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eDiscovery AI: Maintaining Chain of Custody with Audit Logging

eDiscovery teams are starting to use AI for document triage, issue clustering, privilege review assistance, and early case assessment summaries. Those tasks are plausible and useful. They are also easy to mishandle. Once extracted text, case identifiers, or reviewer notes flow through a generic assistant, the organization can blur the line between the source platform's evidence record and the AI system's own processing history.

Keeptrusts is useful here because it can govern the AI segment of the workflow without pretending to replace the eDiscovery platform. A route can require attributable reviewers with RBAC, minimize case identifiers through Case Privacy, restrict provider handling with Data Routing Policy, record AI-path evidence with Audit Logger, and support defensible exports through Export Evidence for a Review.

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Primary audience​

  • Primary: Technical Leaders
  • Secondary: Technical Engineers, litigation support teams

The problem​

Chain of custody is a precision concept. In eDiscovery, the source review system, collection tooling, and evidence repository usually own the authoritative record of where documents came from and how they were handled. Problems start when AI is added informally and people assume the same rigor still applies automatically. A reviewer copies document text into a side assistant. A privileged cluster summary is generated without route identity. A later challenge asks which configuration was active, which provider handled the request, and whether case identifiers were minimized. If there is no answer, the AI step becomes hard to defend.

Another risk is category confusion. The gateway can preserve event evidence for AI requests, but that is not the same thing as replacing the native chain-of-custody system for the underlying documents. Teams need to state that boundary clearly. Keeptrusts can record who used the AI route, what policies fired, and how the route was constrained. It should complement, not supersede, the platform of record for evidence custody.

The practical consequence is that eDiscovery AI must be narrow. You want governed summarization and review support, not an untracked text side channel that bypasses documented review procedures.

The solution​

The strongest pattern is to make the AI route explicitly subordinate to the source review system.

Use rbac so every request is attributable to a named reviewer and matter. That lets the AI activity map back to the correct investigation or litigation support workflow.

Use case-privacy and, where needed, pii-detector so structured case identifiers and personal data are minimized before model processing. This matters because document excerpts often contain exactly the kind of identifiers that should not be copied broadly.

Use data-routing-policy to enforce provider handling conditions. If eDiscovery text must remain on local infrastructure or a zero-retention provider, the route should fail closed when those guarantees are missing.

Then use audit-logger and evidence export workflows to capture the AI interaction history. When stronger evidence requirements apply, Regulated Execution is helpful because it provides the signed-evidence story around sensitive workflows.

Implementation​

This example creates an eDiscovery review lane that records AI-path evidence while keeping the route tightly constrained.

pack:
  name: ediscovery-governance
  version: 1.0.0
  enabled: true

policies:
  chain:
    - rbac
    - case-privacy
    - pii-detector
    - data-routing-policy
    - audit-logger

policy:
  rbac:
    deny_if_missing:
      - X-User-ID
      - X-User-Role
      - X-Matter-ID
      - X-Review-Batch-ID

  case-privacy:
    action: redact

  pii-detector:
    action: redact
    detect_patterns:
      - name
      - email
      - phone
      - address

  data-routing-policy:
    require_zero_data_retention: true
    sanitize_before_provider: true
    tokenize_sensitive_fields: true
    on_no_compliant_provider: block
    log_provider_selection: true

  audit-logger: {}

That configuration should be paired with an operating rule: the review system remains the authoritative source for document custody, while the gateway records the governed AI interaction around that material.

The verification loop is short:

kt policy lint --file ./ediscovery-governance.yaml
kt gateway run --policy-config ./ediscovery-governance.yaml --port 41002
kt events tail --policy audit-logger
kt export create --format json --filter "policy=audit-logger,case-privacy"

Those commands provide two useful checks. The first confirms that route decisions are being recorded. The second creates a portable evidence packet for later review.

Results and impact​

The main gain is defensibility. eDiscovery teams can adopt AI for narrow review-support tasks without creating an undocumented side channel for case material.

That improves investigations and legal review in practical ways. Requests are attributable, case identifiers can be minimized, provider routing is constrained, and the AI path can be exported for incident or audit review. When reviewers later need to explain what happened, they have actual event evidence rather than reconstructed memory.

Just as importantly, the route makes the ownership boundary clearer. The document repository remains the system of record for custody of source evidence, while Keeptrusts provides the policy and evidence layer for the AI interaction. That separation is what makes the workflow sustainable under scrutiny.

Key takeaways​

  • Keeptrusts can govern the AI path in eDiscovery, but it should not be described as the sole chain-of-custody system for source evidence.
  • Use rbac to tie every AI request to a named matter and review batch.
  • Use case-privacy and pii-detector to minimize identifiers before model calls.
  • Use data-routing-policy to keep discovery text on approved provider paths.
  • Use audit-logger and evidence exports to support later review and handoff.

Next steps​