Mandatory requirements. Verifiable evidence. Reduced exposure.
Effective 28 November 2025, BNM RMiT imposes mandatory requirements on all financial institutions regulated by Bank Negara Malaysia [1, page 4, paragraph 4.1]. Non‑compliance creates significant regulatory exposure for directors, officers, and employees, and may lead to enforcement actions under the Financial Services Act 2013 [1, page 4, paragraph 3.1].
What BNM requires
Under RMiT, Bank Negara Malaysia mandates several requirements for regulated financial institutions [1, page 2]. Among other things, these include the following.
Category 1: Data Classification and Shared Definitions
RMiT Reference: Appendix 5, Part B, paragraphs 1 and 4 [1, page 51]
Verbatim extract (relevant terms highlighted):
“A financial institution must establish a clear DLP strategy and processes in order to ensure that proprietary and customer and counterparty information is identified, classified and secured. At a minimum, a financial institution must – (a) ensure that data owners are accountable and responsible for identifying and appropriately classifying data; (b) undertake a data discovery process prior to the development of a data classification scheme and data inventory.” [1, page 51]
How our semantic layer addresses this:
Our RDF‑based semantic governance layer, aligned with FIBO (Financial Industry Business Ontology) [2], provides machine‑readable classification, shared definitions across disparate systems, and a verifiable, queryable data inventory – directly fulfilling RMiT’s requirement for data to be identified, classified, and secured with enterprise‑wide consistency..
Category 2: AI Explainability, Risk Disclosure, and Hallucination Mitigation
RMiT Reference: Appendix 9, page 62, paragraph 2(c) and (e) [1, page 62]
Verbatim extract (relevant terms highlighted):
“A financial institution must only allow the use of emerging technology in a production environment when, at a minimum, the following requirements are met: … (c) the financial institution must be prepared to suspend the use of emerging technology applications when extreme events such as adversarial attacks arise; … (e) the financial institution must disclose to users that emerging technology is utilized in the system, providing adequate information about associated risks.” [1, page 62]
How our semantic layer addresses this:
Our semantic governance layer provides machine‑readable traceability for every AI decision – tracing inputs, rules, and data lineage. This enables institutions to explain decisions and disclose risks to users and BNM with verifiable evidence, supporting the justification for suspension if needed. By grounding AI outputs in verifiable, provenance‑backed data, our semantic layer helps reduce hallucination risks – directly supporting RMiT Appendix 9’s requirement to disclose risks and suspend systems when extreme events occur [1, page 62].
GraphRAG extension: For advanced explainability, our layer can be extended with GraphRAG – a graph‑based retrieval‑augmented generation approach that uses knowledge graphs for multi‑hop reasoning and explainable AI outputs. This further reduces hallucination risk by grounding LLM responses in structured, provenance‑backed data.
Verifiable Data Lineage and Audit Trail
RMiT Reference: Appendix 5, Part B, page 51 [1, page 51]
Verbatim extract (relevant terms highlighted):
“A financial institution shall ensure that all data-at-rest of Personal Identifiable Information (PII) and transaction data are securely protected and rendered unreadable to unauthorised access through the implementation of robust encryption mechanisms or equivalent capabilities.” [1, page 51]
“The financial institution shall also incorporate scanning or screening of customer information into the scope of periodic security assessments … to detect accidental exposure of customer information on financial institution’s systems.” [1, page 51]
How our semantic layer addresses this:
Our semantic layer delivers continuous, machine‑readable lineage (provenance) – a complete, tamper‑evident record of data origin, every change, every access, and how data connects across systems. This enables reconstruction of data activities from creation to consumption, meeting RMiT’s audit trail expectations with minimal manual intervention, and supports faster root‑cause analysis when exposure is detected.
W3C‑backed verifiability: W3C PROV-O (PROV Ontology) provides a standard data model for expressing and exchanging provenance information, enabling machine‑readable, tamper‑evident records of data origin, derivation, and history [3]. For cryptographic proof of integrity, W3C Verifiable Credentials Data Integrity specifications provide mechanisms for tamper‑evident, third‑party verifiable audit records [4]. OWL 2 provides formal semantics for logical constraints, axioms, and consistency checking [5]. RDF 1.2 Semantics provides the formal foundation for RDF graphs [6].
