The Problem: Detection Velocity Can't Keep Pace with Threat Intel
Enterprise banks running Splunk as their primary SIEM face a structural bottleneck: detection engineers are skilled but scarce, and the pipeline from a new MITRE ATT&CK technique to a production-grade rule is long. Engineers must interpret threat intel, translate it to SPL, validate syntax, backtest against historical data, tune false-positive rates, and shepherd the rule through change management. At $80k–$300k per year for retainer-grade detection content support, the cost reflects genuine scarcity — and even with external support, new detection time-to-production averaging three weeks means significant windows of undetected exposure.
How an AI Agent Approaches It
The agent mines threat intel discussions from your Slack channels and the MITRE ATT&CK mapping spreadsheets your team already maintains. It uses those as its source of truth to generate Splunk SPL candidates tuned to your environment's field naming and index structure. Critically, it backtests each rule against 90 days of event data before surfacing it — giving the detection lead fidelity signals and false-positive counts rather than untested SPL. High-confidence candidates are queued for promotion; ambiguous ones are flagged for review. Time-to-production for new detections typically drops from three weeks to two days.
The Business Case
Faster detection coverage directly reduces risk exposure — shorter windows between a new attacker technique appearing and a rule blocking or alerting it. For a regulated bank, that has real audit and regulatory weight: examiners want to see detection coverage that keeps pace with current threats. Beyond risk, the capacity multiplier matters: a detection engineering team of three or four people can maintain coverage across a far larger ATT&CK matrix when rule generation and backtesting are automated. The agent typically reduces detection content engineering effort by 65–83% and is live and producing queued candidates within about six weeks.
How does the agent handle false-positive tuning for our specific environment?
Because it backtests against your own 90-day event data, the fidelity signals it surfaces are specific to your environment's noise floor — not generic benchmarks. The detection lead sees actual hit counts and can adjust suppression logic before promoting to production.
Can the agent update existing rules, or only generate new ones?
Both. When threat intel indicates a technique evolution or an existing rule starts generating excessive false positives, the agent can produce revised SPL and queue the update alongside new-rule candidates for the detection lead's review.