Optimize a compliance review queue using current OFAC exact matches, future exposure probabilities, exposure value, relationship tier, recency, and reviewer capacity. Use this when review_items and review_budget are known; use compliance_exposure_forecast for probability-only analysis or complian...
Risk signalsHigh parameter count (19 properties)
Part of the AurelianFlo server.
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AI agents use compliance_queue_optimize to create or modify resources in AurelianFlo. Write operations carry medium risk because an autonomous agent could trigger bulk unintended modifications. Rate limits prevent a single agent session from making hundreds of changes in rapid succession. Argument validation ensures the agent passes expected values.
Without a policy, an AI agent could call compliance_queue_optimize repeatedly, creating or modifying resources faster than any human could review. PolicyLayer's rate limiting ensures write operations happen at a controlled pace, and argument validation catches malformed or unexpected inputs before they reach AurelianFlo.
Write tools can modify data. A rate limit prevents runaway bulk operations from AI agents.
{
"version": "1",
"default": "deny",
"tools": {
"compliance_queue_optimize": {
"limits": [
{
"counter": "compliance_queue_optimize_rate",
"window": "minute",
"max": 30,
"scope": "grant"
}
]
}
}
}See the full AurelianFlo policy for all 11 tools.
These attack patterns abuse exactly the kind of access compliance_queue_optimize gives an agent. Each links to the full case and the policy that stops it:
Other write tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Optimize a compliance review queue using current OFAC exact matches, future exposure probabilities, exposure value, relationship tier, recency, and reviewer capacity. Use this when review_items and review_budget are known; use compliance_exposure_forecast for probability-only analysis or compliance_edd_report for a case memo.. It is categorised as a Write tool in the AurelianFlo MCP Server, which means it can create or modify data. Consider rate limits to prevent runaway writes.
Register the AurelianFlo MCP server in PolicyLayer and add a rule for compliance_queue_optimize: 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 AurelianFlo. Nothing to install.
compliance_queue_optimize is a Write tool with medium risk. Write tools should be rate-limited to prevent accidental bulk modifications.
Yes. Add a rate_limit block to the compliance_queue_optimize 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 compliance_queue_optimize. 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.
compliance_queue_optimize is provided by the AurelianFlo MCP server (https://api.aurelianflo.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 11 AurelianFlo tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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