Suspicious subject with long procedurally generated text blob

type.inbound
// "#" symbol, and then letters, numbers or more symbols (-#)
and regex.imatch(subject.subject, ".*#[a-z0-9-#]+$")
// not JUST letters
and not regex.imatch(subject.subject, ".*#[a-z-]+$")
// not JUST numbers
and not regex.imatch(subject.subject, ".*#[0-9-]+$")
and not any(beta.ml_topic(body.current_thread.text).topics,
            .name in (
              "Professional and Career Development",
              "Reminders and Notifications"
            )
            and .confidence == "high"
)

// and 1 other suspicious element
and 2 of (
  any(recipients.to, strings.icontains(sender.display_name, .email.domain.sld)),
  (
    regex.imatch(sender.display_name, ".*#[a-z0-9-#]+$")
    and not regex.imatch(subject.subject, ".*#[a-z-]+$")
    and not regex.imatch(subject.subject, ".*#[0-9-]+$")
  ),
  any(ml.nlu_classifier(subject.subject).intents,
      .name == "cred_theft" and .confidence == "high"
  ),
  body.current_thread.text is null
)

// standard negations
and not profile.by_sender_email().any_messages_benign
and not profile.by_sender_email().solicited
and (
  (
    sender.email.domain.root_domain in $high_trust_sender_root_domains
    and not headers.auth_summary.dmarc.pass
  )
  or sender.email.domain.root_domain not in $high_trust_sender_root_domains
)