Detection over Sequences

Every other rule in the library judges one moment — an incident is a story

Every code block below is real, and every output block is its actual output (the run lives in base/test/system/_secdetect_narration_demo.ring; the guard suite is base/test/system/security_detection_narrated.ring, 33 assertions). Incident analysis I3 — doc/design/SOFTANZA_INCIDENT_ANALYSIS.md.

The shape the library never had

But "five failed logins inside a minute" is not a structure — it is a sequence. "A failed sign-in, then a reach for a production secret" is a story with an order and a clock. No rule shape in the library could express that, which is why I2's ledger, useful as it is, still needed a reader.

Three shapes, deliberately few

oD = StzDetection("credential-stuffing")
oD.WhenKind("auth.login.failed").PerActor().Repeats(5).Within(60000)

oD2 = StzDetection("guess-then-reach")
oD2.WhenKind("auth.login.failed").ThenKind("secret.reveal.refused")
   .BySameActor().Within(300000)

oD3 = StzDetection("cloned-authenticator")
oD3.WhenKind("auth.passkey.clone_suspected").OnAnyOccurrence()

BURST (n of a kind inside a sliding window), SEQUENCE (b within a window after a), ANY (one occurrence is already the story — a cloned authenticator needs no second opinion). Three, not thirty: each is computable over a bounded ledger without inventing a query language, and together they cover the shapes the library's own catalog can produce.

PerActor() matters more than it looks: credential stuffing is per account, not per installation. The guard proves the difference — nine failures spread across two accounts fire the grouped detection once (for the account that actually burst) and the ungrouped one once (for the installation).

What the library ships knowing

Detection content is usually something you buy or write. Softanza ships eight detections over its own catalog, because it knows its own event names:

StzDefaultDetectionSet().Show()

Detection set softanza-default -- 8 detection(s).
  Detection credential-stuffing [error] -- 5x auth.login.failed within 60000ms, per actor
    repeated authentication failures against one account
  Detection secret-probing [error] -- 3x secret.reveal.refused within 300000ms, per actor
    an actor repeatedly reaching for secrets it may not have
  Detection escalation-attempts [error] -- 3x capability.refused within 60000ms, per actor
    an actor repeatedly attempting acts beyond its capabilities
  Detection guess-then-reach [error] -- auth.login.failed then secret.reveal.refused within 300000ms, same actor
    a failed sign-in followed by a reach for a secret -- the classic shape of a stolen-credential attempt
  Detection cloned-authenticator [error] -- any auth.passkey.clone_suspected
    a signature counter that did not advance
  Detection replayed-request [error] -- any sig.nonce.replayed
    a nonce reused for the same key -- an active replay
  Detection forged-request [error] -- any sig.signature.forged
    a signature that did not verify -- forged, tampered, or wrong key
  Detection replayed-assertion [error] -- any sso.assertion.replayed
    a SAML assertion presented twice

The whole arc, on a real run

Nothing scripted below: a real stzAuth refuses five real bad passwords, a real stzSecretStore refuses a real sandboxed actor, the I2 seams fill the ledger, and the detections read it.

StzOpenSecurityLedger(512)
# ... five failed logins for 'victim', then an LLM actor reaches for stripe-live ...
aFindings = StzDefaultDetectionSet().CheckAgainst(StzSecurityLedgerQ())

the ledger holds 7 event(s)
  [error] credential-stuffing/victim -- 5 x auth.login.failed within 60000ms by 'victim' -- repeated authentication failures against one account
  [error] guess-then-reach/victim -- auth.login.failed then secret.reveal.refused within 68ms by 'victim' -- a failed sign-in followed by a reach for a secret -- the classic shape of a stolen-credential attempt

Two findings for two real stories, from events nobody wrote by hand.

A defect the demo caught: one story, one finding

The first implementation reported that second story five times — each of the five failed logins pairs with the same secret reach, so five pairs matched. Technically correct, operationally useless: a detection that repeats itself per matching pair is how alert fatigue starts, and alert fatigue is how real alerts get ignored.

The fix is one finding per actor per detection, and the guard now pins it so it cannot come back. Worth stating as a rule: a detection's job is not to report every match, it is to report every story.

The corroboration law

One anomalous read is a rumor.

Corroborated() marks a detection that must not raise an error-severity alarm on a single signal. If the matched evidence spans fewer than two distinct event kinds, the finding is emitted as a warning that says so:

lonely-signal -- auth.passkey.clone_suspected occurred (single signal: reported as a warning until a second, independent signal corroborates it)

The sequence shape satisfies corroboration by construction — it spans two kinds by definition — so the same flag leaves it at error. This is the perf system's fifth law (a self-check compares two independent measurements) wearing security clothes.

It fails the same build

Verdicts are findings in the unified rule shape, so no adapter is needed anywhere:

oRep = new stzRuleReport("nightly-ci")
oRep.Ingest(aFindings)
? "IsSound() = " + oRep.IsSound()

IsSound() = 0
Rule report 'nightly-ci': 2 finding(s), 2 error(s), 0 warning(s) -> UNSOUND (errors present)

A tripped detection now fails CI exactly as a capability escalation or a code-rule violation does — one gate over code, agents, security, workflow, orgcharts, and now incidents. And a quiet ledger keeps the gate sound, which the guard also checks: a detector that cannot stay silent is not a detector.

Honest limits

Detections read the retained window — bounded means forgetting, and a campaign slower than the ledger's capacity outruns it unless sealed out (I1's export). Windows use wall time, so a clock adjustment moves them; ordering within the process is monotonic, but cross-process correlation is wall-based by necessity. There is no baseline learning, no frequency modelling, no threat intel: these are declared patterns, which is exactly what makes them reviewable and version-controllable.

I4 gives them a heartbeat — the sentinel that runs detections on a cadence, fires on transitions only, and opens an incident from what fired.

Proved by

The article names these scenario guards of the library:

• security_detection_narrated.ring(opens in a new tab)

Related reading

• Code First, Subscribe Later
• The Seams
• The Governed Response
• Governing a Project's Security

Cite

Mansour Ayouni. "Detection over Sequences". Softanza narrations, 2026. https://mayouni.github.io/stzsite/en/narrations/stz-security-detection-narration.html

Not run: its code touches files, the network, the clock or chance. The article is published as written; its blocks are illustrations. The code of the articles is Haro code. Where an article was written before the language took its present name, the name was changed and nothing else. Genre, area and series read from the file's name, the year from its first commit: derived, until the article carries its own header. The file on GitHub(opens in a new tab)