Link analysis is exposing the limits of legacy SIEM correlation

At a glance

What this is: This is Gurucul's analysis of why identity-centred link analysis is needed to turn fragmented security telemetry into contextual UEBA and investigation evidence.

Why it matters: It matters because IAM, NHI, and security teams need correlation models that preserve identity, ownership, and context across users, service accounts, API tokens, and AI-era entities.

👉 Read Gurucul's analysis of link analysis, identity context, and UEBA

Context

Link analysis is the process of connecting events, identities, and assets so analysts can understand behaviour instead of staring at isolated records. In this article, Gurucul argues that legacy SIEM correlation fails because it does not preserve the identity context needed to explain who did what across cloud, endpoint, application, and identity systems.

That gap matters for IAM and NHI governance because service accounts, API tokens, device IDs, and GenAI-era identities rarely share a clean one-to-one mapping. When correlation stops at surface fields, security teams lose the ability to trace privilege, ownership, and accountability across the full identity chain.

Key questions

Q: How should security teams improve correlation across identity, endpoint, and cloud telemetry?

A: Security teams should normalise logs into a shared identity model before they rely on correlation rules. That means resolving usernames, device IDs, tokens, and cloud-native identifiers into durable entities, then layering business context and enrichment on top. Without that foundation, investigators will keep pivoting manually between tools instead of following a coherent activity chain.

Q: Why do service accounts and API tokens complicate SIEM investigations?

A: They complicate investigations because they often lack the stable ownership and naming assumptions that work for human users. A token may represent delegated access, automation, or a workload, and each appears differently across tools. If correlation does not preserve entity continuity, the same actor is treated as multiple unrelated records, which hides risk and weakens accountability.

Q: What do security teams get wrong about UEBA in dynamic environments?

A: They often treat UEBA as a scoring problem instead of a context problem. Behavioural models only become reliable when they are built from linked identity, peer-group, and historical data. If baselines are derived from fragmented logs, the system will over-alert on harmless variation and under-detect activity that is unusual in the relevant business context.

Q: How can organisations tell whether their correlation model is working?

A: A correlation model is working when analysts can move from alert to evidence chain without manual field-matching across tools. The clearest signal is reduced investigation time with higher-confidence conclusions, especially when the same identity can be traced through identity systems, endpoints, cloud logs, and application events. If that continuity is missing, the model is only producing partial joins.

Technical breakdown

Why event-centric SIEM correlation misses identity context

Event-centric correlation treats logs as isolated records and joins them only on obvious fields such as username, hostname, or IP address. That approach breaks down in dynamic environments where one actor may appear under multiple identifiers across AD, cloud IAM, EDR, and application logs. Link analysis instead builds a graph of entities and relationships so the same activity can be followed across systems, even when native identifiers differ. For identity security, the technical point is not volume. It is continuity: without continuity, analysts cannot reconstruct behaviour, detect drift, or assign accountability accurately.

Practical implication: map every telemetry source to a durable identity model before relying on correlation for detection or investigation.

How enriched entity graphs improve UEBA and investigation speed

UEBA becomes more accurate when behavioural baselines are built from linked identity, role, peer-group, and historical context rather than raw alert counts. Gurucul describes a multi-hop model in which feeds, lookups, and API enrichment resolve one entity into another, such as a device ID into a hostname and then into cross-domain activity. That matters because many incidents are not visible in a single log source. The technical value is chain reconstruction: activity before, during, and after an anomaly becomes visible as a sequence, not a set of disconnected alerts.

Practical implication: enrich identity and device records before tuning UEBA rules or you will optimise for noise instead of risk.

Why GenAI identities and machine accounts break shallow correlation

The article points to GenAI-driven identities and machine accounts as a complicating factor because these actors do not always follow the same ownership and naming assumptions as human users. A shallow correlation layer may see access tokens, service accounts, or automated workloads as separate signals rather than manifestations of one operating identity. That creates false separations in the graph and hides privilege paths, lateral movement, or delegated execution. In practical terms, modern correlation must handle identity diversity, not just identity volume, or it will miss the true attack path.

Practical implication: include service accounts, API tokens, and AI-era entities in your identity graph design, not just human logins.

NHI Mgmt Group analysis

Identity correlation is now a governance problem, not just a SIEM feature. The article correctly frames link analysis as the difference between seeing logs and understanding behaviour. For IAM and NHI programmes, the real issue is whether every entity can be traced across systems with enough continuity to preserve accountability. That makes correlation architecture part of governance, not just analytics, and it should be treated as such in programme design.

Shallow correlation creates false identity boundaries that attackers can exploit. When usernames, device IDs, tokens, and cloud-native identifiers are not normalised into a single entity model, analysts inherit gaps that hide cross-system movement. This is especially damaging where machine identities and delegated access are involved, because the same actor may appear under different labels in different tools. Practitioners should assume that fragmented identity views produce fragmented security decisions.

