What does 5.9 require?

Human oversight of each AI use case, system or model is designed, implemented and reviewed regularly across its life, whether the human sits in the loop or over it. How much oversight is needed follows from the purpose of the AI and the materiality of the use case, and the FI stays alert to automation bias and decision fatigue as AI grows faster and more pervasive. Four elements: roles, with responsibility for oversight assigned and escalation and decision routes defined; capability, with competent monitors who hold the authority and the practical means to intervene; design, with AI built from the start to permit oversight, including mechanisms that route outputs or recommendations to a person when predefined conditions on reliability or accuracy are met; and documentation and review, with logs and records kept from development, deployment, operation, monitoring and incident handling, and oversight decisions and interventions (incidents and near misses alike) reviewed regularly to judge whether oversight is working.

Singapore MAS Guidelines on Artificial Intelligence Risk...

Other controls in Section 5 AI life cycle controls: data, transparency, fairness, human oversight, third-party AI, selection, evaluation and testing, technology and cyber, reproducibility, pre-deployment review, monitoring, change management and retirement (paragraphs 5.1 to 5.26) – Singapore MAS Guidelines on Artificial Intelligence Risk Management (2026)

5.1-5.2 Plan and implement controls across the entire AI life cycle with clear roles, reviewed regularly for newer technologies, calibrated to risk materiality, with constraints such as limited third-party disclosure mitigated (including by limiting AI use) so that residual risk stays within appetite, and partial deployments governed by explicit policies
5.10-5.11 Third-party AI: onboarding, development and deployment controls adequate to materiality, contractual visibility over AI introduction and changes, testing in the FI's own context with compensatory testing for disclosure gaps and documented suitability; the FI retains accountability and weighs transparency, supply chain, concentration, change notification, contingency, legal terms, staff capability and complexity, limiting, suspending or replacing the service where residual risk exceeds appetite
5.12-5.13 Select AI systems, models and features against the objectives and risks of the use case, documenting the trade-offs between performance, complexity, fairness and explainability in proportion to materiality, with domain expert or user review, and weigh the heightened risks of newer, less understood systems against the FI's ability to mitigate them
5.14-5.15 Evaluate and test each system or model before deployment to reliability and safety levels proportionate to materiality: identify key risks and set measurable thresholds, define and agree evaluation measures, use testing approaches such as out-of-sample and out-of-time testing, sensitivity, stability, sub-population, stress and adversarial testing, error analysis and benchmarking on representative data, mitigate overfitting, and put guardrails in place for identified limitations, covering the failure modes of Generative AI and agents
5.16 Technology and cybersecurity controls for AI systems commensurate with materiality under MAS technology risk requirements: secured environments with hardened configurations, segmentation, input validation, API authentication, encryption and data loss prevention; tightly controlled role-based and multi-factor access with privileged access management and separation of duties; and governance, data exposure restriction and monitoring of third-party components such as plugins and APIs
5.17 Document the AI development process for reproducibility and auditability, in proportion to materiality and in enough detail for an independent reviewer to understand and potentially replicate the implementation and testing, covering data sources and checks, selection rationale, training procedures with code versions, environments and hyperparameters, evaluation measures and results, explainability and fairness analyses, and assumptions, limitations and mitigants
5.18-5.21 Pre-deployment reviews by parties not involved in development, under clear policies with assigned roles and scope and independence proportionate to materiality; formal independent validation by competent, objective validators for high-materiality use cases covering conceptual soundness, data, implementation integrity, evaluation results, explainability and fairness, and assumptions; documented peer review for others; findings, limitations, remediation and conditions reported to the approval body, which ensures they are actioned
5.22 Technology and cybersecurity reviews before deployment covering technical implementation, system and network security, resilience, recoverability and operational readiness: secure design and least-privilege access review of architecture, APIs, plugins and third-party services with go-live checklists confirming encryption, data loss prevention, firewalls, access restrictions and logging; vulnerability assessment, penetration testing and red teaming, adversarial testing and safeguards such as input validation, throttling and anomaly detection

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MCP endpoint All Singapore MAS Guidelines on Artificial Intelligence Risk Management (2026) controls