A Vendor-Neutral Event Architecture for Digital Investigation Governance

Digital-investigation architecture can expose operational events for process and governance analysis while authoritative evidence remains in its designated environment. The proposed pattern separates the investigation record from a minimum-necessary analytical projection of activities, relationships, timestamps, responsibilities, outcomes, and control references.

Process mining, event-log analysis, CASE/UCO semantics, and object-centric event models provide an external technical basis for the components. Their combination into an operational-event projection for investigation governance is DUNNIXER synthesis, not an established forensic standard.

Investigation platforms and process-analysis platforms solve complementary problems. Investigation systems support evidence acquisition, preservation, examination, analysis, case activity, and collaboration. Process-analysis and governance capabilities can reconstruct recorded execution, compare it with explicit models, and connect activities to responsibilities, controls, performance measures, exceptions, and corrective actions.

Consider an employee data-exfiltration investigation. A governance layer may need to know that a device acquisition took six hours, involved a defined evidence object, and was approved by an authorized role. Those questions can often be evaluated without receiving the acquired image or its contents. The architecture must still prove that source events exist, identifiers correlate reliably, timestamps are trustworthy, and every projected field has an approved purpose.

DUNNIXER proposes four functional roles assembled from independently evidenced technical components. The roles define responsibilities and boundaries, not a prescribed product stack.

Reliable assurance remains conditional. Event coverage, data quality, control design, model quality, and operational validation determine whether the analytical outputs can support timely management review.

Where legal, security, privacy, or evidential requirements favor retaining forensic content in a designated environment, the architecture begins by asking whether a smaller operational projection can answer the governance question.

Candidate fields include event identifiers, timestamps, evidence references, status changes, roles, organizational units, processing states, approval events, custody events, exceptions, and handoffs. Identifiers, roles, timestamps, and exception descriptions can themselves reveal personal, privileged, or evidential information. A “metadata-only” label is therefore insufficient.

The boundary must be approved field by field against purpose, necessity, sensitivity, linkage risk, retention, and access. Each projected record should also preserve source provenance, extraction method, transformation history, and the applicable schema or rule version.

Process mining is the established technical term for using event data to discover, compare, and analyze recorded process execution. DUNNIXER uses process intelligence as umbrella terminology for that analysis together with the responsibilities, controls, risks, decisions, performance measures, exceptions, and corrective actions needed for governance.

Recent peer-reviewed research has applied process-mining techniques to digital-forensic event reconstruction. That provides a narrow technical bridge; it does not validate end-to-end investigation-governance monitoring. The OCEL 2.0 specification addresses another relevant problem by allowing an event to relate to several objects. An acquisition event may concern an investigation, multiple evidence objects, an actor or role, and a control at the same time.

A discovered sequence shows what the available records describe. The surrounding process and control model provides the expectations and decision context required for human interpretation. A deviation may reflect a legitimate variation, incomplete data, a deficient model, or a material exception; it is not automatically a forensic failure or legal non-compliance.

The Cyber-investigation Analysis Standard Expression is a community-developed, versioned ontology and specification extending the Unified Cyber Ontology. CASE can provide a semantic basis for representing and exchanging investigative actions, cyber-domain objects, provenance, custody, tools, and relationships. The current stable versions identified during this review are CASE 1.5.0 and UCO 1.5.0.

CASE/UCO does not guarantee interoperability across products and is not itself a process-mining event-log format. A concrete implementation must establish source-system coverage, mapping effort, information markings, access constraints, and the analytical purpose.

On the event-analysis side, IEEE 1849-2023 XES establishes a standard for interoperability in event logs and event streams. OCEL 2.0 is a complementary candidate for object-centric event logs. OCEL is not a forensic standard, so mapping investigation semantics into its event/object representation remains architecture work.

BPMN, CMMN, and DMN are possible modeling candidates for process flow, case-oriented work, and decision logic. An implementation may use one, several, or none of them.

A role-based product example makes the architecture concrete. Magnet One could represent investigation and evidence operations, Cribl Stream could represent event transformation and routing, and ARIS Process Mining and governance capabilities could represent process analysis and governance.

Public documentation supports these individual roles. Magnet One is an investigation, case, evidence, and collaboration environment. Magnet describes Magnet One Connect as a secure, managed integration mechanism for approved partners. Cribl Stream can filter, transform, mask, and route structured event data. ARIS Process Mining can ingest event data and perform process analysis and configured conformance checking.

Public documentation does not establish a Magnet-to-Cribl connector, a packaged Cribl-to-ARIS Process Mining connector, a Magnet-to-ARIS integration, CASE/UCO or OCEL support across the combination, or a deployed three-product solution. It also does not establish that Magnet One Connect can expose the minimum operational fields required here without evidential content. The mapping is technically plausible from separately documented capabilities, but it requires direct interface, security, licensing, residency, and deployment validation.

The DUNNIXER Minimal Investigation Event Model uses eight conceptual elements: Case, Evidence, Actor, Timestamp, Activity, Control, Outcome, and Exception. It is a proposed analytical model, not a forensic standard or validated vendor schema.

The relationships matter as much as the fields. An activity is performed by an actor or role, may concern one or more evidence objects, may satisfy or be assessed against a control, occurs at a timestamp, produces an outcome, and may expose an exception. The case provides the governing investigation context.

That structure allows an acquisition event and a later custody exception to appear as connected governance observations. It does not convert the operational record into evidential fact or determine the significance of the exception.

The architecture is viable only when the participating systems and governance model can support a dependable analytical projection.

• Source-event availability: participating systems expose the activities and state changes required for the approved use case
• Stable correlation: cases, evidence objects, activities, actors, controls, and outcomes can be linked without ambiguous joins
• Semantic normalization: source events can be mapped into governed meanings without erasing material distinctions
• Timestamp quality: clock sources, time zones, event ordering, and corrections are understood
• Provenance: projected records retain source, extraction method, transformation history, and schema or rule version
• Privacy and access: each field has an approved purpose, sensitivity classification, retention rule, and access boundary
• Interpretation boundary: analysts distinguish recorded events from evidential facts and process deviations from non-compliance

• Governing the Digital Investigation Lifecycle Across Systems and Teams — the series governance and lifecycle foundation
• From UAE and Dubai Cybersecurity Controls to Operational Investigation Evidence — applied examples of connecting selected controls to evidence of execution
• Human Authority in AI-Assisted Digital Investigation Governance — the bounded human-authority extension for AI consuming governed operational context
• Authoritative State Must Live Outside the Model Context — an analogous separation between authoritative records and a temporary analytical or reasoning view
• Normalized Entities and Context Slices Beyond Chunk Retrieval — a related treatment of normalized entities and governed projections
• Browse Architectural Insights for the parent collection

• CASE Community — Cyber-investigation Analysis Standard Expression
• CASE Community — CASE 1.5.0 Release
• NIST — Advancing Coordinated Cyber-investigations and Tool Interoperability
• IEEE Standards Association — IEEE 1849-2023 XES
• OCEL Community — OCEL 2.0 Specification
• Adila, Studiawan, and Breitinger — Forensic Event Reconstruction With Process Mining
• UAE Legislation — Federal Decree-Law No. 45 of 2021 Concerning the Protection of Personal Data