Abstract
Since the entry into force of the U.S. Uyghur Forced Labor Prevention Act (UFLPA), U.S. customs enforcement has increasingly relied on data-intensive methods to identify, prioritize, and investigate suspected forced-labor risk in global supply chains. This evolution is often described in operational terms—more holds, more documentation requests, more scrutiny of upstream tiers—but it also reflects a deeper institutional change: the gradual construction of a technology-enabled “enforcement stack” combining government workflows with commercial datasets, graph analytics, AI-assisted network inference, and behavioral anomaly detection. This report offers an analytical synthesis of that stack as it is commonly described in public communications and procurement-related accounts, focusing on five functional components: foundational corporate identity and business intelligence (e.g., Dun & Bradstreet), entity-level risk and relationship graphing (Kharon), network-level supply chain inference (Altana), dynamic behavioral monitoring and transshipment detection (Exiger), and the process/governance layer provided by systems integrators and consultancies (e.g., Deloitte). The central argument is that UFLPA-driven enforcement has moved—at least in part—from “case-triggered” to “system-triggered” decision-making, where structured data and models increasingly shape what investigators see, what they ask for, and how evidentiary narratives are assembled. For firms, the implication is not only compliance in substance but demonstrability in structure: the capacity to produce machine-readable, auditable, and explainable evidence chains becomes a strategic requirement in high-risk sectors.
1. Introduction: UFLPA and the modernization of enforcement
The UFLPA changed the compliance landscape by placing exceptional weight on proof architecture. In practical terms, firms face a demanding standard: to clear shipments or rebut suspicion, they must provide coherent, verifiable documentation sufficient to contest an adverse inference and demonstrate that goods are not produced with forced labor. Whatever one’s normative view of this legal design, its administrative consequence is straightforward: the constraint shifts from “finding wrongdoing in a world of limited visibility” to “evaluating evidentiary completeness in complex, multi-tier supply chains.” The operational burden associated with upstream mapping, transaction documentation, and traceability becomes central to the enforcement process.
At the same time, U.S. enforcement agencies confront their own constraints: scale, time, and information asymmetry. Global trade volumes are too large for purely manual review, and supply chains are too multiplex for single-source verification. This creates incentives for tools that can (i) reconcile entities across languages and jurisdictions, (ii) aggregate and structure disparate public and proprietary information, (iii) infer supply-chain relationships beyond the importer’s self-disclosure, and (iv) detect patterns consistent with evasion strategies such as transshipment or origin misdeclaration. In this context, technology procurement and public-private information infrastructures become not peripheral but operationally constitutive of enforcement capacity.
This report therefore treats “technology” not as an accessory but as a component of governance, shaping how risk is operationalized, how suspicion is triggered, and how evidence is requested and interpreted.
2. Scope, assumptions, and analytical approach
The aim here is conceptual and institutional rather than forensic. The report reconstructs an enforcement “stack” as a functional architecture: what types of tools are used, what problems they solve within the enforcement workflow, and how they interlock across layers. The discussion is grounded in the contour of claims typically associated with UFLPA-era modernization—outsourcing of analytic discovery to commercial platforms, increased reliance on structured data and graph representations, the adoption of risk scoring and network inference, and the integration of outputs into standardized workflows.
Two limitations deserve emphasis. First, commercial platforms referenced in public discourse provide only partial transparency into datasets and models; claims about specific algorithmic methods should be understood as indicative rather than audited. Second, procurement and public statements can illuminate intended use and capability framing, but they do not necessarily reveal day-to-day operational dependence or performance. Accordingly, the report uses cautious language—“reported,” “described,” “positioned,” “used to support”—when attributing functions to specific vendors.
3. Defining a “data-driven enforcement stack”
In this report, a data-driven enforcement stack refers to an integrated socio-technical system in which enforcement decisions are enabled by interoperable data sources and analytic tools, and stabilized through standardized processes that convert analytic signals into administrative actions. The stack is not a single platform; it is an arrangement of services and workflows that collectively answer five recurring questions in forced-labor and trade enforcement:
- Who is the entity in question, and is it a real, stable operating firm or an ephemeral intermediary?
- Is the entity proximally connected to known high-risk geographies, firms, industrial parks, or governance-linked networks?
- Where does the entity sit in the true supply chain, including upstream tiers the importer may not disclose or fully know?
- Do shipments and trading patterns display indicators consistent with evasion tactics such as transshipment, laundering of origin, or sudden intermediary insertion?
- How are these signals translated into consistent, auditable workflows—screening, holds, requests for information, escalation, and case management—across offices and agencies?
Organizing tools by these questions yields a four-layer structure plus a governance layer. This is a functional decomposition, not a claim about formal organizational design, but it helps clarify why different vendors can coexist without being redundant.
