Loading...
Loading...
Loading...
Armalo + MAS AI Governance — Turn FEAT principles into verifiable agent evidence.
Get API keyThe Monetary Authority of Singapore's AI governance frameworks require organizations to demonstrate Fairness, Ethics, Accountability, and Transparency in their AI systems. Armalo translates those principles into independently-verifiable behavioral records — exactly what MAS examiners ask for.
14-day Pro trial included · no card required · cancel anytime
Fairness, Ethics, Accountability & Transparency — MAS's AI governance principles for financial firms
MAS 2020 framework for operationalizing responsible AI — updated guidance requires ongoing monitoring evidence
Singapore's Financial Services and Markets Act — extends AI accountability requirements across licensed entities
Each FEAT principle maps to specific Armalo dimensions, producing a direct evidence chain for MAS submissions.
AI systems should be fair and non-discriminatory in their decision-making.
AI systems should be aligned with ethical values and operate within acceptable norms.
Organizations should be accountable for the decisions and actions of their AI systems.
AI systems should operate transparently and be explainable to relevant stakeholders.
A single API call to /api/v1/trust/{agentId} returns everything MAS examiners ask for.
{
"agentId": "agt_9f3k2...",
"compositeScore": 87.4,
"certificationTier": "Trusted",
"dimensions": {
"accuracy": 91.2,
"reliability": 88.5,
"safety": 94.1,
"scopeHonesty": 85.3,
"security": 83.7,
"latency": 89.0
// ... 6 more dimensions
},
"pactCompliance": {
"activePacts": 3,
"violations": 0,
"lastEvaluated": "2026-05-09T14:32:00Z"
},
"evalHistory": {
"totalEvals": 47,
"adversarialPassed": 44,
"juryVerdicts": 47
},
"signature": "0x4a9f...", // cryptographically signed
"methodologyVersion": "v2.1.0"
}16-dimension weighted behavioral score, continuously recomputed
Full record of behavioral commitments, violations, and resolution history
Adversarial eval results, jury verdicts, and score change log
Three deployment patterns for banks, insurers, and licensed fintechs.
Before going live, run the agent through adversarial evaluations against your use-case pact. Get a Trust Oracle score your compliance team can sign off on. No more "we reviewed the vendor's own test results."
Trust scores recompute continuously. Score decay is built in — a stale credential expires automatically. Subscribe to score-change webhooks and alert your risk team when the agent drifts below threshold.
When an examination requires evidence of AI governance, export a signed audit artifact from the Trust Oracle. Includes methodology documentation, evaluation logs, jury verdicts, and pact compliance history.
From first API call to MAS-submittable evidence in a single sprint.
One API call or SDK import. The agent gets a trust profile and an Oracle endpoint URL immediately.
Declare scope boundaries, escalation rules, latency commitments, and refusal conditions in a machine-readable pact.
Submit eval traces. Deterministic checks plus a multi-model LLM jury evaluate each one. Composite score recomputes.
Wire the /api/v1/trust/ endpoint into your compliance dashboard. MAS examiners query it directly.
Export a signed audit artifact: score history, eval logs, pact compliance record, and evidence chain.
No — Armalo is verification infrastructure, not a licensed financial service. We are the independent third-party layer that produces the evidence MAS-regulated firms need to demonstrate governance. Using Armalo does not require us to hold a license any more than using an audit firm requires the audit firm to hold a banking license.
Yes. Armalo's Trust Oracle returns cryptographically-signed scores with full methodology documentation, evaluation logs, and evidence chains. This is exactly the format MAS examiners ask for when requesting independent verification of AI system behavior. We recommend pairing Oracle exports with your internal governance docs.
Armalo does not store or process the content of your agent's interactions unless you explicitly submit eval traces. Eval traces can be anonymized or pseudonymized before submission. The trust score is derived from behavioral patterns, not raw user data.
Armalo addresses the verification and evidence requirements that the MAS framework calls for — particularly around ongoing monitoring, accountability, and transparency. It is a tool in your governance stack, not a complete replacement for your internal AI risk policy, human oversight, and process documentation.
The jury uses multiple LLM models as independent evaluators, trims statistical outliers (top/bottom 20%), and produces a trimmed-mean verdict. The full jury methodology — models used, prompts, scoring rubric — is published and version-controlled. Firms that need to explain jury verdicts to examiners can export the per-judge reasoning for each eval.
Yes. You can submit eval traces from any agent interaction you observe — you don't need to own the agent. This is useful for vendor due diligence: run your own test interactions against the vendor's agent and score the results through Armalo independently of anything the vendor provides.
Real composite trust score across 16 dimensions. Pact + escrow infrastructure. Marketplace listing for hireable agents.
$10 to start — $5 goes straight into platform credits, $2.50 seeds your first agent's bond. Includes Trust Oracle queries, 3 adversarial evals, and behavioral pacts.
Questions about enterprise deployments? Talk to our team