Drop attempts stateful anomaly on database

Original query, unchanged.

let monitoredStatementsThreshold = 1;           // Minimal number of monitored statements in the slice to trigger an anomaly.
let trainingSlicesThreshold = 5;                // The maximal amount of slices with monitored statements in the training window before anomaly detection is throttled.
let timeSliceSize = 1h;                         // The size of the single timeSlice for individual aggregation.
let detectionWindow = 1h;                       // The size of the recent detection window for detecting anomalies.  
let trainingWindow = detectionWindow + 14d;     // The size of the training window before the detection window for learning the normal state.
let hotwords = pack_array('drop table', 'drop database'); // List of monitored hot words.
let processedData = materialize (
    AzureDiagnostics
    | where TimeGenerated >= ago(trainingWindow)
    | where Category == 'SQLSecurityAuditEvents' and action_id_s has_any ("RCM", "BCM") // Keep only SQL affected rows
    | project TimeGenerated, PrincipalName = server_principal_name_s, ClientIp = client_ip_s, HostName = host_name_s, ResourceId,
              ApplicationName = application_name_s, ActionName = action_name_s, Database = strcat(LogicalServerName_s, '/', database_name_s),
              IsSuccess = succeeded_s, AffectedRows = affected_rows_d,
              ResponseRows = response_rows_d, Statement = statement_s,
              Error = case( additional_information_s has 'error_code', toint(extract("<error_code>([0-9.]+)", 1, additional_information_s))
                    , additional_information_s has 'failure_reason', toint(extract("<failure_reason>Err ([0-9.]+)", 1, additional_information_s))
                    , 0),
              State = case( additional_information_s has 'error_state', toint(extract("<error_state>([0-9.]+)", 1, additional_information_s))
                    , additional_information_s has 'failure_reason', toint(extract("<failure_reason>Err ([0-9.]+), Level ([0-9.]+)", 2, additional_information_s))
                    , 0),
              AdditionalInfo = additional_information_s, timeSlice = floor(TimeGenerated, timeSliceSize)
    | extend hasHotword = iff(Statement has_any (hotwords), 1, 0)
    | summarize countEvents = count(), countStatements = dcount(Statement)
        , countStatementsWithHotwords = dcountif(Statement, hasHotword == 1)
        , countFailedStatementsWithHotwords = dcountif(Statement, (hasHotword == 1) and (Error > 0))
        , countSuccessfulStatementsWithHotwords = dcountif(Statement, ((hasHotword == 1)) and (Error == 0))
        , anyMonitoredStatement = anyif(Statement, (hasHotword == 1))
        , anySuccessfulMonitoredStatement = anyif(Statement, (hasHotword == 1) and (Error == 0))
        , anyInfo = anyif(AdditionalInfo, hasHotword == 1)
        , hotWord = anyif(extract(strcat_array(hotwords, '|'), 0, tolower(Statement)), hasHotword == 1)
        by Database, ClientIp, ApplicationName, PrincipalName, timeSlice,HostName,ResourceId   
    | extend WindowType = case( timeSlice >= ago(detectionWindow), 'detection',
                                           (ago(trainingWindow) <= timeSlice and timeSlice < ago(detectionWindow)), 'training', 'other')
    | where WindowType in ('detection', 'training'));
let trainingSet =
    processedData
    | where WindowType == 'training'
    | summarize countSlicesWithHotwords = dcountif(timeSlice, countStatementsWithHotwords >= monitoredStatementsThreshold)
        by Database;
processedData
| where WindowType == 'detection' 
| join kind = inner (trainingSet) on Database
| extend IsHotwordAnomalyOnStatement = iff(((countStatementsWithHotwords >= monitoredStatementsThreshold) and (countSlicesWithHotwords <= trainingSlicesThreshold)), true, false)
    , anomalyScore = round(countStatementsWithHotwords/monitoredStatementsThreshold, 0)
| where IsHotwordAnomalyOnStatement == 'true'
| project TimeGenerated = timeSlice, Database, ClientIp, ApplicationName, PrincipalName, HostName, ResourceId, countEvents, countStatements, countStatementsWithHotwords, anyMonitoredStatement, anyInfo, anomalyScore, hotWord
| extend Name = tostring(split(PrincipalName,'@',0)[0]), UPNSuffix = tostring(split(PrincipalName,'@',1)[0])