ml_train_anomaly_detector
Trigger training of an anomaly detection model for a specific table/field. [Write]
This record as markdown: /tools/nowaikit-servicenow-ai-toolkit/ml-train-anomaly-detector.md
What ml_train_anomaly_detector does on NowAIKit — ServiceNow AI Toolkit
AI agents invoke ml_train_anomaly_detector to trigger actions in NowAIKit — ServiceNow AI Toolkit. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
Why ml_train_anomaly_detector is rated High
Although labeled '[Write]' in the description, the core action is triggering an ML training pipeline. This is an Execute-category action because: (1) it initiates an external computational process (model training), (2) the effects depend on the data provided and ML algorithm behavior, and (3) retraining models can consume significant resources and overwrite previous model states.
From the tool's definition Tool description states 'Trigger training of an anomaly detection model' — triggers model training, which is a computational operation with side effects that cannot be easily reversed or undone.
Attacks that exploit this kind of access
The rule that runs ml_train_anomaly_detector safely
PolicyLayer is an MCP gateway: it sits between your AI agents and NowAIKit — ServiceNow AI Toolkit, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For ml_train_anomaly_detector, this is the rule to start with:
ml_train_anomaly_detector stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect NowAIKit — ServiceNow AI Toolkit, apply this rule, and every ml_train_anomaly_detector call is checked against it from then on.
Questions about ml_train_anomaly_detector
Trigger training of an anomaly detection model for a specific table/field. [Write]. It is categorised as a Execute tool in the NowAIKit — ServiceNow AI Toolkit MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the NowAIKit — ServiceNow AI Toolkit MCP server in PolicyLayer and add a rule for ml_train_anomaly_detector: 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 NowAIKit — ServiceNow AI Toolkit. Nothing to install.
ml_train_anomaly_detector is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the ml_train_anomaly_detector 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 ml_train_anomaly_detector. 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.
ml_train_anomaly_detector is provided by the NowAIKit — ServiceNow AI Toolkit MCP server (nowaikit). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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