Enforcement practice in regulated sectors is undergoing a structural recalibration, in which the traditional emphasis on incident-driven investigations and document-based accountability is being replaced by a supervisory model that relies primarily on data both as the detection mechanism and as the evidentiary substrate. Supervisors are increasingly operating as analytics-led organisations capable of integrating, normalising and testing large volumes of transaction, reporting and behavioural data in short cycles for inconsistencies, outliers and anomalies. This development has a dual effect: on the one hand, the probability of early identification of heightened risks increases through statistical signalling and pattern recognition; on the other hand, the practical burden of proof shifts towards demonstrable control over the data chain, including the ability to trace, at record level, why a particular signal was acted upon or not acted upon. In that context, “defensibility” acquires a technical dimension: it is not only the outcome that matters, but also the reproducibility, explainability and audit trail of detection and decision-making that becomes part of the supervisory dialogue.
At the same time, there is growing recognition that advanced forensic technologies, ranging from journal entry analytics to communications monitoring and automated screening, are no longer deployed solely in the context of incident investigations, but increasingly as continuous instruments for prevention and early warning. Where shortcomings were previously treated as relevant primarily once tangible harm had materialised or intent had been established, an enforcement logic is emerging in which “data gaps”, governance failures and persistent control failures may be treated in their own right as risk factors warranting corrective intervention. In parallel, supervisors are more explicitly steering towards consistency across external regulatory reporting, financial statements and internal management information, not least because discrepancies may indicate incomplete data definitions, deficient reconciliation, or inadequate control over transformations. At the same time, the bar is being raised for the responsible use of analytics and models in regulated environments: not only effectiveness, but also bias management, change control, independent validation, and the ability to explain decisions in clear terms are increasingly treated as prerequisites for trust, proportionality and compliance, including, where personal data is involved, non-compliance with the GDPR as an explicit aggravating factor.
Data-driven supervision: the shift from “reactive” to “proactive” enforcement
Data-driven supervision is developing towards a proactive enforcement approach in which large-scale data analytics no longer functions as an ancillary tool, but instead constitutes the primary detection mechanism. In practice, this means supervisors increasingly rely on automated anomaly detection across transaction, reporting and behavioural data to identify patterns at an early stage that indicate heightened risks, structural vulnerabilities or atypical operating conduct. The traditional cycle of periodic information requests and thematic inquiries is therefore supplemented, and in some cases displaced, by near real-time monitoring, coupled with an expectation that relevant management information is available in a timely manner and that monitoring outputs are reproducible. Where signals cannot be traced back to source data, parameters and decision logic, there is a material risk that the credibility of control statements will be undermined and that the discussion will shift away from the substance of incidents towards structural control and governance.
A second characteristic is the emphasis on consistency across different internal “truths”: regulatory reporting, financial statements and internal management data are expected to be mutually reconcilable, both at aggregated level and through drill-down to transactions and underlying attributes. Divergences between these domains are increasingly treated by supervisors as indicators of incomplete data definitions, uncontrolled transformations or deficient reconciliation processes. This has direct implications for the design of reporting architectures, the quality of mapping tables, the control of exception flows and the existence of shadow reporting. In addition, sector-wide benchmarking enables supervisors to isolate outliers quickly: thematic reviews are scaled on the basis of patterns in sector datasets, with attention directed not only to absolute breaches, but also to relative deviations compared to peers.
A third dimension concerns the enforcement focus on persistent control failures and governance shortcomings, even where intent cannot readily be established. Backlogs in alert handling, structural overrides without a persuasive rationale, or long-standing known issues that remain insufficiently remediated may be treated as aggravating factors that increase the perceived risk profile and trigger more intensive supervision. Enforcement is also increasingly pursued on the basis of “data gaps” as such: missing fields, insufficient data density or deficient logging may be characterised as governance failures that render effective monitoring impossible. Moreover, cross-agency data sharing is intensifying, with automated dataset matching, for example between supervisors, FIU-type bodies and sector registers, increasing detection capability. Whistleblower information plays an expanded role as an input into targeted analytics, while at the same time the threshold for explainability in decision-making and monitoring rises, including the need to account consistently for assumptions, exceptions and escalation decisions.
