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Government Research AI: Protecting Pre-Publication Data

Government labs, policy research offices, and federally funded programs increasingly want AI help with literature synthesis, experiment summaries, and draft briefings. The value is obvious. A model can condense a grant package, compare methods sections across dozens of papers, or help researchers turn notes into a publishable outline. The risk is equally obvious: the same prompt can expose unpublished findings, controlled technical details, or export-sensitive context before an agency has cleared the material for wider sharing.

Keeptrusts gives research programs a useful governance boundary around that workflow. Instead of assuming researchers will remember what not to paste, the gateway can require identity with RBAC, block controlled terms with DLP Filter, restrict provider eligibility through Data Routing Policy, and record reviewable evidence with Audit Logger.

Use this page when​

  • You support government or public-interest research teams using AI before papers, reports, or datasets are approved for release.
  • You need a practical control pattern for pre-publication data, controlled unclassified information, or draft technical findings.
  • You want a workflow aligned to Government, Data Residency, and Pass Compliance Audits.

Primary audience​

  • Primary: Technical Leaders
  • Secondary: Technical Engineers, research-security reviewers

The problem​

Pre-publication government research is awkward for AI because it lives between clearly public and clearly classified information. A national lab draft may not be classified, but it can still contain unpublished methods, embargoed performance data, procurement-sensitive information, or references to external partners that should not leave the organization. A policy analyst preparing a briefing may pull in internal comments, draft recommendations, or data that is accurate enough to matter but not yet cleared for dissemination.

That means the usual "just redact names" mindset is too weak. The sensitive element is often not a social security number or an email address. It is the fact that the experiment exists, the timing of a result, the identity of a program office, or the structure of a still-unreleased recommendation. Once that material reaches an overly broad provider route, the organization has created a disclosure path that did not exist in the ordinary publication process.

Government research also has procedural risk. Many programs must show who handled a draft, who reviewed it, and what controls were applied before anything moved outside the originating team. If AI assistance happens through a generic chatbot with shared credentials, that chain becomes hard to reconstruct. The organization cannot easily answer basic questions such as which team accessed the route, whether the route was restricted to an approved provider set, or whether obvious controlled markers were blocked before egress.

Keeptrusts is useful here because it treats the AI path as an access and routing problem first. It does not certify scientific correctness or classify the document for you. What it can do is enforce that only approved users reach the route, that prompts containing protected markers are blocked or sanitized, and that provider selection obeys declared handling requirements. That is the right boundary for pre-publication governance.

The solution​

For agency research, the strongest pattern is a dedicated pre-publication route with explicit identity checks, term blocking, and provider restrictions that fail closed when metadata is missing.

Start with rbac. Every request should carry user, role, and program identity so the route is attributable. Shared generic access undermines later evidence review.

Use dlp-filter to block markers that are operationally meaningful in research environments: terms such as CUI, FOUO, draft release language, program codes, or internal project identifiers. dlp-filter is especially useful when the sensitive issue is a phrase or label rather than a personal identifier.

Use data-routing-policy to enforce the provider side. If pre-publication prompts must stay on a local model, in a sovereign environment, or on a zero-retention target, that requirement belongs in the route itself rather than in training material people may ignore.

Then use audit-logger so the organization can review activity later and export evidence if a publication review board, security office, or oversight body asks how the route was governed. If the workflow is document-heavy, Citation Verifier can help ensure summaries stay grounded in the supplied source material instead of inventing confident claims about unpublished work.

Implementation​

This example restricts a pre-publication research route to approved analysts and local-only or zero-retention targets while blocking common internal release markers.

pack:
  name: government-research-prepublication
  version: 1.0.0
  enabled: true

providers:
  targets:
    - id: agency-research-local
      provider: ollama
      model: llama3.1:70b
      base_url: http://localhost:11434
      data_policy:
        zero_data_retention: true
        training_opt_out: true
        retention_days: 0
        in_memory_only: true
        sanitized: true
        accepts_tokenized_input: true
        allow_internet_egress: false
        local_only_processing: true

policies:
  chain:
    - rbac
    - dlp-filter
    - data-routing-policy
    - citation-verifier
    - audit-logger

policy:
  rbac:
    deny_if_missing:
      - X-User-ID
      - X-User-Role
      - X-Program-ID
    roles:
      research-analyst:
        allowed_tools:
          - summarize
          - compare_sources
          - draft_outline
      publication-reviewer:
        allowed_tools:
          - summarize
          - review_release_readiness

  dlp-filter:
    blocked_terms:
      - controlled unclassified information
      - for official use only
      - draft not for public release
      - pre-publication findings
    action: block
    fuzzy_matching: true
    max_distance: 1

  data-routing-policy:
    require_zero_data_retention: true
    require_in_memory_only: true
    sanitize_before_provider: true
    allow_internet_egress: false
    local_only_processing: true
    on_no_compliant_provider: block
    log_provider_selection: true

  citation-verifier:
    require_sources: true
    require_source_match: true
    min_confidence: 0.8
    output_action:
      unverified_action: block

  audit-logger: {}

The important design choice is that the route governs draft handling, not publication approval itself. A model can help create a review package, but release authority still belongs to the agency's established process.

The quick validation loop is short and practical:

kt policy lint --file ./government-research-prepublication.yaml
kt gateway run --policy-config ./government-research-prepublication.yaml --port 41002
kt events tail --policy dlp-filter
kt events tail --policy data-routing-policy

Those checks answer the two questions that matter most. Did the request stay inside the approved route, and did the route refuse to use an ineligible provider?

Results and impact​

The immediate benefit is that pre-publication AI use stops being an informal exception. Instead of a researcher copying sensitive draft material into a convenience tool, the workflow moves through a governed lane with explicit access rules and provider constraints.

That reduces accidental disclosure risk in three ways. First, only approved roles reach the route. Second, obvious internal release markers are blocked before they travel further. Third, provider selection fails closed when the target cannot prove it meets the research program's handling requirements.

The evidence story improves as well. Research administrators and security reviewers can inspect logged decisions, export evidence, and show that draft assistance was constrained by policy rather than left to individual judgment. For public-sector research programs, that is often the difference between an AI pilot that stalls in review and one that can continue under documented controls.

Key takeaways​

  • Pre-publication research AI is primarily a disclosure-boundary problem, not only a privacy-redaction problem.
  • Use rbac so research access is attributable to named users and program lanes.
  • Use dlp-filter for release markers and controlled terms that matter operationally in agency environments.
  • Use data-routing-policy to make local-only or zero-retention routing enforceable.
  • Add citation-verifier when summaries or briefings must stay grounded in supplied draft material.

Next steps​