Creating AI Workflows That Maintain Chain of Custody in eDiscovery

As law firms and legal departments layer AI into eDiscovery, chain of custody remains the non‑negotiable backbone of defensibility. Every identification, collection, processing, review, and production decision must be traceable and reproducible—even when AI accelerates the work. This week’s guide shows how to design AI-driven workflows that preserve evidence integrity, reduce risk, and deliver faster, more accurate outcomes using platforms like Microsoft 365, RelativityOne, Reveal, Everlaw, DISCO, and Nuix.

Table of Contents

• Why Chain of Custody Still Matters in AI-Driven eDiscovery
• Design Principles for AI Workflows That Preserve Chain of Custody
• A Defensible, AI-Assisted Workflow Using Microsoft 365 and Leading Platforms
• AI Review With Provenance: Logging Model Usage Without Contaminating Evidence
• Tooling Comparison: Chain of Custody and AI Capabilities
• Compliance, Security & Risk Mitigation with AI
• Hands-On Example: Generate a Chain-of-Custody Packet with Microsoft 365 + Power Automate
• Ethical & Regulatory Considerations
• Future Trends: Cryptographic Provenance and Confidential AI
• Quick Checklist: AI Workflows That Preserve Chain of Custody

Why Chain of Custody Still Matters in AI-Driven eDiscovery

Chain of custody is the verifiable record that evidence was collected, handled, processed, reviewed, and produced in a reliable manner. In an AI context, this spans far more than imaging and hashing. You must demonstrate:

• How data was placed on legal hold and preserved without alteration.
• What was collected, from whom, when, and by which tool or API.
• Processing steps, deduplication, culling, and search/analytics parameters.
• Review decisions and any AI-assisted classifications or summaries.
• Exports, productions, checksums, and delivery logistics.

Courts evaluate authenticity (Fed. R. Evid. 901, 902(13)-(14)), proportionality and preservation (FRCP 26 and 37(e)), and the reasonableness and transparency of your methods (Sedona Principles). AI does not change these obligations—it heightens the need for consistent governance because models can influence what is found, flagged, or excluded.

Best practice: Treat AI like any other legal technology: test, validate, document, and control it. Your workflow should enable a non-technical explainer to walk a judge or opposing counsel through what the system did, when, and why—supported by hardened logs and reproducible settings.

Design Principles for AI Workflows That Preserve Chain of Custody

Use the following design principles to keep AI-enabled eDiscovery defensible:

1. Immutability at the Source: Preserve originals using legal holds, retention policies, and WORM/immutability controls (e.g., Microsoft 365 retention with Preservation Lock or immutable cloud storage).
2. Deterministic Identification: Record stable identifiers (custodian, mailbox, site, device, source path), timestamps, and content hashes (e.g., SHA-256) wherever supported.
3. Separation of Evidence and Work Product: Store AI outputs, notes, and annotations in a distinct workspace or layer so underlying evidence remains pristine.
4. Provenance-by-Design: Capture model name/version, prompt, parameters, training context (if any), and the input/output mapping for each AI interaction.
5. Controlled Automation: Use role-based access control (RBAC), approvals, and least-privilege service accounts for automations (e.g., Microsoft Power Automate + Graph API).
6. Reproducibility: Save processing recipes (tokenization, OCR, language packs, de-NIST, dedupe, filters) and AI configurations (TAR/active learning thresholds, relevancy models).
7. Time Synchronization: Use trusted time sources for all systems to ensure consistent, court-ready timestamps across logs.
8. Comprehensive Auditing: Turn on premium audit logging where available (e.g., Microsoft Purview Audit) and protect logs with immutability and retention policies.
9. Encryption and Keys: Encrypt at rest/in transit; use customer-managed keys (CMK) or key escrow policies as permitted.
10. Human Oversight: Document human validation steps at every AI decision point that could impact discoverability or privilege.

A Defensible, AI-Assisted Workflow Using Microsoft 365 and Leading Platforms

Below is an end-to-end workflow leveraging Microsoft 365 eDiscovery (Premium), Microsoft Purview, and a review platform (e.g., RelativityOne, Reveal, Everlaw, DISCO, or Nuix). The emphasis: keep originals safe, record every action, and isolate AI analytics from source evidence.

1. Intake & Planning – Open a matter; define scope, custodians, and systems (M365, Slack, Google Workspace, endpoints).
2. Preservation – Place legal holds in Microsoft Purview eDiscovery (Premium) and any other systems (e.g., Slack/Google via connectors). Enable Preservation Lock where justified.
3. Collection – Collect in-place from M365 sources (Exchange, SharePoint, OneDrive, Teams) using Purview. Record job IDs, custodians, filters, and time range.
4. Processing – Process to review sets with hashing, de-NISTing, deduplication, OCR. Export log of processing options.
5. Transfer to Review – Export to a review platform using encrypted transfer; verify hashes and log chain-of-custody receipt.
6. AI-Assisted Review – Use TAR/active learning, NLP categorization, entity/PII detection. Store AI outputs separately from raw evidence.
7. Privilege & Redaction – Apply automated suggestions; require human QC; log redaction reasons and versions.
8. Production – Produce with Bates numbers, load files, and checksums; log delivery and recipient acknowledgments.
9. Reporting – Generate audit reports (holds, collections, processing settings, AI model usage, exports, hashes) and store in immutable storage.

