--- title: "Audit Logging and Provenance for AI Workflows (AWS and MongoDB Patterns)" description: "If an AI system affects legal, financial, or compliance outcomes, you need to answer: what happened, when, why, and who approved it. This post outlines audit logging a" url: https://lidvizion.ai/blog/audit-logging-and-provenance-for-ai-workflows-aws-mongodb/ site: Lid Vizion index: https://lidvizion.ai/llms.txt --- [Insights](https://lidvizion.ai/blogs/) Integration Playbooks 3 min read 2025-11-17 # Audit Logging and Provenance for AI Workflows (AWS and MongoDB Patterns) If an AI system affects legal, financial, or compliance outcomes, you need to answer: what happened, when, why, and who approved it. This post outlines audit logging a By [Shawn Wilborne](https://lidvizion.ai/author/shawn-wilborne/) [Integration Playbooks](https://lidvizion.ai/blog-categories/integration-playbooks/) Tags: [Audit Logging](https://lidvizion.ai/tags/audit-logging/), [AWS](https://lidvizion.ai/tags/aws/), [MongoDB](https://lidvizion.ai/tags/mongodb/), [OCR](https://lidvizion.ai/tags/ocr/) ![Hero image for: Audit Logging and Provenance for AI Workflows (AWS and MongoDB Patterns)](https://rcicvvtcawdbfiuzwexb.supabase.co/storage/v1/object/public/webflow-media/webflow/media/cms/69927ccd1fcb2cff2feb05c3/68cdf6bb2acf72bfc5602273-monitoring-logging-and-analytics-for-vision-systems.png) ## Key Takeaways - Audit logs are a product feature for regulated workflows. - Capture hashes and versions so outputs are defensible. - Use S3 versioning and append only logs. - Store review actions and approvals as explicit events. If an AI system affects legal, financial, or compliance outcomes, you need to answer: what happened, when, why, and who approved it. This post outlines audit logging and provenance patterns for AWS plus MongoDB based systems. ## What to capture in an audit log At minimum: - Actor (system, user, service) - Timestamp - Action (state transition) - Input pointers and hashes - Output pointers - Model or ruleset version ## Append only, never overwrite An audit log should be append only. If something changes, you write a new event. This reduces risk and simplifies investigations. ## AWS storage patterns - Store raw documents in S3 with versioning - Store logs in CloudWatch and export if needed References: - S3 versioning: [https://docs.aws.amazon.com/AmazonS3/latest/userguide/Versioning.html](https://docs.aws.amazon.com/AmazonS3/latest/userguide/Versioning.html) - Amazon CloudWatch Logs: [https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/WhatIsCloudWatchLogs.html](https://docs.aws.amazon.com/AmazonCloudWatch/latest/logs/WhatIsCloudWatchLogs.html) ## MongoDB patterns MongoDB can store: - Audit events (append only) - Workflow state per document - Review and approval actions Useful features: - Change streams for downstream consumers References: - Change streams: [https://www.mongodb.com/docs/manual/changeStreams/](https://www.mongodb.com/docs/manual/changeStreams/) ## Compliance baselines Map controls early. References: - NIST SP 800-53: [https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final](https://csrc.nist.gov/publications/detail/sp/800-53/rev-5/final) - NIST AI RMF: [https://www.nist.gov/itl/ai-risk-management-framework](https://www.nist.gov/itl/ai-risk-management-framework) - OWASP ASVS: [https://owasp.org/www-project-application-security-verification-standard/](https://owasp.org/www-project-application-security-verification-standard/) zed in logs, store pointers where possible, and implement deletion workflows that respect legal requirements. **Q: Should we log model prompts and responses?** Sometimes yes, but treat them as sensitive. Apply encryption and access controls, and consider redaction. Internal reference: - Lid Vizion secure automation blog: [https://lidvizion.ai/blog/secure-expungement-automation-ocr-rules-pdf-sharepoint](https://lidvizion.ai/blog/secure-expungement-automation-ocr-rules-pdf-sharepoint) ## FAQs **Q: How long should we retain audit logs?** Retention depends on your domain and policy. Design for configurable retention with legal hold capability. **Q: How do we handle privacy requests?** Keep personal data minimi **Q: What should we link to internally?** A: Link to relevant solution pages like Computer Vision or Document Intelligence, and only link to published blog URLs on the main domain. Avoid staging links. [PreviousMulti Tenant AI SaaS Architecture on AWS and MongoDB (A Practical Guide)](https://lidvizion.ai/blog/multi-tenant-ai-saas-architecture-aws-mongodb/) · [NextCost Optimization for AI Document Pipelines on AWS (A Playbook)](https://lidvizion.ai/blog/cost-optimization-for-ai-document-pipelines-on-aws/) Keep reading ## Related insights. - [AI Document Analysis for Contract & Form Review: Real Estate + B2B Workflow Demo](https://lidvizion.ai/blog/ai-document-analysis-contract-form-review-real-estate-b2b/): See how Lid Vizion’s Document Intelligence workflow drops into your app to review forms, extract fields, and flag compliance issues—ideal for real estate brokerages and any document-heavy team. - [Human in the Loop Document Review UI (Architecture on AWS and MongoDB)](https://lidvizion.ai/blog/human-in-the-loop-doc-review-ui-architecture-aws-mongodb/): Fully automated document extraction is rare in real operations. The difference between a pilot and a production system is usually a clean human in the loop layer that ha ## Where does your workflow get stuck? [Discuss the workflow](https://lidvizion.ai/contact/) · [Book a call](https://calendar.app.google/bB26LdA9e2gmGFp69)