How to Secure AI Research Agents

AI research agents query databases, call external APIs, execute computational experiments, and write results — any ungated action can compromise data integrity, break reproducibility, or exfiltrate proprietary research data. SafeClaw by Authensor enforces deny-by-default policies on every action your research agent attempts, ensuring datasets are read but not modified, external API access is controlled, and every computational step is logged in a hash-chained audit trail for reproducibility. Policy evaluation completes in sub-milliseconds, adding no overhead to research workflows.

Quick Start

npx @authensor/safeclaw

Creates a .safeclaw/ directory with deny-all defaults. Your research agent cannot read datasets, call APIs, or write results until you define explicit allow rules.

Data Integrity Protection

Research depends on data integrity. An AI agent that can modify source datasets can silently corrupt your results:

# .safeclaw/policies/research-agent.yaml
rules:
  - id: allow-read-datasets
    action: file.read
    effect: allow
    conditions:
      path:
        pattern: "data/raw/*/.{csv,parquet,json,hdf5}"
    reason: "Agent can read raw datasets"

- id: block-modify-raw-data
    action: file.write
    effect: deny
    conditions:
      path:
        pattern: "data/raw/**"
    reason: "Raw data is immutable — never writable by agents"

- id: allow-write-processed
    action: file.write
    effect: allow
    conditions:
      path:
        pattern: "data/processed/*/.{csv,parquet,json}"
    reason: "Agent can write to processed data directory"

- id: allow-write-results
    action: file.write
    effect: allow
    conditions:
      path:
        pattern: "results/*/.{csv,json,png,pdf,tex}"
    reason: "Agent can write results and figures"

- id: deny-all-writes
    action: file.write
    effect: deny
    reason: "Default deny for all other file writes"

Reproducibility Through Audit Trails

Every action your research agent takes is recorded with cryptographic integrity, creating a reproducible execution trace:

# .safeclaw/config.yaml
audit:
  enabled: true
  hashChain: true
  retention: "10y"  # Research data retention requirements
  fields:
    - timestamp
    - action
    - effect
    - agentId
    - experimentId
    - policyRuleId
    - requestDetails
    - inputHash      # Hash of input data for reproducibility
    - outputHash     # Hash of output data

This audit trail lets you reconstruct exactly what the agent did during any experiment — which datasets it read, what computations it ran, and what results it produced. Every entry is hash-chained, so tampering is detectable.

External API Gating

Research agents often need to call external APIs for data enrichment, literature search, or computation. Gate each API to prevent unauthorized data sharing:

rules:
  - id: allow-pubmed-search
    action: api.call
    effect: allow
    conditions:
      endpoint:
        pattern: ".ncbi.nlm.nih.gov//esearch*"
      method: "GET"
    reason: "Agent can search PubMed"

- id: allow-arxiv-search
    action: api.call
    effect: allow
    conditions:
      endpoint:
        pattern: "export.arxiv.org/api/query"
      method: "GET"
    reason: "Agent can search arXiv"

- id: allow-semantic-scholar
    action: api.call
    effect: allow
    conditions:
      endpoint:
        pattern: "api.semanticscholar.org"
      method: "GET"
    reason: "Agent can query Semantic Scholar"

- id: block-data-upload
    action: api.call
    effect: deny
    conditions:
      method: "{POST,PUT,PATCH}"
      body:
        sizeGreaterThan: 1024
    reason: "Block large data uploads to external services"

- id: deny-all-apis
    action: api.call
    effect: deny
    reason: "Default deny for all other API calls"

Computational Experiment Gating

When research agents execute code or run experiments, gate the execution environment:

rules:
  - id: allow-python-scripts
    action: shell.execute
    effect: allow
    conditions:
      command:
        pattern: "python {scripts,experiments}/*/.py*"
    reason: "Agent can run approved Python scripts"

- id: block-package-install
    action: shell.execute
    effect: deny
    conditions:
      command:
        pattern: "{pip install,conda install}"
    reason: "Package installation requires researcher approval"

- id: block-network-tools
    action: shell.execute
    effect: deny
    conditions:
      command:
        pattern: "{curl,wget,scp,rsync}"
    reason: "Network transfer tools are blocked"

- id: deny-all-shell
    action: shell.execute
    effect: deny
    reason: "Default deny for all other shell commands"

Why SafeClaw

• 446 tests covering research-specific policy patterns including data integrity edge cases
• Deny-by-default — no data access or computation until explicitly permitted
• Sub-millisecond evaluation — negligible overhead on compute-intensive research pipelines
• Hash-chained audit trail — cryptographically verifiable execution traces for reproducibility
• Works with Claude AND OpenAI — same integrity policies regardless of which model powers your research agent

Cross-References

• Research Agent Recipe
• How to Prevent AI Agent Data Exfiltration
• Tamper-Proof Audit Trail Explained
• How to Audit AI Agent Actions