AI Review With Provenance: Logging Model Usage Without Contaminating Evidence

AI can speed culling, classification, privilege detection, and summarization, but it must not compromise evidence integrity. Use these practices:

• Layered Workspaces: Keep raw evidence repositories separate from AI workspaces. Maintain a read-only copy of originals.
• Provenance Metadata: For each AI task, record:
  • Matter ID and data source(s) involved.
  • AI provider, model name/version, and configuration parameters.
  • Prompt or task definition; date/time; user or service account.
  • Input document IDs and output object IDs (cross-reference table).
  • Model confidence scores or thresholds used.
• Immutable Logs: Store logs and crosswalks in WORM/immutable storage with retention aligned to the matter.
• Re-Run Capability: Snapshot model configs so you can re-run and replicate results if methodology is challenged.
• Privilege Shielding: Ensure AI outputs live in privileged work-product containers with strict access controls.

Defensible documentation tip: Produce a “Methods Appendix” that clearly distinguishes data handling steps (preservation, collection, processing) from AI-driven analysis steps (classification, summarization), so the evidentiary chain remains separate and intact.

Tooling Comparison: Chain of Custody and AI Capabilities

Platform	Legal Hold & Preservation	Audit & Chain-of-Custody Controls	AI/Analytics Features	Integrations	Export & Hashing
Microsoft 365 eDiscovery (Premium)	In-place holds across Exchange, SharePoint, OneDrive, Teams; Preservation Lock	Purview Audit (standard/premium), RBAC, retention, sensitivity labels	Review sets, analytics, near-dup detection; integrates with external AI review	Connectors for Slack, Google Workspace, and more; Graph eDiscovery API	Exports with checksums; logs for collection/processing steps
RelativityOne	Legal Hold module, custodian communications	Comprehensive audit, workspace permissions, defensible processing	Active learning/TAR, NLP categorization, email threading	Office 365, G-Suite, Slack, many ingest formats	Production sets with Bates, load files, SHA hashes
Reveal (incl. Brainspace)	Integrations for holds; third-party legal hold options	Processing/analysis logs, robust audit trails	Concept clustering, entity extraction, visual analytics, TAR	Cloud/email/chat sources; connectors and APIs	Granular productions with hash validations
Everlaw	Hold integrations and workflow tools	Detailed activity logs and permissioning	Predictive coding, search term management, Storybuilder	Cloud ingest, collaboration tools	Trusted productions with audit-ready reports
DISCO	Holds via integrations	End-to-end auditability, defensible processing	AI-assisted review, auto-tagging, analytics	Cloud data sources, APIs	Production tools with checksums and logs
Nuix Discover	Holds via integrations; strong processing capabilities	Detailed processing/audit logs; evidence handling	Analytics, OCR, entity extraction, TAR	Enterprise data sources; forensics toolchain	Flexible production options with hashing

Note: Capabilities evolve; confirm current features and licensing before implementing.

Compliance, Security & Risk Mitigation with AI

A defensible program aligns technical controls with legal obligations:

• Frameworks: ISO/IEC 27001, SOC 2 Type II, NIST SP 800-53/171; privacy regimes like GDPR/CCPA where applicable.
• Legal Standards: FRCP (preservation and proportionality), Fed. R. Evid. 901/902 for authenticity, Sedona Principles, ABA Model Rules 1.1 (tech competence) and 1.6 (confidentiality).
• Technical Controls: Sensitivity labels, DLP, RBAC, conditional access, customer-managed keys, immutable logging, retention lock, access reviews, and vendor due diligence.

Risk	Potential Impact	Mitigating Controls
AI alters or overwrites evidence	Loss of authenticity; sanctions risk	Read-only evidence stores; separate AI workspace; immutability; RBAC
Insufficient AI provenance	Challenges to defensibility	Log model/version, prompts, parameters; immutable audit; reproducible configs
Data leakage to external AI	Privilege waiver; breach exposure	Private tenants; contractual restrictions; data residency; no training on tenant data
Weak hashing or missing checksums	Inability to verify integrity	SHA-256 at collection/export; verify at each transfer; store hash manifests
Over-collection or bias in culling	Burden; missed evidence; fairness concerns	Proportional scoping; human validation; search QA; TAR sampling and reports

Hands-On Example: Generate a Chain-of-Custody Packet with Microsoft 365 + Power Automate

This practical example shows how to standardize and accelerate documentation without compromising defensibility. It assumes Microsoft 365 eDiscovery (Premium), Microsoft Purview Audit, and access to Microsoft Graph eDiscovery APIs.