Data governance as an enforcement-determining factor
Data governance is evolving into an independent anchor of enforcement, because the effectiveness of monitoring, screening and reporting is directly contingent on control over data origin, transformations and ownership. Supervisors place increasing weight on data lineage: the ability to reconstruct where data originates, which enrichments and transformations have been applied, which systems serve as systems of record, and who is accountable for definitions and changes. In the absence of traceability, outputs may be produced, but a defensible explanation for their accuracy and completeness is lacking. This affects not only the control environment, but also the ability to respond effectively to information requests and to demonstrate, in an enforcement setting, that signals were assessed in a consistent, proportionate and reproducible manner.
Master data governance is a core component in this respect, particularly for customer, vendor and product data underpinning sanctions screening, KYC/AML processes and transaction monitoring. Incorrect or inconsistent master data can result in missed matches, incomplete risk profiles, duplicate entities and flawed aggregations within scenarios. As a consequence, focus shifts to explicit data quality standards such as completeness, accuracy, timeliness and consistency, which are not only monitored as KPIs, but are also linked to escalation pathways, remediation obligations and root cause analysis. Data quality is therefore not merely an IT concern; it is a governance issue involving definitions, ownership, exception decision-making and the extent to which business processes capture data correctly at source.
A robust audit trail is essential to make monitoring and reporting defensible. Logging and audit trails are increasingly treated as necessary preconditions to evidence which data was used, which access rights applied, which queries were executed and which decisions were taken, including overrides. Identity and access management is subject to stricter scrutiny, with emphasis on least privilege, periodic privileged access reviews and segregation of duties monitoring. Change management governance becomes equally central: model updates, threshold changes and release controls must be demonstrably controlled so that unauthorised or insufficiently tested changes do not silently degrade controls. Data retention also requires precise alignment with statutory retention periods, investigatory needs and legal holds, particularly because incomplete availability of historical data undermines the ability to perform lookbacks and reconstructions. Outsourcing and cloud governance are addressed explicitly: accountability, audit rights and data portability must be contractually and operationally embedded, supported by periodic independent testing and consistent documentation of data definitions and reporting logic to mitigate interpretive risk.
Transaction monitoring and AML analytics: model risk and tuning discipline
Transaction monitoring and AML analytics are increasingly assessed through the lens of model risk and tuning discipline, because detection effectiveness is materially shaped by scenario coverage, parameterisation and the quality of underlying data. A credible design requires explicit mapping of relevant typologies such as trade-based money laundering, mule accounts and layering to detection rules, scenarios and network indicators, supported by clear articulation of which risks are and are not covered. There is a growing expectation that scenarios are not static, but are periodically recalibrated in light of internal incidents, external typology developments, sector alerts and lookback outcomes. In that context, supervisors increasingly expect a coherent framework explaining the relationship between the risk assessment, the scenario set, threshold choices and the prioritisation of detection outputs for follow-up.
Tuning governance becomes a differentiating factor. Thresholds, filters and segmentation must be supported by data, including analysis of false positives and an explicit approach to false negatives, given that failure to detect relevant patterns will often be treated as more consequential in an enforcement context than generating additional alerts. Back-testing, sensitivity analyses and change approvals sit at the centre of a defensible tuning process, and parameter history and rationale must be reproducible. Backlog management is no longer viewed as a purely operational challenge, but as a control issue: service levels, resourcing and quality assurance must prevent alert fatigue from resulting in fragmented handling, shortcuts or control degradation. A structurally increasing backlog may be treated as a persistent control failure, with direct implications for supervisory intensity and potential enforcement outcomes.
Explainability is not optional in this context. For alerts and closures, a reproducible rationale is expected, supported by evidence capture showing which data was reviewed, what reasoning was applied and why an alert was closed or escalated. Integrating network and entity analytics can enhance detection by making relationships between customers, UBOs and counterparties visible, but it introduces additional requirements for governance over training data, bias testing and model validation where machine learning is used. Quality assurance over case handling, through sampling, peer review and consistency checks, becomes essential to ensure comparable cases are treated comparably. Cross-border constraints, including data localisation and privacy requirements, necessitate controlled review arrangements that safeguard both effectiveness and compliance, with non-compliance with the GDPR presenting a tangible risk where legal bases are inadequate, access restrictions are deficient or retention periods are unclear. Independent testing, including periodic effectiveness reviews and lookbacks supported by closure evidence, is increasingly treated as a minimum standard for demonstrable operating effectiveness.