4. Layer 1—Foundational corporate identity and baseline intelligence (e.g., Dun & Bradstreet)
The first layer addresses a deceptively simple problem: entity resolution and baseline credibility. In global trade, the same corporate actor may appear under multiple translated names, abbreviated variants, or local registrations. Conversely, different firms may share similar names, and intermediaries may be created and dissolved rapidly. For enforcement, misidentifying entities can produce both false positives (wrongly linking a firm to a high-risk network) and false negatives (missing a genuine connection due to naming or registry opacity).
Commercial corporate intelligence systems—often exemplified by Dun & Bradstreet—are commonly used to stabilize identity attributes: legal names, addresses, corporate registration details, ownership signals where available, line-of-business descriptors, and indicators of corporate continuity. Within an enforcement stack, such systems are not usually “risk engines” on their own; rather, they supply the identity scaffolding that makes downstream graphing and inference more reliable. They can also help evaluate whether an entity behaves like an operating firm (e.g., consistent location and management footprint) or resembles a shell intermediary (e.g., anomalous formation timing, thin corporate footprint, frequent address or officer changes).
This layer matters because UFLPA-related scrutiny often hinges on supply-chain assertions. If an importer claims sourcing from a specific upstream manufacturer, enforcement needs confidence that the manufacturer is the intended one—not a similarly named entity—or that subsequent intermediaries are not being used to fragment visibility. Identity and baseline credibility data thus function as “ground truth anchors” for more complex network models.
5. Layer 2—Entity-level risk graphing and structured enforcement intelligence (Kharon)
The second layer focuses on proximity to risk. Platforms commonly discussed in this context—Kharon is frequently cited—are described as aggregating sanctions-related intelligence, corporate networks, ownership chains, senior executive ties, adverse media, and other public or semi-public records into structured, queryable graphs. While such capabilities originated in sanctions compliance and national security contexts, they can be adapted to forced-labor enforcement by constructing datasets and relationship models aligned to enforcement-relevant categories (e.g., entities associated with high-risk regions, industrial parks, or policy-linked labor programs).
The principal operational advantage of entity-level graphing is speed with explainability. Instead of relying on manual dossier-building, enforcement analysts can retrieve a structured narrative: who controls whom, which affiliates share directors, how a firm’s ownership connects to other nodes, and what public reporting or legal records suggest about relevant conduct or geography. When used as a screening tool, this layer acts as a first filter: it generates candidate entities that warrant scrutiny and provides a relational rationale for why they are suspicious or potentially connected to a broader high-risk universe.
Public descriptions often emphasize three features as particularly relevant in UFLPA-related contexts: multilingual data ingestion (including Chinese-language sources), industrial-park-level modeling (capturing risk at a location/cluster level rather than only at the firm level), and “piercing” of ownership or control chains. Together, these features support the core enforcement question at this layer: not merely whether an entity is on a list, but whether it is sufficiently connected to a risk ecosystem to justify deeper investigation.
6. Layer 3—Network-level supply chain inference and “governance by connectivity” (Altana)
Entity risk is necessary but insufficient in multi-tier supply chains. The enforcement challenge is often to connect the upstream risk node (where forced labor might occur) to downstream goods entering the market. Network-level platforms—Altana is commonly presented as a leading example—are described as creating a global supply chain knowledge graph by integrating trade and shipment data, customs-relevant information, logistics pathways, and corporate relationship signals. They are often portrayed as capable of mapping multiple tiers (“n-tier visibility”) and of inferring likely supply-chain links when a firm’s disclosures are incomplete or unreliable.
This layer’s distinctive contribution is the shift from entity scrutiny to relational scrutiny. Enforcement is no longer centered on “Is Company X risky?” but on “How does Company X connect, through transactions and production pathways, to risk nodes?” Network inference tools enable what might be called governance by connectivity: the capacity to identify plausible upstream linkages, concentrate investigative attention on the most consequential nodes, and generate structured hypotheses about how goods flow from origin to import.
Public accounts also suggest that UFLPA-related toolsets were deployed early in this vendor space, implying that forced-labor risk analysis was not an afterthought but a core use case. By 2025, descriptions of new contracting and media reporting often foreground broader trade security applications—such as identifying origin fraud or high-risk trade flows—yet these are conceptually adjacent to UFLPA enforcement. A platform designed to infer supply chains and flag anomalous connectivity patterns can be repurposed across multiple enforcement mandates. In practice, this means UFLPA-related risk inference can become embedded in a multifunctional trade intelligence platform rather than isolated as a single-issue tool.
For U.S. and EU readers accustomed to regulatory approaches that increasingly emphasize upstream transparency, the importance of this layer is intuitive: it institutionalizes the idea that compliance is evaluated not only at the final assembler but across the network. The method by which the network is reconstructed—data fusion and inference rather than pure self-disclosure—marks a structural change in how enforcement sees supply chains.
