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18 articles from Crelis on MAS Guidelines. Most recent: “Hong Kong, Singapore, Indonesia: Three Regulators, Three Retention Clocks”.
Cross-border agent record retention should start with named instruments and authority records, not country labels or platform log defaults.
Audit evidence is not a synonym for logs. For AI agents, the useful artefact must support the conclusion under review.
AI model risk management for Singapore banks is an evidence problem, especially when agents can act at runtime.
Agent security maps help only if they lead to evidence for the permission behind a consequential action.
MAS’s AI risk management material increases pressure to evidence authorization at the point an agent acts.
MCP server guidance can reduce unsafe calls, but the audit problem is proving who authorised the action before money, records, or tools changed.
OWASP maps agentic AI risks; it does not prove who authorised a payment, record change, or refund.
An LLM judge can return a different verdict on the same facts tomorrow; a deterministic policy engine returns the same decision each time and can name the rule that made it — that difference decides w
MAS SAFR, read for financial institutions: every safeguard the white paper names, the record it implies, and what your agentic AI would have to be able to produce.
As of January 1, 2026, the legal landscape shifted permanently.
Dynatrace found that nearly half of organizations discard log data, excluding an average of 86% of it. Tamper-evident AI audit logs turn passive monitoring into proof of authorized execution.
What happens when an autonomous agent executes a high-value transfer that no human authorized and no legacy log can explain? MAS and industry published the SAFR white paper in July 2026.
Understanding a model's logic is not a legal defence. The industry is moving from model explainability to verifiable proof of what an agent actually did.
In a high-stakes clinical environment, an AI's output is a mere proposal until a human grants the permission to execute.
Trust is a structural vulnerability in your enterprise AI stack. As autonomous agents scale, the gap between an AI proposal and a permitted action becomes a high-stakes liability. You cannot audit an
Grant Thornton found that just 18% of banking leaders were fully confident they could pass an independent review of their AI controls in the next 90 days.
Singapore governs AI through voluntary frameworks, data protection law and sector guidance rather than a binding AI statute — this reference maps each instrument, what it asks of an enterprise, and th
The framework a bank needs is not a policy document. It is the ability to show, for one agent action, the authority it relied on, the decision that let it through, and a record of both that does not d