Sanctions screening technology: ownership/control and circumvention detection
Sanctions screening has evolved from name-based matching into a technology-driven discipline in which ownership/control analysis and circumvention detection are central. Screening systems are expected not only to identify direct matches, but also to assess complex ownership and control structures through entity resolution, shareholding and governance structures, and 50%-type rules or control tests. This requires high-quality data on legal entities, UBOs and connected parties, as well as a consistent logic for determining indirect exposure. Screening quality therefore depends on master data governance, data enrichment and the ability to link entities reliably across datasets, without duplication, transliteration variants or alias structures resulting in missed hits or unacceptable noise.
Fuzzy matching governance is a core risk, because transliteration, alias handling and threshold calibration directly affect the balance between detection and operational workability. Thresholds set too tightly increase the risk of misses; thresholds set too broadly generate alert volumes that strain decision-making and can undermine adjudication quality. A disciplined set-up is therefore expected, in which matching logic, thresholds and exceptions are documented, periodically tested and changed only through controlled change processes. Alert adjudication requires defined service levels, escalation criteria, quality assurance and documented rationale for overrides, not least because overrides without robust justification can readily be characterised as governance failures in a supervisory setting. Evidence retention is critical: logging of match logic, inputs, outputs and operator decisions must be regulator-ready, including the ability to reproduce a screening decision after the fact.
A further emphasis concerns the ability to respond to dynamic changes in lists, ownership structures and transaction flows. Continuous monitoring in response to designation events and ownership changes requires immediate re-screen triggers, to avoid reliance on periodic batch reviews that may detect changes too late. Circumvention analytics is increasingly relevant: route anomalies, transshipment patterns and unusual end-users may indicate evasion behaviour, particularly where screening is enriched with trade finance and shipping data to enable end-to-end detection. Licensing workflow tooling must support conditions, expiries and post-transaction monitoring to demonstrate compliance with licensing requirements. Third-party data enrichment through corporate registries, UBO datasets and adverse media feeds can enhance detection capability, but introduces vendor and data quality risks that must be managed contractually and operationally. Independent validation of screening performance and periodic model reviews are increasingly necessary elements to substantiate the effectiveness and proportionality of screening on an ongoing basis.
Forensic accounting technology: journal entry testing and “books and records” evidence
Forensic accounting technology is shifting from ad hoc analysis to a structured toolkit for journal entry testing and “books and records” evidence, with a focus on continuous detection of anomalies and the ability to generate reproducible evidence. Journal entry analytics targets risk signals such as late postings, manual overrides, unusual user activity, atypical authorisation patterns and outlier entries that do not align with normal posting logic. These analyses become materially stronger where contextual data is added, including roles and entitlements, change history, posting windows and correlation to underlying source transactions. The value in an enforcement context lies not only in identifying an anomaly, but in demonstrating a consistent methodology, completeness of the dataset and traceability of findings back to system-of-record information.
Continuous controls monitoring enables automated detection of deviations in processes such as procure-to-pay and order-to-cash, including unauthorised vendor changes, inconsistencies in three-way matching and atypical credit notes. Vendor master analytics can expose risks through duplicate vendors, bank account overlaps and address clustering, where the combination of master data and payment data is often indicative of fraud or bribery risks. Payment analytics focuses on patterns such as split invoicing, round amounts, weekend payments and offshore routing, which, particularly when combined with atypical approval flows, may signal elevated risk. Revenue analytics can identify cut-off anomalies, channel stuffing signals and round-tripping patterns, and requires carefully defined data domains and consistency with financial reporting to avoid interpretive disputes.
Reconciliation tooling is a critical building block: completeness checks across subledgers, bank statements and the general ledger should be performed systematically to ensure analyses are not conducted on incomplete or unreconciled datasets. Case linking increases evidentiary weight by connecting financial transactions to communications and approvals, enabling reconstruction of causality, timing and decision-making. In an enforcement or investigatory setting, the preparation of evidence packs is essential: reproducible extracts, query logs, parameter history and system-of-record confirmations form the backbone of defensible conclusions. An audit interface aligned with ICFR and disclosure controls, including remediation tracking, supports not only identification of findings but their demonstrable translation into structural control improvements. Finally, lessons learned should be converted into monitoring use cases and control uplift so that findings do not remain isolated anomalies, but result in measurable strengthening of the control environment.