7. Layer 4—Dynamic behavioral analytics and transshipment detection (Exiger)
Forced-labor enforcement is a dynamic contest. As scrutiny increases, firms and intermediaries may attempt to reduce detectable exposure through re-routing, third-country processing, blending of inputs, re-labeling, or the insertion of new trading entities. This shifts the enforcement problem from static association to behavioral evolution: the question becomes whether trade patterns indicate evasion or laundering.
Platforms often discussed in this role—Exiger is frequently highlighted—are described as bringing methods associated with anti-money laundering and illicit network detection into supply-chain contexts. The emphasis is on behavioral pattern recognition, anomaly detection, and risk scoring over time: unusual route choices, abrupt changes in counterparties, improbable shifts in sourcing that do not match capacity or industry constraints, or the sudden appearance of intermediaries that fragment traceability.
In the stack architecture, this layer is complementary rather than duplicative. Entity-level graphs can show who is connected to whom; network inference can show how supply chains likely connect; dynamic detection focuses on how behaviors change in ways consistent with regulatory evasion. For UFLPA enforcement, this matters because the most contested cases often involve claims of third-country transformation or complex trading chains that obscure origin and upstream labor conditions. Behavioral tools can provide triggers for closer scrutiny or for targeted requests for documentation focused on specific segments of the route or on specific intermediary relationships.
Importantly, this layer also helps explain why enforcement modernization can scale. Static lists do not capture tactical adaptation well; behavioral models, while imperfect, can be designed to learn from newly observed evasion motifs. When linked to case outcomes and investigative feedback, anomaly detection becomes part of a learning loop rather than a one-off screen.
8. The governance and process layer: translating signals into decisions (consultancies and integrators)
Even high-quality data and models do not automatically produce enforceable, defensible administrative actions. Enforcement requires procedures: thresholds for escalation, standardized documentation requests, internal review mechanics, audit trails, and consistent decision rationales across staff and organizational units. This is where consultancies and systems integrators—Deloitte is frequently referenced in public procurement contexts—enter the stack.
Their contributions are often described in terms of governance frameworks, audit process design, workflow automation, and the development of standard operating procedures that translate analytic outputs into actionable case steps. Functionally, this layer turns “signals” into “decisions” by ensuring that analytic prompts yield consistent operational responses: what is requested from importers, how evidence is evaluated, and how determinations are documented. In a multi-agency environment, process engineering also plays a coordination role, aligning definitions, data models, and workflow handoffs so that information can be shared and reused without losing meaning.
This layer is sometimes overlooked because it does not look like enforcement on its face. Yet it is central to the institutionalization of technology-enabled enforcement. It determines whether reliance on commercial platforms remains ad hoc or becomes routinized into repeatable, trainable practice.
9. Inter-agency alignment: shared data models and the conditions of “scalable suspicion”
A defining feature of technology-enabled enforcement is that it incentivizes alignment across agencies and offices, not only in mission but in semantics. For information to be shared effectively, agencies need compatible representations of entities, relationships, risk categories, and evidentiary strength. Otherwise, “sharing” becomes the movement of context-poor fragments, and analytic work must be repeated rather than compounded.
From an institutional perspective, inter-agency alignment is the hidden infrastructure of scalability. It enables a shift from investigator-specific suspicion to system-wide suspicion: risk can be operationalized as a set of consistent triggers, and case narratives can be assembled from standardized components. This alignment also makes feedback loops feasible. Enforcement outcomes—holds resolved, evidence found insufficient, patterns validated or disproven—can be encoded to refine screening rules, update risk graphs, and recalibrate anomaly thresholds. Under an ideal design, this process improves consistency and targeting over time, although in practice it also raises questions about governance, accountability, and the management of model error.
10. From “case-triggered” to “system-triggered” enforcement: operational implications
Taken together, the stack supports a broader shift in enforcement logic. In a case-triggered model, attention is prompted by specific tips, complaints, or investigative leads. In a system-triggered model, attention is prompted by algorithmic and data-driven signals produced at scale across trade flows. This does not imply the elimination of human judgment; rather, it changes the distribution of judgment. Humans increasingly arbitrate among system-generated candidates, interpret model-provided rationales, and translate them into procedural actions and evidence requests.
This shift has three operational consequences.
First, the unit of enforcement analysis moves from the firm to the network and the shipment. Risk can be framed not only as an attribute of a supplier but as a property of a route, a set of counterparties, and a pattern of transactions. Second, evidentiary expectations become more granular and structured. Importers may be asked to address specific nodes and transitions—inputs, transformations, intermediaries, logistics stages—rather than provide general assurances. Third, compliance becomes time-sensitive. When screening is continuous and behavior is monitored, abrupt changes in supply-chain configuration may trigger scrutiny, requiring firms to produce immediate explanations and traceability materials that can withstand external data cross-checks.
