The most effective method to reduce false positives from a threat intelligence-based rule is to increase its specificity. Threat intelligence feeds often contain indicators with varying confidence and severity levels. By modifying the YARA-L rule to only trigger on indicators where the Unified Data Model (UDM) field graph.metadata.threat.severity is marked as "critical" or "high," you filter out lower-confidence or informational indicators that are common sources of noise. This directly addresses the issue of excessive false positives while retaining the core function of using high-fidelity threat intelligence for alerting.

A. Converting the rule to a dashboard does not reduce false positives; it only changes the output from an alert to a visualization, failing to solve the underlying issue of the noisy rule logic.

C. The riskscore is typically an outcome of a detection, contributing to an entity's overall risk. Using it as a primary filter within the rule itself is not the standard or most direct way to tune the rule's event-matching logic.

D. Increasing the match window from 2 minutes to 24 hours would likely increase, not decrease, the number of false positives by expanding the pool of events for correlation.

1. Google Cloud Documentation

Unified Data Model field list: The graph.metadata.threat.severity field is defined as part of the UDM schema to represent the severity of a threat associated with an indicator. This confirms the field's existence and intended use for filtering.

Source: Google Cloud

Chronicle Security Operations

"Unified Data Model field list"

Section on Threat.

2. Google Cloud Documentation

YARA-L 2.0 language syntax: The syntax for YARA-L rules allows for conditions in the events section to filter events based on the values of specific UDM fields. A condition like $event.graph.metadata.threat.severity = "CRITICAL" is a standard implementation.

Source: Google Cloud

Chronicle Security Operations

"YARA-L 2.0 language syntax"

Section: "Events section".

3. Google Cloud Documentation

Detection engine best practices: Official guidance on reducing false positives in rules emphasizes adding more specific criteria to the rule logic. Filtering on threat severity is a primary example of making a rule more specific and reducing noise from lower-priority intelligence.

Source: Google Cloud

Chronicle Security Operations

"Best practices for rules and detections"

Section: "Reduce false positives".