1. Start with a Matter Intake List in SharePoint:
   • Columns: Matter ID, Counsel, Custodians (people picker), Data Sources, Scope Dates, Sensitivity.
2. Automate Case Creation:
   • Use Power Automate to trigger on new intake items.
   • Call Microsoft Graph eDiscovery API to create a case and add custodians.
   • Record returned Case ID and Custodian IDs in SharePoint.
3. Apply Preservation:
   • Apply in-place holds for each custodian’s mailbox, OneDrive, Teams, and relevant SharePoint sites.
   • Write hold policy IDs and timestamps to the matter record.
4. Collect and Process:
   • Initiate collection jobs from Purview; capture job IDs, filters, and scope dates.
   • Process to a review set with OCR, dedupe, de-NIST; export the processing settings report.
5. Hash and Store Exports:
   • Export to encrypted ZIP; compute SHA-256 manifest.
   • Store the export and manifest in an immutable Azure Blob container with time-based retention.
   • Write the container URI and retention policy to the matter record.
6. AI-Assisted Documentation:
   • Have Power Automate assemble a “Chain of Custody Packet” Word document from a template: holds, collections, processing options, exports, hashes, and access list snapshots.
   • Use Copilot for Microsoft 365 to draft an executive summary from the packet and relevant emails/meeting notes. A human reviewer finalizes the summary.
7. Secure Collaboration:
   • Create a private Teams channel for the matter with sensitivity labels and guest access disabled.
   • Store the packet and logs in the channel’s SharePoint with “view-only” for non-admins.
8. Production and Closeout:
   • On production, add Bates range, production set hash, and delivery method to the packet.
   • Capture recipient acknowledgment and freeze logs according to retention.

Result: A repeatable, AI-accelerated process that still produces court-ready artifacts showing what happened, who did it, when, and with which tools—without ever altering source evidence.

Ethical & Regulatory Considerations

• Competence: ABA Model Rule 1.1 requires technological competence. Train teams on AI capabilities and limitations, and document validation procedures.
• Confidentiality: ABA Model Rule 1.6 applies to AI use. Ensure that vendor terms restrict training on your data and that data residency and encryption meet client obligations.
• Reasonableness & Proportionality: AI can help target collections and cull volumes, but keep proportionality front and center and validate with sampling.
• Transparency & Explainability: Be prepared to explain AI-aided decisions with logs, crosswalks, and methods appendices.
• Privilege: Keep AI work product segregated and logged; scrutinize redactions and privilege determinations with human QC.

Litigation adage for AI era: If it isn’t preserved and logged, it didn’t happen. Build your AI processes so the paper trail writes itself.

Future Trends: Cryptographic Provenance and Confidential AI

Several developments will strengthen chain of custody in AI-enabled matters:

• Cryptographic Provenance: Standards like C2PA and verifiable timestamps will attach tamper-evident lineage to files, transcripts, and AI outputs.
• Confidential Computing: Secure enclaves and confidential VMs protect data in use, enabling sensitive AI analytics without exposing raw contents.
• Provenance Tokens for AI: Model outputs will carry signed attestations (model version, parameters, source scope) for audit-friendly review.
• Integrated eDiscovery Graphs: Platforms will map entities, communications, and events into matter-specific graphs for faster, more explainable AI review.
• In-Place Review Matures: More analysis will occur where data resides (e.g., in Microsoft 365), minimizing exports and reducing chain-of-custody gaps.

Quick Checklist: AI Workflows That Preserve Chain of Custody

• Enable legal holds and Preservation Lock for relevant sources.
• Record stable IDs, timestamps, and SHA-256 hashes on collection and export.
• Store originals read-only; segregate AI outputs as privileged work product.
• Capture model/version, prompts, thresholds, and input-output mappings.
• Use immutable storage for audit logs, manifests, and chain-of-custody packets.
• Implement RBAC, least privilege, and approval workflows for automations.
• Validate search terms, sampling, and TAR thresholds with documented QC.
• Use encrypted transfers and verify checksums at every handoff.
• Maintain a methods appendix and produce audit-ready reports on demand.
• Regularly test and re-certify workflows against policy and case law updates.

AI can make eDiscovery faster, more accurate, and more economical—if you design for provenance and defensibility from the start. The keys are immutability, segregation of work product, comprehensive logging, and reproducibility. With disciplined workflows in Microsoft 365 and modern review platforms, your team can harness AI without sacrificing the evidentiary story a court requires.

Want expert guidance on improving your legal practice operations with modern tools and strategies? Reach out to A.I. Solutions today for tailored support and training.