AI Compliance Monitor
Continuously watch documents, policies, and activity for compliance gaps before they become findings.
- Project range
- $8,000–18,000
- AWS running cost
- $80–400/mo
- Time to deploy
- 4–6 weeks
- Best-fit industries
- Healthcare, Financial services
Executive summary
A monitoring system that maps your obligations — regulations, contracts, and internal policies — into machine-checkable controls, then continuously evaluates documents and activity against them. It surfaces gaps with citations, tracks remediation, and produces audit-ready evidence, replacing point-in-time manual reviews with always-on assurance.
Business problem
Compliance is checked periodically and by hand, so problems are found late — often during an audit or after an incident. Requirements change, evidence is scattered across systems, and assembling proof for auditors consumes weeks of staff time.
Architecture
AWS services
Amazon EventBridge
Messaging- — Trigger evaluation on document or policy changes
- — Schedule periodic full sweeps
AWS Lambda
Compute- — Evaluate records against controls
- — Track findings and remediation state
Amazon S3
Storage- — Immutable evidence and document store
- — Versioning and retention for audit
Amazon Bedrock
AI / ML- — Map obligations to controls
- — Explain gaps with citations to source text
Control knowledge base
AI / ML- — Encode the regulatory and policy framework
- — Ground findings in authoritative language
Amazon DynamoDB
Database- — Findings, severity, and remediation tracking
- — Control coverage state
Amazon SNS
Messaging- — Alert risk owners on new or escalating gaps
Amazon CloudWatch
Observability- — Operational logs, metrics, and alarms
Data flow
- 1
A document, policy, or activity record changes and EventBridge triggers an evaluation.
- 2
Lambda checks the record against the encoded control framework, using Bedrock to reason over obligations.
- 3
Gaps are recorded in DynamoDB with severity and citations to the exact source language.
- 4
Evidence is preserved immutably in S3 with versioning for audit defensibility.
- 5
SNS alerts route findings to risk owners; a dashboard tracks coverage and remediation to closure.
Security considerations
- Immutable, versioned evidence store supports defensible audit trails.
- Least-privilege IAM; segregation of duties between evaluation and remediation.
- All findings cite source text — no unexplained black-box judgments.
- Sensitive records encrypted at rest and in transit; access logged.
Cost considerations
- Bedrock evaluation is the main variable cost — driven by document volume and change rate.
- Event-driven checks avoid re-scanning unchanged material, controlling spend.
- S3 lifecycle tiers move older evidence to cheaper storage while preserving it.
Scalability
- Serverless and event-driven; scales with document and control volume.
- New regulations or policies extend the framework without code changes.
- Multiple frameworks (e.g., HIPAA, SOC 2, contractual) run side by side.
Deployment roadmap
Phase 1 — Encode controls
Weeks 1–2- — Translate obligations into checkable controls
- — Provision evidence store and AWS foundation
Phase 2 — Build & connect
Weeks 3–5- — Build the evaluation engine and alerting
- — Connect document sources and activity feeds
Phase 3 — Validate & harden
Week 6- — Validate findings with your compliance team
- — Tune severity thresholds and reporting
Future enhancements
- Auto-generated audit packages and regulator response drafts.
- Predictive risk scoring to prioritize preventive action.
- Vendor and third-party compliance monitoring.
- Control-coverage trends reported into the executive dashboard.