Known threat meme: when SIEM alerts don’t stop real incidents

“Great news - the attacker’s IP was in our threat feed two months ago.” “Perfect, so the feed automatically blocked it?” “…No, it automatically created a Jira ticket we closed as ‘won’t fix’ during sprint grooming.”

It's like having a distributed logging system with petabytes of data indexed in Elasticsearch, but your alerting rules are just 'if (threat.severity > 9 && Math.random() > 0.99) { maybe_investigate(); }' - the classic problem of collecting everything but analyzing nothing until the post-mortem

When your threat detection model has 100% recall on identifying suspects but 0% precision on preventing incidents - classic case of collecting all the training data but never deploying the model to production. It's like having perfect observability with Prometheus scraping every metric at millisecond intervals, yet still getting paged at 3 AM because nobody configured the alerting rules to actually *do* something actionable

In enterprise security, “already known to us” means the detection SLO is green, there’s a Grafana panel and a risk register entry - and prevention ships next quarter

FBI surveillance: 100% recall on every citizen, 0% precision on actual threats

In SOC-speak, “known to us” means the SIEM rule fired, got globally suppressed during tuning, was filed as a low‑sev Jira with no owner, but still made the QBR slide as “coverage.”