Compliance Monitoring Engine
Continuous monitoring across PDPO, PDPA, PIPL, APPI, MAS-TRM, J-SOX. Find findings before auditors do.
The problem
Your compliance team owns a 240-page policy document, a quarterly attestation cycle, and a sinking feeling that operational reality has drifted from what is on paper. The annual audit takes 14 weeks. Findings include three "process not followed" issues that started six months earlier and went unnoticed.
KPMG's 2024 Asia Compliance Outlook finds that 64% of mid-market regulated firms in APAC report compliance findings that originated more than 90 days before discovery — most preventable with continuous monitoring.[^1] The bottleneck is not policy quality. It is the gap between policy and operational reality.
Our approach
Source: policies (PDF, Confluence, SharePoint) + operational data
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Policy ingestion (Document Intelligence Suite + RAG Stack)
- clause extraction, control mapping, regulatory tagging
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Operational evidence collectors
- access logs (Okta, Azure AD, Active Directory, SailPoint)
- change logs (Jira, ServiceNow, GitHub, GitLab)
- communication metadata (Slack, Teams, email — metadata only)
- transaction logs (ERP, core banking, custody)
- configuration state (AWS Config, Azure Policy, GCP Security Command)
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Drift detection engine
- rule-based (deterministic compliance checks)
- LLM-based (Claude Sonnet 4.6, with policy + evidence + reasoning)
- confidence + materiality scoring
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Triage queue (Filament UI)
- high materiality, high confidence → automatic ticket to control owner
- low materiality → trended in dashboard
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Audit-evidence pack generator (on demand)
Who it is for
- A licensed financial-services firm in Singapore under MAS-TRM with policy, change-management, and access-control obligations.
- A regulated insurer in Hong Kong under HKMA and PDPO with cross-border data flow obligations to Mainland China.
- A 600-person Mainland China subsidiary of a Japanese conglomerate managing PIPL, APPI, and J-SOX in parallel.
Tech stack
- Policy ingestion: Powered by AIMenta Document Intelligence Suite + Knowledge Base / RAG Stack
- LLMs: Claude Sonnet 4.6 for policy interpretation and drift analysis (high stakes); Claude Haiku 4 for high-volume rule classification
- Evidence collectors: Custom Laravel jobs against vendor APIs (Okta, Azure AD, Jira, ServiceNow, GitHub, AWS Config, Azure Policy, GCP Security Command, SAP audit log, Oracle audit log)
- Storage: Append-only event store (AWS QLDB, Azure equivalent, or self-hosted Postgres with audit triggers) — tamper-evident and exportable
- Workflow: Laravel + Filament 3 with role-based reviewers and approvers
- Reporting: PhpSpreadsheet, PptxGenJS, custom regulator-format exports (J-SOX templates, MAS templates, HKMA templates)
Integration list
Okta, Azure AD, Active Directory, SailPoint, Jira, ServiceNow, GitHub, GitLab, AWS Config, Azure Policy, Google Cloud Security Command, SAP, Oracle EBS, NetSuite, custom core banking systems via secure API, Slack and Teams (metadata only — no message content unless explicitly enabled), email metadata via Microsoft Graph or Google Workspace API.
Deployment timeline
| Week | Activity |
|---|---|
| Week 1-2 | Policy ingestion (top 3 policy domains); regulator scope confirmed |
| Week 3-4 | Evidence collectors deployed (typically 6-12 sources for first wave) |
| Week 5-6 | Drift detection rules + LLM prompts tuned against historical data |
| Week 7-8 | Triage queue live; first review cycle with compliance team |
| Week 9-10 | Cutover to live monitoring; first month-end attestation generated automatically |
Mini-ROI
A licensed financial-services firm in Singapore deployed the engine for MAS-TRM controls in week 8 of 2025. Annual audit preparation time dropped from 14 weeks to 5. Internal-audit findings discovered before external audit rose 3.7x. Estimated avoided remediation cost: US$340,000 in year one (regulator-flagged findings carry remediation timelines that can compress operations).
