ai_act_training_data_audit
As a CTO, audit AI training datasets for EU AI Act compliance with bias detection and regulatory risk assessment. Inputs: dataset identifier (Hugging Face ID or URL) and optional risk thresholds. Outputs: compliance score, bias metrics, regulatory warnings, and source references. Ideal for pre-de...
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What ai_act_training_data_audit does on Gapup Mcp
AI agents call ai_act_training_data_audit to retrieve information from Gapup Mcp without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
async | boolean | — | If true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client ti |
dataset_id | string | Yes | Hugging Face dataset identifier or direct URL to dataset |
risk_threshold | number | — | |
include_bias_metrics | boolean | — |
Parameters from the server's own tool schema.
Why ai_act_training_data_audit is rated Low
This tool reads and analyzes training data to produce compliance and bias reports. While the analysis itself may involve computation, the core function is data retrieval and assessment—determining compliance status and metrics without altering datasets or triggering external operations.
From the tool's definition Tool performs 'audit' and 'bias detection' on datasets with outputs of 'compliance score, bias metrics, regulatory warnings, and source references'—all informational retrieval.
Attacks that exploit this kind of access
The rule that runs ai_act_training_data_audit safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Gapup Mcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For ai_act_training_data_audit, this is the rule to start with:
ai_act_training_data_audit is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Gapup Mcp, apply this rule, and every ai_act_training_data_audit call is checked against it from then on.
Questions about ai_act_training_data_audit
As a CTO, audit AI training datasets for EU AI Act compliance with bias detection and regulatory risk assessment. Inputs: dataset identifier (Hugging Face ID or URL) and optional risk thresholds. Outputs: compliance score, bias metrics, regulatory warnings, and source references. Ideal for pre-deployment risk evaluation. Pass async:true to avoid timeout. It is categorised as a Read tool in the Gapup Mcp MCP Server, which means it retrieves data without modifying state.
ai_act_training_data_audit accepts 4 parameters: async, dataset_id, risk_threshold, include_bias_metrics. Required: dataset_id. The full parameter table on this page comes from the server's own tool schema.
Register the Gapup MCP server in PolicyLayer and add a rule for ai_act_training_data_audit: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Gapup Mcp. Nothing to install.
ai_act_training_data_audit is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the ai_act_training_data_audit rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for ai_act_training_data_audit. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
ai_act_training_data_audit is provided by the Gapup MCP server (https://mcp.gapup.io/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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