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What does this mean for your institution?
Taken together, these RMiT requirements [1] demand that a financial institution can, at any time, provide BNM or an auditor with verifiable evidence – that data is classified, AI decisions are explainable, and a complete inventory exists.
This ability to deliver instant evidence is what we call audit readiness. Without a semantic layer, achieving this requires manual processes – exposing your Board and Senior Management.
The reality today
When BNM or the auditor asks for evidence – say, how an AI reached a decision – many banks struggle to provide it at all, or take longer than acceptable [1]. Even sophisticated audit logs cannot trace lineage across disparate systems or provide machine‑checkable proof of data integrity. That gap leaves your Board and Senior Management exposed.
But what if that changed?
What if – whenever BNM or the auditor requests that evidence – your system delivered an instant, complete, audit‑ready trail? That eliminates the struggle and delay, removing personal exposure by design.
The solution: Semantic Governance Layer
The Axonias Semantic Governance Layer is built on proven RDF technology and aligned with FIBO [2]. Our RDF semantic layer enforces logical constraints and uses OWL 2 axioms [5] to automatically verify consistency and infer new facts – with minimal custom code.
What makes this audit‑ready: Every piece of data carries machine‑readable traceability – a tamper‑evident record of its origin, every change, every access, and how it connects to everything else. Using W3C PROV-O as our provenance model [3] and W3C Verifiable Credentials Data Integrity for cryptographic proof [4], we create verifiable, tamper‑evident audit trails. What once required manual effort is now available instantly. For advanced use cases, our layer can be extended with GraphRAG, enabling graph‑based reasoning across provenance trails and query‑focused summarization of audit evidence.
Designed to layer on top of your existing audit logs, agentic frameworks, or graph databases – no rip‑and‑replace required. This is delivered as a standalone semantic governance layer, successfully implemented across the globe by multiple top‑tier financial institutions.
What you gain
·Audit readiness
Wvidence for BNM or any auditor, always complete and verifiable, addressing RMiT Appendix 5 [1, page 51] and Appendix 9 [1, page 62]
·Reduced regulatory exposure for Board and Senior Management
Defensible AI
Every decision can be explained and traced, meeting Appendix 9 disclosure and suspension justification requirements [1, page 62], with reduced hallucination risk (enhanced by optional GraphRAG extension)
Peace of mind
Your evidence is always tamper‑evident and cryptographically verifiable, backed by W3C standards
Next step
If you are interested, we can schedule a confidential discussion to explore how the Axonias semantic governance layer addresses your RMiT audit readiness requirements.
Disclaimer
This document is for informational purposes only. Axonias is not affiliated with Bank Negara Malaysia. BNM RMiT is the sole property of Bank Negara Malaysia [1]. Readers should refer to the official RMiT document for complete regulatory requirements.
Citations
All technical claims in this document are supported by the following references:
[1] Bank Negara Malaysia. (2025). Risk Management in Technology (RMiT). Policy Document. Effective 28 November 2025.
[2] FIBO (Financial Industry Business Ontology). OMG/EDM Council standard for financial business concepts, expressed in W3C OWL.
· URL: https://spec.edmcouncil.org/fibo/
[3] W3C PROV-O (PROV Ontology). W3C Recommendation for expressing and exchanging provenance information.https://www.w3.org/TR/prov-o/
[4] W3C Verifiable Credentials Data Integrity 1.1. W3C Working Draft for cryptographic integrity and tamper‑evident audit records.
· URL: https://www.w3.org/TR/vc-data-integrity/
[5] W3C OWL 2 Web Ontology Language. W3C Recommendation providing formal semantics for classes, properties, axioms, and consistency checking.
· URL: https://www.w3.org/TR/owl2-overview/
[6] W3C RDF 1.2 Semantics. W3C Candidate Recommendation for formal RDF semantics.
· URL: https://www.w3.org/TR/2026/CR-rdf12-semantics-20260407/
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