CFTC Rule 5.17(z) conflict-of-interest pre-trade check. Analyzes a Kalshi market's metadata to detect whether trading it would expose decision-makers (election candidates, government officials, athletes, regulators) to insider-trading liability. Pattern-matches against the Kalshi April 2026 disci...
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AI agents call pg_kalshi_conflict_of_interest_check to retrieve information from Predictionguard without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though pg_kalshi_conflict_of_interest_check only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"pg_kalshi_conflict_of_interest_check": {}
}
}See the full Predictionguard policy for all 41 tools.
These attack patterns abuse exactly the kind of access pg_kalshi_conflict_of_interest_check gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
CFTC Rule 5.17(z) conflict-of-interest pre-trade check. Analyzes a Kalshi market's metadata to detect whether trading it would expose decision-makers (election candidates, government officials, athletes, regulators) to insider-trading liability. Pattern-matches against the Kalshi April 2026 disciplinary cases (Moran/Klein/Enriquez) and the CFTC Van Dyke complaint. Returns ALLOW/MONITOR/WARN/BLOCK recommendation + 0-100 score + reference cases. Cannot identify actual trader identity (Kalshi public API anonymizes users) — pair with internal surveillance for full enforcement.. It is categorised as a Read tool in the Predictionguard MCP Server, which means it retrieves data without modifying state.
Register the Predictionguard MCP server in PolicyLayer and add a rule for pg_kalshi_conflict_of_interest_check: 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 Predictionguard. Nothing to install.
pg_kalshi_conflict_of_interest_check 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 pg_kalshi_conflict_of_interest_check 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 pg_kalshi_conflict_of_interest_check. 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.
pg_kalshi_conflict_of_interest_check is provided by the Predictionguard MCP server (https://feedoracle.io/predictionguard/mcp/). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 41 Predictionguard tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
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