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gdpr_check

Identify sensitive personal fields in an index via rules, names, and sampled values. Dry-run suggests mask fields; apply merges them into config, logs the change, and writes a compliance report.

Instructions

Run the DSGVO plausibility check on an index: find sensitive fields.

Reads the index mappings, samples a few documents, and classifies the fields by three heuristics in decreasing certainty: custom rules from config.yaml (gdpr_checker.custom_patterns), field-name patterns (source.ip, user.name, host.hostname, user.email, ...), and sampled values (an actual value like 192.168.1.100 reveals an IP even when the field name does not).

Priorities: IPs, usernames and e-mails are directly personal (high); hostnames and agent ids are indirectly personal (medium); free-text fields that embed personal data are flagged as such. Fields already in the anonymization mask_fields are reported as covered, not re-suggested.

With apply=true the suggested fields are merged into anonymization.mask_fields of config.yaml (KLAXON_CONFIG), the action is appended to gdpr_check.log, and gdpr_compliance_report.json is written. The change takes effect for the running server on restart unless KLAXON_ANONYMIZATION_MASK_FIELDS is set, which always overrides the file. apply=false (default) is a dry run: suggestions only, nothing changed.

Args: index: Index or datastream pattern, e.g. "wazuh-events-v5-*". prefix: Restrict to a field namespace, e.g. "user." or "source.". sample_docs: Documents to sample for content analysis. Defaults to KLAXON_GDPR_SAMPLE_SIZE (10). 0 disables sampling. apply: When true, merge the suggested fields into config.yaml and log. exclude: Field names to skip (e.g. internal fields without GDPR relevance). as_json: When true, return a machine-readable JSON report instead of the table.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
applyNo
indexYes
prefixNo
as_jsonNo
excludeNo
sample_docsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description fully discloses behavior: reading mappings, sampling documents, using three heuristics, classifying fields into high/medium/flagged, handling already-covered fields, and explaining the apply=true side effects (config merge, logging, report writing, restart requirement, and environment override). This is exemplary transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear paragraphs and an Args list. Every sentence contributes value: heuristics, priorities, apply behavior, and parameter details. It is detailed yet concise, with no wasted words or redundancy with the schema.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 6 parameters and significant side effects, the description covers all necessary contextual aspects: input parameters, heuristics, classification logic, side effects, environment variable override, and default behavior. Since an output schema exists, return value details are not required, but the description still mentions the report format and filenames.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description becomes the sole source of parameter meaning. It compensates comprehensively by listing all six parameters in an Args section, explaining each one's purpose, defaults (e.g., sample_docs defaults to KLAXON_GDPR_SAMPLE_SIZE, apply defaults to false), and examples (index pattern, prefix format, exclude semantics).

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool 'runs the DSGVO plausibility check on an index: find sensitive fields.' It details three heuristics and clearly distinguishes itself from sibling tools (search, schema, etc.) by its unique GDPR-specific function.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context on when to use this tool (for GDPR checks and finding sensitive fields) and explains the dry-run vs. apply modes. It does not explicitly mention alternatives or exclusions, but the purpose is unambiguous and self-contained.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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