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# Tech Tip - Tracing Hidden Risk: How a Transitive-Risk Knowledge Graph Catches What Sanctions Screening Misses

**[Agentic AI Context Builders](https://daily.dev/sources/agentic_ai_context_builders)** · [@markmilinkovich](https://daily.dev/markmilinkovich) · 2 min read · 1 upvotes · 0 comments

## Summary

Sanctions screening against flat lists like OFAC's SDN misses transitive risk through ownership and control relationships. Sentries, a risk-intelligence system built on ArangoDB, models entity relationships as a graph and computes how risk propagates hop by hop. ArangoDB's multi-model capability stores entity records, relationship edges, and ontology rules in a single system, enabling a single traversal to join all three without external glue code. The result is a five-step analyst toolset (search, trace, vet, concentrate, visualize) usable by human analysts, auditors, and AI agents against the same stored queries.

## Content

Sanctions screening is a solved problem — until it isn’t.

Every bank, defense contractor, and fintech operating in the United States runs counterparty data against the OFAC Specially Designated Nationals (SDN) list: roughly 18,500 individuals, organizations, and entities that U.S. persons are prohibited from transacting with. Compliance teams have spent years tuning these pipelines. They work well for the entities that appear on the list.

The failure mode is everything else.

**What Sentries does**

Sentries is a working risk-intelligence system built on ArangoDB. Rather than screening entity names against a flat list, it models the ownership, control, and family relationships between entities as a graph — then computes how risk propagates along those relationships, hop by hop.

ArangoDB is a multi-model database that stores documents, a property graph, and a semantic schema (ontology) in a single system, all queryable together. That matters here because the three ingredients of a transitive-risk calculation — entity records, relationship edges, and the rules governing how risk travels across them — normally live in three separate systems. Sentries keeps them in one query context, which means a single traversal can join all three without external application code holding the pieces together.

The result is a five-step analyst toolset: **search → trace → vet → concentrate → visualize**. An analyst, an auditor, and an AI agent all call the same stored queries against the same data — there is no divergence between what the human sees and what the system decided.

Read more: [https://arango.ai/blog/tracing-hidden-risk-how-a-transitive-risk-knowledge-graph-catches-what-sanctions-screening-misses/](https://arango.ai/blog/tracing-hidden-risk-how-a-transitive-risk-knowledge-graph-catches-what-sanctions-screening-misses/)

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---

Tags: [#architecture](https://daily.dev/tags/architecture), [#fintech](https://daily.dev/tags/fintech), [#compliance](https://daily.dev/tags/compliance), [#arangodb](https://daily.dev/tags/arangodb)

[View this post on daily.dev](https://daily.dev/posts/tech-tip---tracing-hidden-risk-how-a-transitive-risk-knowledge-graph-catches-what-sanctions-screeni-rcy30hkdl)

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