By RODEO INFOSEC CORP · October 22, 2026 · ~7 min read
Every incident teaches you something. An analyst chases down a suspicious process, confirms a malicious binary, or spots a command-and-control pattern in network traffic. The value of that work is not just closing the ticket — it is making sure the same thing is caught automatically the next time it appears, anywhere in the environment. That translation, from a one-off finding to a durable, reusable detection, is detection engineering. It is also where most security teams quietly fall behind.
Three languages, three layers
There is no single place to catch an adversary, so there is no single detection format. Three open standards cover the layers that matter, and each answers a different question:
- Sigma — the lingua franca for log-based detection. A Sigma rule is a vendor-neutral YAML description of a suspicious pattern in event logs (a process spawning a shell, a registry key change, an anomalous login). Its point is portability: one Sigma rule can be converted into the native query language of whatever SIEM or log platform you run, so detection logic is not locked to a product.
- Suricata — detection on the network. Suricata is an IDS/IPS engine whose rules match live traffic: protocols, payload content, TLS and certificate details, file transfers, beacon patterns. This is where you catch command-and-control and lateral movement on the wire, independent of what any endpoint reports.
- YARA — detection on files and memory. A YARA rule is a set of string and byte patterns plus a boolean condition used to classify and hunt for malware in files, processes, and memory images. It is the workhorse of malware analysis, triage, and incident response.
Together they span log, network, and file/memory telemetry. A serious detection program produces all three, and maps each rule to the adversary behavior it covers — usually via MITRE ATT&CK — so coverage is measurable rather than anecdotal.
Why hand-writing detections does not scale
Writing a good rule is skilled work. The author has to understand the behavior, express it precisely, and tune it so it fires on the real thing without drowning the SOC in false positives. That expertise is scarce, and it is the bottleneck:
- Volume. New techniques, new malware, and new findings arrive faster than a small team can hand-author rules for them. The backlog becomes permanent.
- Consistency. Hand-written rules vary with whoever wrote them — different structure, different naming, uneven ATT&CK mapping, uneven quality.
- Coverage decay. Environments change and adversaries adapt. Rules that were accurate a year ago quietly stop matching, and nobody notices until something gets through.
- Translation tax. The same logic often has to be re-expressed for the SIEM, the network sensor, and the file scanner — three times the work for one behavior.
From finding to detection: what “generating” means
This is exactly the kind of structured, repetitive translation that machines are good at. Generating a detection means taking a confirmed finding or a described technique and emitting a candidate rule in the right language: Sigma for a log pattern, a Suricata signature for a network behavior, a YARA rule for a file or memory artifact — already structured, named consistently, and mapped to the relevant ATT&CK technique.
Done well, this collapses the translation tax and clears the backlog: one confirmed behavior can yield coverage across all three layers in seconds rather than days, with consistent metadata throughout. The analyst’s scarce expertise moves from typing rules to deciding which ones are worth keeping.
The quality problem, and why a human stays in the loop
A generated rule is a draft, not a decision. The failure mode of automated detection is not too few rules — it is too many bad ones: noisy signatures that bury real alerts, or brittle ones that miss slight variations. A generated detection has to be reviewed by someone who can judge it, tested against historical telemetry to see what it would actually have fired on, and tuned before it ever reaches production.
This is the same principle that runs through everything RODEO does: automation drafts and proposes; a human approves anything that changes what the system watches or does. Generation makes the expert faster; it does not replace the expert’s judgment. Every promoted rule should carry a record of who reviewed it and what it was tested against — the same control-level evidence trail described in the note on automating CMMC 2.0.
Keeping detections mapped and maintained
Detection content is not write-once. It is code, and it should be treated like code: versioned, reviewed, and continuously measured against an ATT&CK coverage map so you can see your gaps. When a rule starts misfiring or a technique evolves, you want to find and revise it deliberately — and in many of the environments this matters most, you want all of that to work with no internet connection at all, a constraint covered in the note on running security AI offline.
How R-O-D-E-O approaches it
Turning findings into detections is a built-in step of the R-O-D-E-O loop: attack surfaces a real, exploitable behavior; the platform turns that finding into detection content in Sigma, Suricata, and YARA, mapped to ATT&CK; and the defense side responds, with analyst sign-off on anything high-impact. Because the models run on local weights, detection generation works inside air-gapped boundaries, and every promoted rule is human-gated and logged. The whitepaper walks through how the detection pipeline fits the rest of the architecture, and the platform ships in editions sized to the environment.
The goal is not to generate the most rules. It is to make sure every lesson your team learns the hard way becomes a detection that holds — fast enough to keep up, and reviewed enough to trust.