Context and Challenge

A large public-sector cybersecurity and intelligence unit supporting multiple EU agencies relied on structured reporting to move threat intelligence from analysts to procurement and compliance teams. The unit had mature analytic workflows, but the final step—exporting intelligence in a form procurement systems could consume—was inconsistent and slow.

Analysts produced detailed assessments tagged with the DISARM framework to describe influence and manipulation tactics, techniques, and procedures. Those tags were valuable for operational understanding, yet procurement teams needed the same information translated into vendor-risk language: requirements, controls, contractual clauses, and audit-ready documentation. The gap between analytic structure and procurement structure created recurring friction:

• Misalignment of taxonomies: DISARM tags were meaningful to analysts, while procurement platforms expected categories aligned to risk domains, control families, and procurement checklists.
• Manual rework: Reports were exported as documents, then re-entered or summarized into procurement tools by separate staff, introducing delays and errors.
• Inconsistent evidence packaging: Procurement decisions required traceability—what the observed behavior was, how it mapped to known patterns, and which mitigations were recommended—yet evidence often lived in narrative prose.
• Cross-agency variability: Different EU agencies used different procurement workflows and documentation formats, complicating standardization.
• Governance and confidentiality constraints: Intelligence handling rules limited how much raw data could be embedded in procurement records, requiring careful redaction and access controls.

The overall problem was not lack of intelligence. It was lack of interoperability: turning structured analytic outputs into structured procurement inputs without losing meaning, traceability, or governance.

Approach and Solution

The unit implemented a structured export pipeline designed to preserve DISARM-tagged intelligence while producing procurement-ready artifacts. The solution focused on data modeling, automation, and governance, rather than changing analyst behavior.

1) Unified Data Model Bridging DISARM and Procurement Needs

A canonical schema was defined to represent each report as a set of machine-readable objects. The schema kept DISARM tags intact but added fields procurement systems could act on:

• Observed activity summary: a normalized description of the behavior and context (who/what/when/where), written to be reusable outside an intelligence platform.
• DISARM mappings: tactic/technique identifiers, confidence level, and analytic notes.
• Risk statement: a concise formulation of impact and likelihood assumptions, suitable for procurement review boards.
• Control implications: a mapping layer translating DISARM techniques into procurement-relevant domains such as content moderation requirements, supplier monitoring obligations, incident response expectations, and transparency reporting.
• Evidence packaging: references to underlying sources stored in controlled repositories, with redaction markers and handling caveats.

This model ensured the same report could satisfy two audiences: analysts needed fidelity; procurement needed structure.

2) Structured Report Templates with Enforced Fields

To reduce variance, analysts used structured templates that required key elements before a report could be exported:

• Minimum required DISARM tags (or explicit “not applicable”)
• Confidence and rationale
• Recommended mitigations
• Handling instructions (what can be shared, with whom, and at what classification level)

The goal was not to constrain analysis, but to ensure that every report contained the consistent building blocks required for downstream systems.

3) Export Formats Designed for Procurement Workflows

Instead of exporting only human-readable documents, the pipeline generated multiple outputs from the same canonical object:

• Procurement system ingest package: a machine-readable export aligned to typical procurement intake forms (risk category, requirement statements, recommended clauses, review timelines).
• Audit-friendly summary: a structured narrative with clear traceability to tags and evidence, designed for internal review and oversight.
• Redacted vendor-facing excerpt: a version that could be attached to procurement communications without disclosing sensitive collection details.

Each export type was generated automatically, ensuring consistency across agencies and reducing duplication of effort.

4) Rules Engine: From DISARM Tags to Procurement Requirements

A rules engine was introduced to translate DISARM tagging into standardized procurement actions. For example:

• When certain amplification or impersonation techniques were present, the export included supplier obligations around detection capabilities, escalation paths, and reporting cadence.
• When coordination or infrastructure-related techniques were identified, the export suggested due diligence checks and monitoring requirements aligned to the procurement cycle.
• When high-impact targeting patterns were tagged, the export recommended service-level expectations for incident handling and response timelines.

The rules were designed to be configurable to accommodate differences among EU agencies’ procurement frameworks without rewriting the underlying intelligence.

5) Governance: Access Control, Redaction, and Traceability

Because procurement records often circulate broadly, governance was treated as a first-class feature:

• Field-level redaction ensured sensitive source descriptions remained in restricted systems, while procurement tools received only what they were permitted to store.
• Immutable report identifiers provided traceability without exposing raw intelligence.
• Change logs tracked how an intelligence report evolved and which procurement artifacts were generated from which version.
• Role-based access separated analyst-only notes from procurement-facing summaries.

This allowed compliance teams to demonstrate appropriate handling while still enabling procurement action.

Results

The export pipeline changed the relationship between intelligence production and procurement execution. Outcomes were visible across operations, governance, and cross-agency consistency.

Faster Procurement-Grade Outputs (Approximate)

By automating translation and packaging, procurement teams received standardized intelligence-derived requirements significantly sooner than before. Time savings were reported as meaningful and repeatable, especially for recurring categories of threats where rules mappings were already well-tuned. While exact measurements varied by agency and workflow, stakeholders described the shift as moving from “manual re-entry” to “review and approve.”

Improved Consistency Across Agencies

The canonical schema and templates reduced the drift that previously occurred when different teams summarized intelligence in different styles. DISARM tags remained consistent, and procurement systems received normalized fields that supported comparable decisions across separate procurement pipelines.

Better Auditability and Decision Traceability

Procurement decisions could be linked back to:

• the underlying DISARM-tagged assessments,
• documented confidence levels and assumptions,
• and standardized requirement mappings.

This strengthened internal oversight and reduced the back-and-forth typically needed to justify why a particular requirement or contractual clause had been included.

Reduced Risk of Oversharing or Undersharing

The redaction and role-based export strategy decreased two common failure modes:

• Oversharing: sensitive evidence accidentally copied into procurement records.
• Undersharing: procurement teams receiving vague summaries that didn’t support enforceable requirements.

Procurement reviewers consistently received the “actionable minimum” plus traceability hooks, while sensitive materials stayed in restricted repositories.

More Actionable Supplier Requirements

Instead of generic language, procurement outputs increasingly contained testable requirements derived from observed techniques. This helped shift discussions from abstract threat descriptions to concrete obligations: monitoring, reporting, escalation, and verification.

Key Takeaways

• DISARM tagging becomes more powerful when paired with a translation layer. Analysts need DISARM for structured understanding; procurement needs structured requirements. A rules-based mapping bridges the two without diluting analytic rigor.
• A canonical schema prevents downstream fragmentation. When every report is an object with consistent fields, exports can adapt to different procurement systems while keeping one source of truth.
• Templates drive consistency without constraining insight. Enforced minimum fields improve reusability and audit readiness while leaving room for narrative context.
• Governance must be built into the export pipeline. Redaction, access control, and immutable identifiers are essential when intelligence flows into systems designed for broader operational use.
• Procurement becomes faster when teams shift from retyping to reviewing. Automating packaging and requirement generation reduces manual handling and enables procurement teams to focus on decision quality rather than data transformation.

Structured intelligence can inform procurement at scale, but only when the last mile—exporting, translating, and governing DISARM-tagged reports—is treated as an engineering and process design problem, not a documentation task.