11. Implications for firms and markets: compliance as “evidence engineering”
For firms, the strategic challenge is no longer limited to whether forced labor is present in fact; it is whether a firm can prove the absence of forced labor through evidence that is coherent, auditable, and compatible with how enforcement systems reconstruct supply chains. This is a subtle but critical distinction. In a data-driven environment, firms can be operationally “at risk” if their supply-chain disclosures are incomplete, inconsistent, or unverifiable against external datasets, even if their internal belief is that sourcing is clean.
This dynamic can reshape markets in at least three ways. It increases fixed compliance costs, particularly in sectors with large, multi-tier supply chains and commodity-like upstream inputs. It rewards firms with strong data governance and traceability capabilities, potentially creating competitive advantages unrelated to product quality. Finally, it may indirectly encourage supply-chain consolidation or the migration toward suppliers who can produce standardized, machine-verifiable documentation, raising barriers for smaller suppliers who lack data infrastructure.
For EU readers, these implications resonate with a broader policy trend toward mandatory due diligence and import restrictions associated with forced labor. Even where legal mechanisms differ, the administrative reality converges: enforcement effectiveness depends on traceability and on the ability to produce and validate evidence across tiers.
12. Governance challenges: transparency, error risk, and accountability
Technology-enabled enforcement also introduces governance risks that merit attention in a neutral academic assessment.
One concern is opacity. When decisions are influenced by proprietary datasets and models, affected parties may struggle to understand what precisely triggered scrutiny and what kinds of evidence would be most responsive. If process design does not include adequate explainability and contestability, enforcement may be perceived as a black box.
A second concern is data and model error. Entity resolution mistakes, outdated corporate records, biased coverage of certain regions or languages, and statistical false positives or false negatives are inherent risks in any large-scale analytic system. In a trade enforcement setting, errors have real costs—delays, financial losses, reputational damage—and thus raise the need for careful calibration, human review, and remedial pathways.
A third concern is the drift of evidentiary standards. As enforcement agencies acquire enhanced inference capabilities, there may be a tendency—intentional or not—to demand increasingly granular proof from firms. If evidentiary expectations rise faster than feasible traceability in certain industries, compliance may become uneven and produce distributional effects, favoring larger firms and penalizing smaller actors. Managing this tension requires stable guidance and consistent application, as well as attention to proportionality.
13. A firm-side response: building a verifiable evidence architecture
In a stack-shaped enforcement environment, an effective firm-side response is best framed as building a verifiable evidence architecture rather than producing ad hoc documentation. Three capabilities are particularly salient.
The first is robust entity governance: a master data discipline that maintains consistent supplier identities across languages, name variants, corporate changes, and affiliate structures. This capability enables firms to rapidly reconcile their supplier records with external graphs and to resolve disputes about “who is who” before they escalate into enforcement problems.
The second is tiered traceability with shipment-level integrity: evidence that links procurement, production, transformation, and logistics in a way that is internally consistent and externally auditable. The practical objective is not to generate maximal paperwork but to generate evidence that can survive cross-checking—time-stamped records, coherent lot/batch linkages, and documentation that closes the loop between inputs and outputs.
The third is internal anomaly awareness: monitoring indicators that enforcement systems are likely to interpret as suspicious, such as abrupt supplier substitutions, route changes, intermediary insertion, or sourcing shifts that appear inconsistent with capacity and market realities. When such changes are legitimate, firms benefit from contemporaneous documentation explaining the commercial rationale and preserving supporting records that can be produced quickly if challenged.
These capabilities do not guarantee clearance in every case, but they reduce the probability that a firm will be unable to answer system-generated questions with verifiable, structured evidence.
Conclusion
UFLPA-era enforcement can be understood not only as a legal shift but as an infrastructure shift. A layered enforcement stack—combining corporate identity intelligence, entity-level risk graphing, network-level supply chain inference, behavioral anomaly detection, and process/governance engineering—enables enforcement agencies to scale suspicion, standardize workflows, and focus investigative attention in complex global trade networks. This architecture supports a move from largely case-triggered scrutiny toward more system-triggered screening and monitoring, with significant implications for how evidence is produced, evaluated, and contested.
For firms, the most durable implication is that compliance increasingly depends on demonstrability. In high-risk sectors, the capacity to produce machine-compatible, auditable, and explainable evidence chains becomes as important as the substantive risk controls themselves. For policymakers and regulators—whether in the United States or the European Union—the key governance task is to balance the efficiency gains of data-driven enforcement with safeguards for transparency, error correction, and proportionality, ensuring that the modernization of enforcement strengthens accountability rather than replacing it with opacity.