Forecast future OFAC wallet exposure for a wallet set using stored OFAC snapshot diffs when available, listedOn backfill when honest, or an explicit caller prior; returns current exact-match baseline, metadata-weighted per-wallet risk, and report-shaped output. Use this when current screening is ...
Risk signalsHigh parameter count (19 properties)
Part of the AurelianFlo server.
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AI agents use compliance_exposure_forecast 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_exposure_forecast 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_exposure_forecast": {
"limits": [
{
"counter": "compliance_exposure_forecast_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_exposure_forecast 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.
Forecast future OFAC wallet exposure for a wallet set using stored OFAC snapshot diffs when available, listedOn backfill when honest, or an explicit caller prior; returns current exact-match baseline, metadata-weighted per-wallet risk, and report-shaped output. Use this when current screening is not enough; use wallet_ofac_batch for current hit status only.. 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_exposure_forecast: 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_exposure_forecast 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_exposure_forecast 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_exposure_forecast. 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_exposure_forecast 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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