KPMG's benchmark is US$240,000-US$680,000 annual value per regulated mid-market entity from continuous compliance monitoring versus point-in-time attestation, primarily through avoided remediation and reduced external-audit hours.[^2]
Pricing tiers
| Tier | Setup (one-time) | Monthly run cost | Best for |
|---|---|---|---|
| Starter | US$32,000 - US$58,000 | From US$2,400/mo | One regulatory regime, 3-5 control domains, single subsidiary. |
| Scale | US$75,000 - US$160,000 | From US$5,800/mo | 2-3 regulatory regimes, 10+ control domains, multi-source evidence. |
| Strategic | US$180,000 - US$380,000 | From US$11,500/mo | Group-level multi-jurisdiction (HK + SG + JP, or HK + CN + JP), regulator-grade reporting, dedicated audit support. |
All tiers include external-audit walkthrough support and quarterly regulatory-update reviews.
Frequently asked questions
Does this replace our compliance team? No. It scales them. Compliance professionals shift from data-collection drudgery to investigation and judgement. Across the last 9 deployments, compliance team headcount remained flat or grew slightly — but the function expanded from quarterly attestation to continuous monitoring.
Will the regulator accept AI-assisted compliance evidence? Yes, when audit trail is complete. Every model decision logs the policy clause, the evidence source, the reasoning, the confidence, and the human reviewer. We have walked HKMA, MAS, FSA Japan, and OJK Indonesia through deployments. None have raised methodology objections.
Can the engine read messages in Slack and Teams? By default, only metadata (who sent what when, in which channel). Message content is read only with explicit configuration and a documented use case (e.g., monitoring for insider-trading red flags in a regulated trading desk). All such configurations require sign-off from compliance and HR.
How does this handle PIPL cross-border data flow obligations? Evidence collectors stay in country. Cross-border policy comparisons (e.g., comparing a HK policy to a CN subsidiary's implementation) happen in a designated cross-border zone with PIPL standard contract clauses applied. Data flow diagrams ship with every multi-jurisdiction deployment.
Can we extend the engine for new regulations? Yes. New regulatory regimes are onboarded on a fixed-fee basis (typical: US$18,000-US$45,000 per regime depending on policy complexity). The engine has been extended to 11 regulations across the nine markets in the last 24 months.
What happens when policies change? The policy ingestion pipeline re-runs on policy update. Affected controls re-baseline. Drift signals between old and new policy are surfaced to the compliance team for reconciliation. Typical lag from policy publish to monitoring update: 48 hours.
Will this slow our operations? No. Evidence collection is read-only and asynchronous. Drift detection runs on a scheduled basis (typically every 4 hours for high-materiality controls, daily for medium, weekly for low). Operational systems are unaffected.
How do we measure success? Three metrics: % of audit findings discovered internally before external audit, average time from drift to detection, and compliance-team time-allocation shift (% on data collection vs % on investigation). Reported monthly.
Where this is most often deployed
Industries where AIMenta frequently scopes this kind of solution.
Beyond this solution
Browse our other productized solutions, plus the verticals and Asian markets where they ship.
Other solutions
By industry
By Asian market
Frequently asked questions
Which regulatory frameworks does the engine monitor out of the box?
Initial coverage includes: MAS TRM and Notice 655 (SG financial services), HKMA Supervisory Policy Manual (HK banking), PDPC Advisory Guidelines (SG data protection), Japan APPI amendments, Korea PIPA, China PIPL, and EU AI Act (for companies with EU nexus). Framework libraries are updated quarterly as regulations evolve.
How does the engine detect a potential compliance breach before it escalates?
The engine continuously scans transaction logs, communication records, and operational reports against a rule library mapped to the relevant regulations. When a pattern matches a risk indicator — for example, an unusual fund transfer sequence under AML rules — an alert is generated with severity rating, regulatory reference, and recommended remediation steps, typically within minutes of the triggering event.
Can the engine produce the evidence packages auditors need for regulatory submissions?
Yes. For each monitored period the engine generates a compliance summary report with pass/fail status per regulatory requirement, supporting evidence (logs, document excerpts, timestamps), and exception commentary. Reports are templated to the specific regulator format (MAS, HKMA, SFC) and can be exported as Word or PDF for legal review before submission.
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