Transaction Monitoring and AML Analytics: Model Risk and Tuning Discipline
Transaction monitoring and AML analytics are increasingly assessed as a model-driven control framework that must be demonstrably effective and demonstrably controlled. At its core, the issue is not merely whether scenarios exist, but whether there is a verifiable alignment between the risk assessment, typology development and the concrete detection logic running in production. An organisation that cannot explicitly trace scenario coverage back to relevant risks—such as trade-based money laundering, mule accounts, layering and the misuse of corporate vehicles—faces the risk that detection has evolved largely through historical accretion rather than deliberate design and periodic refresh. This, in turn, creates an enforcement reality in which supervisors are less persuaded by generic statements about “risk-based monitoring” and instead focus on the traceable substantiation of scenarios, the segmentation of monitored populations, the rationale for thresholds and the manner in which exceptions are governed. Where that substantiation is absent, the discussion tends to shift swiftly to governance failures, because a monitoring system that cannot be traced and explained is difficult to defend in terms of proportionality and consistency.
A second focal point is tuning discipline as a control in its own right. Thresholds, filters, lookback windows and scoring logic largely determine the detection output; that output is then frequently used as the basis for escalation, reporting and decisions with legal and reputational impact. In that context, tuning should not be treated as a purely operational optimisation intended to reduce alert volumes, but as a controlled change to a core control, supported by a demonstrable impact assessment. Back-testing and sensitivity testing constitute the minimum standard to evidence that changes do not produce an unacceptable increase in false negatives, particularly where detection targets typologies characterised by adaptive evasion behaviour. A defensible tuning process therefore requires explicit change governance: defined trigger criteria for recalibration, independent review, controlled release, and a reproducible parameter history capable of later reconstruction. Absent such discipline, supervisors may characterise tuning as a “silent weakening” of controls, with the consequence of intensified supervision and an expectation of immediate remediation.
A third dimension concerns end-to-end control over alert handling and case handling, given that detection effectiveness in practice is materially shaped by follow-up. Backlog management is increasingly treated as an indicator of control degradation: growing backlogs, structural breaches of SLAs and insufficient quality assurance may point to under-resourcing, unsuitable tooling or inadequate prioritisation, and therefore to a persistent control failure. In that setting, a mature quality model is expected: sampling controls, peer reviews, consistency checks and audit-ready file building, including evidence capture that is traceable to the sources used and the relevant context. Explainability is a core requirement: closures must be reproducible and supported by a verifiable rationale, enabling retrospective determination of why an alert was assessed as non-suspicious and what information underpinned that assessment. Where cross-border constraints apply—such as data localisation, secrecy laws and privacy requirements—additional demands arise for controlled review arrangements; inadequate control of these prerequisites may result in non-compliance with the GDPR, which in a supervisory context may be classified not merely as a compliance issue but as a governance and accountability deficiency.
Sanctions Screening Technology: Ownership/Control and Circumvention Detection
Sanctions screening is moving towards an integrated technology practice in which the classic name match is only the starting point and where ownership/control analysis and circumvention detection are determinative of effectiveness. The enforcement bar is shifting towards the ability to identify indirect exposure through complex corporate structures, shareholdings, control rights and beneficial ownership relationships, not least because sanctions risk often manifests through nominees, layered entities and transaction flows that formally sit outside direct listing. This requires high-quality entity resolution: reliably consolidating entities across internal systems and external sources, supported by explicit rules for deduplication, alias management and transliteration variants. Where entity resolution is not adequately controlled, the same party may appear in multiple guises, leading to inconsistent screening and non-uniform application of escalation criteria. Supervisors therefore expect the “single view” of entities to be more than an aspiration: it should operate as a demonstrably effective mechanism with clear ownership and periodic quality measurement.
Governance of fuzzy matching forms a second critical layer, because calibration of thresholds and match logic determines the balance between detection, operational workability and consistency. In a defensible set-up, thresholds must be explainable not only technically but also from a policy perspective: what level of noise is acceptable, which risks are intentionally covered, and how operational pressure is prevented from translating into structural overrides or routine suppression of borderline matches. This requires a tightly designed adjudication process with SLAs, escalation routes, second-line review for sensitive categories and a consistent QA structure capable of detecting divergent decisions. A documented rationale for overrides is essential; overrides without traceable substantiation may be interpreted, in an enforcement context, as a governance failure, particularly where they are structural and unaccompanied by corrective measures. Evidence retention is equally a core prerequisite: not only the decision itself, but also the inputs, match scores, lists used, parameters and the timeline of updates must be reconstructable to demonstrate retrospectively that screening operated correctly at the relevant point in time.
