Secure Configuration Guide for AI Coding Assistants in Research Environments

1. Why Researchers Need a Dedicated Configuration Guide

Research scenarios differ significantly from general software engineering. Ordinary software projects usually emphasize delivery speed, feature completeness, and system stability. Research code, however, also carries requirements such as data compliance, experimental reproducibility, auditable results, and traceable processes. This is especially critical when working with subject information, medical records, unpublished manuscripts, patent materials, ethics review documents, or restricted datasets. In such cases, even a single inappropriate input, exposed path, or retained log entry can cause irreversible risk.

Therefore, researchers should not start by asking only whether an AI coding assistant can write code. They should first answer three questions:

1. Which data may enter the assistant’s context?
2. Which file access rights and execution permissions does the assistant have?
3. When the assistant generates code, modifies files, or runs commands, how can those actions be recorded, reviewed, and rolled back?

If these questions are addressed early, AI coding assistants can become a controlled part of the research workflow rather than an external black box with unclear provenance, excessive permissions, and uncontrollable output.

2. Understanding the Tool Layers of AI Coding Assistants

Many researchers assume that differences between coding assistants depend mainly on how strong the underlying model is. In practice, when the underlying model capabilities are similar, the outer tool architecture often has a greater impact on the actual results. This architecture includes how context is acquired, which tools can be called, where permission boundaries are set, which execution environment is used, and what logging and audit mechanisms are available.

AI coding assistants can generally be grouped into several common layers:

1. Conversational question-and-answer assistants
   They are useful for explaining concepts, suggesting code snippets, and helping diagnose errors. Their main limitation is that they usually work only with content manually provided by the user and cannot proactively read project files.

2. In-editor suggestion tools
   They are useful for completing functions, generating local code, and explaining selected code fragments. They depend on the current editing environment and have a limited context range. Even so, developers should avoid sending sensitive code fragments into completion requests.

3. Terminal-executing agents
   These can read project files, modify code, run scripts, inspect output, and make iterative corrections based on execution results. They are more capable, but also riskier. They must be paired with path restrictions, command approval, and log auditing.

4. Automated workflow platforms
   These connect code generation, test execution, log collection, and report output. They are suitable for repetitive research tasks, such as data cleaning pipelines, experiment reproduction script generation, and batch result summarization. This form requires stricter configuration governance.

For researchers, the key to choosing a tool is not to pursue the “strongest form,” but to match the task’s risk level. Low-sensitivity tasks may use interactive assistants. Tasks involving internal data, restricted code, or high-privilege operations should prioritize controlled environments that support read-only modes, limited paths, and auditable logs.

3. Installation and Initialization: Build an Isolated Environment First

Before installing and configuring an AI coding assistant, it is advisable to set up an independent research working environment. The goal is to prevent the assistant from accessing sensitive material unrelated to the current task and to avoid accidental actions that could affect real experimental data.

3.1 Create a dedicated workspace

Create a separate directory for each research project. Store raw data, sanitized data, code, logs, outputs, and temporary files separately. At a minimum, the workspace should include the following directories:

project/
  ├── raw/             # raw sensitive data; should not enter external assistants by default
  ├── sanitized/       # sanitized or summarized data
  ├── code/            # analysis and modeling code
  ├── outputs/         # generated charts, reports, and model results
  ├── logs/            # execution logs
  └── sandbox/         # isolated area that may be accessed by the assistant

The sandbox directory should be the primary accessible area for the AI coding assistant. The raw data directory should be denied by default.

3.2 Use a least-privilege account

Do not run an AI coding assistant under an account with full system administrator rights. Create a normal user or project-specific account for research tasks and limit its file read/write scope. For cloud resources, combine access controls, sub-account policies, and project-level permission models to prevent a single tool invocation from spreading across the entire cloud environment.

3.3 Isolate the dependency environment

Research code often depends on specific package versions. Create an independent runtime environment for each project and lock dependency versions to prevent environment drift that could make results irreproducible. For example, record the required components in a dependency manifest instead of allowing the assistant to install unknown packages freely in any environment.

3.4 Limit network access

For tasks that process internal or controlled data, network access by the assistant should be denied by default. Open only specific domains or services when necessary and compliant. For cloud execution scenarios, use network policies, access control lists, and security groups to restrict outbound traffic.

