A security data model that connects related signals across SaaS environments so teams can see how identities, integrations, activity, and access patterns relate. In practice, it helps transform isolated telemetry into usable context for triage, investigation, and remediation decisions.
Expanded Definition
An Obsidian knowledge graph is a security data model for connecting SaaS signals into a relationship map, so identities, integrations, permissions, and activity can be analysed together rather than as separate alerts. The value is not the graph software itself, but the way it turns fragmented telemetry into context for NHI and access review decisions.
In NHI security, the term is used to describe a correlation layer that helps answer questions such as which API key belongs to which workload, which service account touched a sensitive dataset, and whether a token, integration, or automation path has drifted from expected behaviour. Definitions vary across vendors because some products emphasise entity resolution, while others focus on graph analytics or investigation workflows. NHI Management Group treats the concept as a governance aid, not a substitute for source-of-truth inventory or access control. For control mapping, this complements guidance in NIST SP 800-53 Rev 5 Security and Privacy Controls and identity relationship modeling used in Zero Trust programs.
The most common misapplication is treating a visual graph as proof of identity governance, which occurs when teams assume connected data automatically means the underlying entitlements are current and accurate.
Examples and Use Cases
Implementing an Obsidian Knowledge Graph rigorously often introduces data normalization overhead, requiring organisations to weigh faster investigation speed against the cost of maintaining clean entity resolution.
- Linking a CI/CD service account, its cloud role, and the deployments it triggered so analysts can trace risky automation paths during incident response.
- Connecting SaaS audit logs with secret usage events to identify whether a leaked token was actually exercised before revocation.
- Mapping integrations between ticketing, source control, and data warehouse systems to expose where a single NHI can reach multiple high-value systems.
- Correlating file access, admin consent, and OAuth app behaviour to show whether access changes match the approved business workflow.
- Using the Ultimate Guide to NHIs alongside graph-based investigation to prioritise rotation, offboarding, and privilege review for exposed credentials.
These use cases are most effective when the graph is fed by authoritative sources and cross-checked against policy definitions from NIST SP 800-53 Rev 5 Security and Privacy Controls, rather than relying only on observed relationships.
Why It Matters in NHI Security
Obsidian Knowledge Graphs matter because NHI risk often hides in relationships, not in isolated events. A service account may look benign in one log source, while the graph reveals it is tied to a production database, a dormant automation path, and a long-lived secret stored outside a vault. That context is essential when organisations need to decide whether a token is merely stale or actively dangerous. The NHIMG data shows why this matters operationally: Ultimate Guide to NHIs reports that 97% of NHIs carry excessive privileges, which means relationship visibility directly affects blast-radius assessment and remediation priority.
Without a graph view, teams tend to underestimate exposure, miss lateral movement paths, and delay containment because each signal appears unrelated. With it, investigators can identify which integration should be disabled first, which secrets need rotation, and which access edges violate least privilege or Zero Trust expectations. This is especially important when SaaS sprawl creates overlapping ownership between platform teams, application owners, and security operations. Organisations typically encounter the urgency of an Obsidian Knowledge Graph only after a credential compromise or suspicious automation event, at which point relationship visibility becomes operationally unavoidable to address.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Non-Human Identity Top 10 and OWASP Agentic AI Top 10 address the attack and risk surface, while NIST CSF 2.0, NIST Zero Trust (SP 800-207) and NIST SP 800-63 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| OWASP Non-Human Identity Top 10 | NHI-01 | Graphing identities and integrations supports visibility, inventory, and relationship-risk controls for NHIs. |
| NIST CSF 2.0 | DE.CM-8 | Entity relationships improve continuous monitoring and detection across SaaS activity sources. |
| NIST Zero Trust (SP 800-207) | SC.SD | Zero Trust depends on knowing trust relationships and access paths across systems. |
| NIST SP 800-63 | AAL2 | Identity assurance concepts help validate that linked accounts and credentials are attributed correctly. |
| OWASP Agentic AI Top 10 | AGENT-03 | Agentic systems need traceable tool use and relationship context to constrain unsafe actions. |
Correlate SaaS telemetry into one view so anomalous NHI behavior can be detected and investigated faster.
Related resources from NHI Mgmt Group
- What is the difference between a SaaS knowledge graph and a SIEM?
- How should teams use multi-hop relationships in a knowledge graph for governance decisions?
- What should teams check before publishing derived relationships in a knowledge graph?
- How should organisations decide between a semantic layer, an ontology, and a knowledge graph in AI data architecture?