A third dimension is the capability to respond to dynamic changes in lists, ownership structures and transaction flows. Continuous monitoring around designation events and ownership changes requires immediate re-screen triggers that are not dependent on periodic batch processes, because the compliance implications of delayed detection may be substantial. Circumvention analytics therefore assumes greater prominence: route anomalies, transhipment patterns and unusual end-users may indicate evasion, particularly where trade finance data, shipping data and end-use information are integrated. In that context, licensing workflow tooling becomes increasingly relevant: conditions, expiries, permitted counterparties and post-transaction monitoring must be demonstrably controlled to prevent licences from being treated as a formality without effective compliance. Third-party data enrichment can strengthen detection, but introduces vendor and data quality risks that must be addressed contractually (audit rights, transparency, exit/portability) and operationally (validation, monitoring, incident management). Independent validation of screening performance and periodic model reviews therefore become not merely “good practice” but a necessary building block for regulatory defensibility.
Forensic Accounting Technology: Journal Entry Testing and “Books & Records” Evidence
Forensic accounting technology is developing towards a continuous evidence function, in which journal entry testing and “books & records” integrity can be assessed not only periodically but on an ongoing basis. Journal entry analytics targets patterns that repeatedly prove relevant in forensic contexts, such as late postings, manual overrides, unusual users or roles, atypical posting windows, exceptional combinations of general ledger accounts and amounts that do not align with normal process patterns. The value of such analytics increases materially where the data environment enables entries to be enriched with context: workflow steps, authorisation chains, change history, source systems and references to underlying documentation. This makes it possible not only to determine that an entry deviates, but also whether that deviation is plausibly explainable or constitutes an escalation signal. In an enforcement context, there is also an expectation that selection criteria and analytical methods are reproducible, such that it can be demonstrated retrospectively that findings did not arise from opportunistic sampling but from consistent and controllable detection logic.
Continuous controls monitoring (CCM) forms a second pillar, particularly where process risks manifest within procure-to-pay and order-to-cash. Automated detection of deviations—such as exceptions to three-way matching, unauthorised vendor changes, atypical approval paths or unusual credit notes—enables timely identification of control failures before they generate material loss or reporting impact. Vendor master analytics can reveal structural vulnerabilities through duplicate vendors, bank account overlaps and address clustering, which in practice frequently correlate with fraud or bribery risks, especially where governance over supplier onboarding and changes is insufficiently strict. Payment analytics reinforces this picture by isolating patterns such as split invoicing, round amounts, weekend payments and routing via offshore accounts, where correlation with approvals and limit exceptions is essential for defensible interpretation. Revenue analytics adds a specific dimension, focused on cut-off anomalies, channel stuffing signals and indications of round-tripping, where alignment with financial reporting and disclosure controls is critical to limiting interpretive and classification risk.
A third element is assurance over completeness and reconciliation, because forensic findings are only defensible where the underlying datasets are complete and consistent. Reconciliation tooling that tests subledgers, bank statements and the general ledger for completeness is a necessary prerequisite to prevent analytics being performed on partial or unreconciled populations. Case linking then enhances evidentiary value by connecting financial transactions to communications, approvals and workflow logs, allowing better reconstruction of timing, decision-making and potential influence. In a regulator- or litigation-sensitive context, the assembly of evidence packs becomes a core process: reproducible extracts, query logs, system-of-record confirmations, parameter history and chain-of-custody documentation should be structured so that the integrity of the evidential record cannot be credibly challenged. An audit interface that links to ICFR and disclosure controls, including remediation tracking, enables findings not only to be reported but also to be demonstrably translated into structural improvements. This turns “lessons learned” into a controlled process in which findings are systematically converted into monitoring use cases and control uplift, supported by measurable follow-up and demonstrable reduction in recurrence risk.
Digital Discovery and AI-Assisted Review: Scale, Speed and Defensibility
Digital discovery and AI-assisted review have become essential components of modern investigative and enforcement practice, precisely because scale and speed are, as a rule, no longer compatible with the required level of care without technological tooling. eDiscovery processing includes a set of baseline measures treated as hygiene factors: deduplication to reduce duplicate documents, threading to assess conversations in context, and metadata preservation to safeguard the integrity of timelines, authorship and document history. Where metadata is incomplete or is altered during processing, evidential risk increases significantly, because reconstructions of intent, decision-making and timing typically depend on such attributes. In an enforcement context, supervisors therefore expect not only technical correctness in processing, but also that methodology and tooling are audit-ready, including documented workflows, reproducible steps and control points demonstrating that the dataset has not been inadvertently altered.
