewer duplicate queries. In another, it may be better case handoff because the same graph path is visible to every analyst. The core issue is whether the graph reduces uncertainty at decision time. If it does not, the graph is a catalog, not an investigation aid.

That distinction matters most when machine identities are part of the story, because the relationship between a workload, its credentials, and its effective privilege often changes faster than the supporting inventory. Teams that only optimise for completeness often miss the operational question of whether the graph is helping an analyst prove or disprove a lead before the window closes.

Related resources from NHI Mgmt Group

• How can security teams know whether passkey adoption is actually improving security?
• How do teams know whether external MFA is actually improving security?
• How do teams know whether cross-cloud federation is actually improving governance?
• How do security teams know whether connector coverage is actually improving governance?