3.5 Cloud isolation recommendations

If research computing is performed in the cloud, teams can establish isolation boundaries using common capabilities on cloud platforms such as Baidu AI Cloud. For example:

• Use object storage to store sanitized data and result files;
• Use access control mechanisms to restrict project member permissions;
• Use logging services to record assistant invocations and command execution;
• Use monitoring and alerting to detect abnormal access or high-frequency writes;
• Use container platforms or isolated compute resources to run experimental tasks.

These practices are not tied to one specific tool. They help integrate the AI coding assistant into a unified research governance framework.

4. Initialization Configuration: Encode Security Boundaries into Policy

Instead of allowing the assistant to run with default open permissions, create an explicit policy file. The following illustrative configuration explains the intended approach:

research_assistant:
  mode: assisted
  workspace: ./sandbox
  allowed_paths:
    - ./sandbox/code
    - ./sandbox/sanitized
  denied_paths:
    - ./raw
    - ./outputs/final_report
  external_network: deny
  execution_policy: ask
  allowed_actions:
    - read_file
    - edit_file
    - run_test
  denied_actions:
    - delete_directory
    - install_unknown_dependency
    - export_env_keys
  data_classification:
    - internal_only
    - no_personal_identifier
    - no_restricted_clinical_data
  logging:
    enabled: true
    retention_days: 90
  human_review:
    required_for:
      - modify_code
      - execute_script
      - export_result

The value of this configuration is that it turns “whether it is allowed” from an individual judgment into an institutional constraint. For researchers, especially in team settings, the policy file should be placed under version control and checked before each project starts.

5. Data Classification and Sensitive Information Isolation

Data isolation is the first principle for researchers using AI coding assistants. Research data can be divided into four levels:

1. Public data
   Data that can be freely used, such as publicly published statistics, open datasets, and public literature abstracts.

2. Internal data
   Data restricted to internal team use, such as unpublished experimental designs, internal discussion drafts, and draft code.

3. Restricted data
   Data subject to collaboration agreements, privacy commitments, or access restrictions, such as controlled questionnaire data or restricted datasets.

4. Controlled data
   Data containing personal identifiers, subject information, medical records, genetic information, children’s data, or other regulated information. In principle, such data should not be directly provided to external AI coding assistants.

When deciding whether input is acceptable, use a simple rule: if you would not place the data in a public cloud synchronization space, do not paste it into an AI coding assistant dialog. More precisely, any material that could create compliance risk after unloading, migration, or sharing should be treated as not suitable for input by default.

Common sensitive fields

These include, but are not limited to:

• Names, phone numbers, email addresses, ID numbers, and home addresses;
• Medical records, diagnosis records, imaging reports, and gene sequences;
• Subject identifiers and their mapping relationships;
• Full texts, figures, and supplementary materials of unpublished manuscripts;
• Technical details disclosed before patent filing;
• Collaboration agreements, confidentiality agreements, and commercial terms;
• Internal server addresses, access keys, and database connection strings.

Sanitize data before use

If the assistant must process a piece of logic, sanitize or summarize the data first. For example, provide field structures, statistical characteristics, or pseudocode instead of real records. The following illustrative function shows this boundary:

def sanitize_for_assistant(record):
    if contains_personal_identifier(record):
        return None
    if contains_restricted_field(record):
        return None
    return {
        "field_types": infer_field_types(record),
        "row_count": len(record),
        "sample_schema": build_schema_only(record),
        "notes": "no raw values included"
    }

The core idea of this illustrative expression is to let the assistant understand the data structure rather than directly access raw data content.

6. Do Not Let Credentials, Keys, or Connection Information Enter Conversations

Many security problems do not come from model capabilities but from habitual pasting by users. When configuring environments, researchers should avoid entering access keys, database passwords, tokens, certificate contents, or connection strings directly into the assistant.

Safer practices include:

1. Store temporary credentials in environment variables and mask them in logs;
2. Use cloud key management services to centrally manage long-term keys;
3. Rotate keys regularly;
4. Reference credential names in code without exposing real values;
5. Sanitize logs to prevent command output from echoing sensitive parameters;
6. Prevent the assistant from reading areas of configuration files that may contain secrets.

If the assistant accidentally reads a file containing credentials, immediately stop the task, rotate the relevant credentials, and check logs and access records.

7. Execution Permissions: Read-Only by Default, Approve Write Operations

Terminal-executing agents can modify files, run commands, and install dependencies. These capabilities are valuable for research automation but can also cause damage. Use a read-only-first policy:

• Allow reading code and sanitized data by default;
• Require human confirmation before modifying files;
• Check parameters and target paths before executing scripts;
• Prohibit recursive deletion;
• Prohibit unapproved dependency installation;
• Prohibit uploading data to unknown external addresses;
• Set alerts for long-running or resource-intensive tasks.