Technology-assisted review (TAR) and more advanced AI-supported review methods then require strict governance, because acceptance of automated classification depends on demonstrable quality and defensible choices. Training sets should be representative, sampling must be statistically grounded, and acceptance criteria should be pre-defined to avoid “performance” being defined by reference to desired outcomes. AI-assisted summarisation may increase efficiency, but introduces a real risk of hallucination and bias; controlled use therefore requires explicit QA requirements, human verification of core conclusions and a clear distinction between summary and factual determination. Privilege protection presents a distinct risk: automated privilege detection can be supportive, but typically remains dependent on second-level review for borderline cases, given that privilege assessments are context-dependent and errors may have disproportionate consequences. Multilingual review adds complexity through translation, transliteration and context-aware search parameters, where careless configuration can lead to missed documents or misunderstood meaning.
A third dimension concerns production governance and defensibility before courts or supervisory forums. Redaction standards, load file specifications, rolling productions and version control must be tightly managed to prevent productions from becoming inconsistent, incomplete or non-reproducible. Audit trails are indispensable: logging review decisions, overrides, quality checks and search iterations enables retrospective explanation of why certain documents were or were not produced and how quality was assured. Data privacy and confidentiality require controlled access rooms and least-privilege review environments, particularly because review teams often access large volumes of personal data and confidential information; inadequate measures can culminate in non-compliance with the GDPR, with escalation effects that may complicate the underlying matter. Court/regulator defensibility is ultimately supported by method statements, validation reports and reproducibility of the review approach, ensuring that the process is not merely efficient but also controllable, explainable and legally defensible.
Communications Analytics: Intent, Collusion and Off-Channel Conduct
Communications analytics is assuming an increasingly important position in supervisory and enforcement contexts, because intent, collusion and off-channel conduct are often primarily visible through communication patterns. A mature approach begins with comprehensive mapping of communication channels: email, chat tools, mobile messaging and collaboration platforms, including governance around archiving, retention and access control. Incomplete channel coverage creates structural blind spots, which supervisors increasingly characterise as governance failures, particularly where the existence of shadow IT or private channels was known but insufficiently addressed. Detecting off-channel communications therefore requires both technical signalling—such as unusual routing, unregistered applications, export and forwarding patterns—and policy enforcement, with clarity on permitted channels, how exceptions are assessed and what consequences follow from breaches.
NLP analysis of risk language can generate signals around urgency cues, code words and euphemisms that appear more frequently in fraud, bribery or sanctions contexts. The evidential value of such analytics, however, depends heavily on controlled use, because language patterns are context-sensitive and carry a high risk of over-interpretation. A defensible set-up therefore requires that outputs are treated as indicative rather than determinative evidence, and that interpretation is consistently supported by additional facts and human assessment. Social network analysis can make collusion indicators visible through clusters, key nodes and anomalous interaction patterns, particularly where communication volumes and directions deviate materially from normal working relationships. The strength of these analytics increases where correlation with transaction data is established, for example by comparing approvals against instructions, timing and potential pressure or influence. This enables a reconstruction that shows not only “what” occurred, but also maps plausible causality, provided that limitations and assumptions are made explicit.
A third component is forensic reconstruction of deletions and edits, because evidential records in communications are often shaped by retention settings, version histories and audit logs. In environments involving BYOD or limited MDM coverage, legally defensible access is particularly complex: privacy constraints, proportionality and legal bases must be carefully secured to avoid subsequent challenges to evidence and to prevent non-compliance with the GDPR. Witness interference monitoring may be additionally relevant, particularly by detecting unusual contact patterns after an investigation has commenced, although this requires an explicit legal basis and strict proportionality assessment. Reporting discipline is essential in this domain: conclusions should be expressed with appropriate caveats, with a clear separation between factual observation, analytical indication and interpretive assessment, and with an avoidance of overclaiming. Such an approach supports not only the substantive robustness of findings, but also strengthens defensibility vis-à-vis supervisors and other external stakeholders.