Context-rich link analysis improves UEBA only when identity ownership is explicit. Behavioural baselines become more trustworthy when the platform knows not only what happened, but whose activity it was, what role it mapped to, and what business unit it touched. That is a direct fit for OWASP-NHI and NIST-CSF thinking around identity visibility and monitoring. The practitioner conclusion is simple: without ownership, behavioural analytics remain descriptive rather than defensible.

Chain analysis is the closest thing SIEM has to a narrative engine. Stitching events into a sequence is more valuable than stacking alerts because it shows how an investigation evolves over time. That matters for insider threat, account compromise, and delegated machine activity alike, where the path matters as much as the endpoint. The implication is that SOC teams should prioritise evidence chains that can survive audit, not just detections that trigger quickly.

Context-aware telemetry is becoming essential as GenAI identities and machine accounts proliferate. The article signals a broader shift in enterprise identity: more non-human actors, more cross-platform movement, and less tolerance for static rule sets. Legacy correlation will not keep pace if it assumes one identity equals one stable user. Practitioners need identity graphs that can absorb new entity types without weakening detection fidelity.

From our research:

• From our research: Only 5.7% of organisations have full visibility into their service accounts, according to the Ultimate Guide to NHIs.
• Another finding from our research shows that 97% of NHIs carry excessive privileges, according to the Ultimate Guide to NHIs.
• For the governance side of this problem, the Ultimate Guide to NHIs explains why visibility, rotation, and offboarding must be tied to identity lifecycle controls.

What this signals

Identity graph coverage is becoming a detection quality issue. When only 5.7% of organisations have full visibility into their service accounts, contextual correlation is no longer a nice-to-have. It becomes the difference between a traceable event chain and a pile of disconnected alerts. Teams should expect correlation platforms to prove entity continuity across human and non-human identities, not just ingest more telemetry.

The next maturity step is to treat machine identities as part of the same governance surface as users, because the investigative model cannot be split cleanly in practice. That means joining access, ownership, and behavioural context before relying on UEBA, and using a framework like the NIST Cybersecurity Framework 2.0 to anchor monitoring, response, and recovery expectations.

Identity context is the new control plane for SOC analytics. As AI-era entities and service accounts proliferate, the organisations that can resolve who or what acted, and why it mattered, will move faster in triage and defend decisions more convincingly.

For practitioners

• Build an identity graph before you tune detections Normalize users, service accounts, tokens, devices, and cloud-native IDs into a single entity model so investigations can follow activity across systems without manual pivots.
• Enrich telemetry with business context Add role, department, title, and business unit data to security events so analysts can judge whether behaviour is unusual in operational terms, not only in technical terms.
• Stitch events into evidence chains Group related activity into before, during, and after sequences so SOC teams can reconstruct attack paths, support casework, and reduce alert fragmentation.
• Treat machine identities as first-class correlation objects Include API tokens, service accounts, and automated workload identities in the same investigative model as human logins to avoid blind spots in hybrid environments.

Key takeaways

• Legacy correlation breaks down when identity is fragmented across logs, tools, and systems.
• Context-rich link analysis improves UEBA only when it preserves durable entity continuity and ownership.
• Security teams should build identity graphs and enrichment layers before expecting faster, defensible investigations.

Key terms

• Link Analysis: Link analysis is the practice of connecting events, entities, and relationships so investigators can see behaviour in context. In security operations, it turns isolated logs into a traceable sequence that reveals who or what acted, how systems are connected, and why an activity matters.
• Entity Graph: An entity graph is a structured model of identities, devices, applications, and relationships across a security environment. It lets teams resolve different identifiers to the same actor, preserving continuity across systems so investigations can follow activity without manual field matching.
• UEBA: User and entity behaviour analytics is a detection approach that compares activity against expected patterns for people and machine identities. It becomes more reliable when those patterns are built from identity context, role, history, and peer behaviour rather than from raw event counts alone.
• Chain Analysis: Chain analysis is the process of stitching related events into a sequence that shows how an activity unfolded over time. In practice, it helps analysts move from alert triage to evidence-based investigation by linking what happened before, during, and after an anomaly.

What's in the full article

Gurucul's full blog post covers the operational detail this post intentionally leaves for the source:

• The platform's step-by-step example of resolving one identifier into another across endpoint, cloud, and identity systems
• The specific behavioural baseline inputs Gurucul says it uses for UEBA and chain analysis
• The investigation workflow details behind the 'Investigate' search experience and contextual attributes
• The business-impact framing Gurucul uses to connect detection speed with SOC efficiency

👉 Gurucul's full post shows how chain analysis, enrichment, and identity timelines are operationalized.

Deepen your knowledge

NHI governance, agentic AI identity, and machine identity security are core topics in our NHI Foundation Level course, the industry's only accredited NHI security programme. If you are responsible for identity security strategy or programme maturity, it is worth exploring.