In team settings, introduce a two-person review mechanism. For example, a data processing script generated by the assistant should first be reviewed by one team member for logic, then submitted to a sandbox environment for execution. Execution results should record input version, code version, runtime parameters, and output checksums.

8. Configuration Recommendations for Typical Research Workflows

8.1 Literature review

AI coding assistants can help summarize abstracts, organize terminology lists, and generate review frameworks. They should not be used to upload full papers subject to copyright restrictions. Researchers can provide titles, abstracts, keywords, and personal notes, then ask the assistant to generate a structured outline.

8.2 Data cleaning

Do not ask the assistant to process raw sensitive data directly. First generate field descriptions, missing-value distributions, outlier rules, or sanitized examples. Then ask the assistant to write cleaning logic. Run the resulting scripts in an isolated environment and keep them separated from the raw data.

8.3 Experiment reproduction

The key to research reproduction is consistency across environment, parameters, data, and procedures. The assistant can generate reproduction scripts from experiment records, but researchers must manually check dependency versions, random seeds, input paths, output formats, and evaluation metrics. It is advisable to produce a “reproduction checklist” rather than relying only on the code provided by the assistant.

8.4 Code review

The assistant is suitable for checking boundary conditions, exception handling, numerical stability, statistical assumptions, and code readability. For high-risk logic, such as significance testing, model training, causal inference, or clinically relevant analysis, require the assistant to provide evidence, possible error sources, and alternatives. Do not accept conclusions without verification.

8.5 Writing and polishing

The assistant can be used for language polishing, structural organization, and expression optimization. Do not input unpublished full texts, verbatim reviewer comments, or confidential material from collaborators. Provide only abstracts, paragraph themes, or anonymized descriptions.

9. Reliability Assessment: Do Not Judge Only by Whether Output Looks Plausible

The output of an AI coding assistant may look fluent, but that does not mean it is correct. Researchers need acceptance criteria rather than relying on intuition.

Assess outputs from the following dimensions:

1. Executability
   Can the code run in the specified environment? Are dependencies missing? Are there path errors?

2. Reproducibility
   Are random seeds, parameters, versions, input/output paths, and execution logs recorded?

3. Data security
   Does the process touch restricted directories, output sensitive fields, or leave credentials in logs?

4. Reasonableness of results
   Do statistical results make sense? Are there unit errors, sample bias, overfitting, or misuse of metrics?

5. Verifiability of references
   If the assistant provides references, formulas, or method names, verify them against the original sources. Avoid accepting citations that cannot be traced.

A practical method is to build a small test set. For example, prepare several sanitized examples with known answers, ask the assistant to complete the same task, and check whether the output is stable, whether path restrictions are respected, and whether the assistant proactively asks for missing information. After passing these tests, expand the scope of use.

10. Common Questions and Governance Checklist

Common issues in practical use include:

• Pasting raw data directly to save effort;
• Pasting keys or connection strings into conversations;
• Giving the assistant access to the entire disk;
• Failing to record the source of code modifications;
• Failing to verify references generated by the assistant;
• Using personal credentials in shared environments;
• Ignoring sensitive echoes in logs.

Use the following checklist before starting a project:

• Have raw data and inputtable data been separated?
• Has an independent sandbox directory been created?
• Is default external network access disabled?
• Are the assistant’s read/write paths restricted?
• Is human approval required for critical write operations?
• Is logging enabled and a retention period recorded?
• Have logs been sanitized?
• Is the environment confirmed to contain no keys, tokens, or connection information?
• Have output results been reviewed by a human?
• Have versions of code, data, and configuration been recorded?

11. Conclusion: Bring the Assistant into Research Governance

For researchers, the real value of AI coding assistants is not to replace research judgment, but to reduce the cost of repetitive work. However, the rigor of research requires treating them as system components that need configuration, constraints, and auditing, rather than as temporary tools to be used casually.

The safest usage principles are as follows: understand the capabilities of the underlying model, but control the permissions of the outer tool; isolate sensitive data as much as possible; require code execution to leave traces; and treat outputs as aids to judgment rather than substitutes for the researcher’s own professional verification. If data classification, permission boundaries, log auditing, and human review are integrated into daily workflows, AI coding assistants can become reliable parts of the research infrastructure rather than sources of risk.