Verify the known-good once. Escalate the new risk the moment the evidence changes.
ClarityPipeline™ is an early-access, application-aware SOC platform for Elastic-led alert review. Evaluate it yourself in the console, bring it to your own alerts through a walkthrough, or embed it in an Elastic practice you already run.
Built by a security engineer from live analyst workflows. The product visuals on this page mirror the actual console; no customer logos or outcome metrics are shown because the platform is still in early access.
No form, no signup — the console runs in your browser on synthetic data.
Verify once
Reversible reason for identical, unchanged files.
Escalate drift
Changed signature, class, behavior, or posture returns to review.
Explain every call
Unknown stays unknown until evidence supports a call.
Analyst Workbench
Grouped pattern
Multiple ATT&CK tactics on one host
host srv-build-02
17 related alerts collapse into one story: shared lineage and a rare hash, not a shared rule.
Interpreter execution
Suspicious PowerShell arguments
ws-fin-0447 • m.alvarez • powershell.exe
Class lolbin: signer recorded but non-dispositive — judged on lineage, arguments, and workflow fit.
MCP review
AI host bridged into a local shell
ws-eng-0212 • mcp_shell_bridge
An MCP tool carrier reached shell capability. Review required before the workflow is trusted.
Application identity resolves before triage. Queue state, related activity, and next steps stay backend-driven — entities shown are illustrative.
One detection, walked end to end: identity resolves, related activity collapses into a story, evidence is weighed with its gaps priced in, and the decision arrives with its proof attached. Step through the same flow the console runs.
A rule hit is a starting point, not a decision.
Elastic owns ingress and base suppression. Everything after this point is what the platform adds around the alert — without moving raw telemetry out of its lane.
See it working
Walk one alert from detection to a defensible decision — evidence, blockers, and the gaps the platform refuses to paper over. Runs in your browser on synthetic data.
Process trees matter, but analysts judge the application. One classifier decides what kind of thing a binary is — and the class, not the signer, selects the trust model, so a trusted binary is not treated as trusted when it is being abused.
One classifier decides what kind of thing a binary is. The class — not the signer — selects the trust model.
binary_classification • live modeltrust model: identity_based
Signer verified → concern lowered
Identity evidence is decisive for vendor applications: verify once, then escalate only when signature, hash, or behavior drifts.
trust model: behavior_based
Judged on behavior, not signature
A valid signature says who built the binary, not that what it did was approved. Abuse of this class is behavioral: lineage, arguments, evasion indicators.
trust model: behavior_based
Wrong path + wrong parent → suspect
A masquerading binary inherits nothing from the name it wears. Name-derived identity keys are suppressed so a fake svchost never joins the real one’s clusters.
Every classification is enveloped: class_basis names the fields that decided it, and a coverage state says what telemetry backed it — a LOLBin with no behavioral telemetry renders as a blind spot, never clean.
The detail view is built around the question analysts actually need answered: what evidence lowers concern, what increases it, what validation is missing, and what should happen before the disposition changes.
Technical Assessment
ws-fin-0447 • m.alvarez • powershell.exe
Interpreter execution matched an approved deployment workflow: powershell.exe launched by northline-deploy.exe, chain owner verified, command transcript decoded, descendants retained.
Lowers concern
Increases concern
Evidence envelope
What decided this, where it came from, and how fresh it is — attached to the decision, not hidden behind it. Advisory only: disposition remains a human action.
Confidence breakdown
82% • High
Primary blocker
Missing Descendant Validation
Confidence stays bounded by the retained validation gap — blocks verified-benign until the descendant chain is validated in Elastic.
Analyst action
System assessment stays separate from the final analyst outcome. Confirm, escalate, or defer — every call is reversible, and the platform never closes an alert on its own.
Every decision carries a coverage envelope: which telemetry channels backed it, how fresh the measurement is, and exactly which claims a gap forfeits. “We saw nothing” is never displayed as “there is nothing.”
Telemetry coverage • ws-fin-0447
process
process lineage
library
DLL / side-load evidence
network
egress behavior
file
drop-and-execute
registry
persistence evidence
software inventory
identity, signer, hash
native audit (4688)
lineage corroboration
dns
domain egress attribution
wdac / code integrity
would-block verdicts
What a gap forfeits
registry stale → persistence evidence decides with reduced confidence. Remediation named: recycle the Defend package policy on this host.
Honest by construction
A refused read is never rendered as an empty fleet. WDAC would-block verdicts are shown as an integration gap — not yet flowing — never as silently clean.
Correlation keys are typed and class-aware: process entity, lineage, rare hash, command pattern, behavior fingerprint. What a key proves depends on what the binary is — and a shared rule never links anything by itself.
Related Activity
17 alerts → one storystrongly_related • 3
contextually_similar • 12
Shown, never counted
9 same-rule hits stay context_only — a shared rule is what “related” must stop meaning. Signer matches on LOLBins are recorded the same way: visible to the analyst, worth nothing to the link.
A single confidence number is easy to produce and impossible to argue with. Every number the console shows is assembled from named engines that each answer one question — and the tactic, alert-class, and entity models they reason over are countable.
22
Analytical engines
each returns a judgment, not a field
78
ATT&CK techniques
techniques and sub-techniques mapped
14
Enterprise tactics
the full tactic set, kill-chain weighted
242
Carrier binaries
LOLBAS-derived, provenance-stamped
20
Correlation key types
what may link two alerts
11
Telemetry channels
measured for coverage per host
The engines
Counted strictly: every engine below takes evidence and returns a security judgment. Formatting, export, and persistence services are not counted.
Identity
What is this thing?
Binary classification
the class — and whether signer trust applies at all
Process identity & legitimacy
whether the identity reads legitimate or suspect
Application awareness
carrier role, LOLBin abuse context, MCP and WDAC posture
Canonical application identity
one merged answer plus exploitation posture
Application attribution
vendor family, install-root coherence, software category
Lineage & correlation
What is it connected to?
Process tree signal
the parent/child trust verdict and primary blocker
Carrier-aware lineage
who really launched it, walking past pass-through shells
Correlation key scoring
whether a link is dispositive, supporting, or context
Cross-alert campaign correlation
the related set, its strength, and its reason
Behavior clustering
cluster membership and exception eligibility
Behavior
What did it actually do?
Behavior fingerprinting & drift
a stable behavioral identity, and when it drifts
Behavior interpretation
the behavior type and expected follow-on
Evasion detection
which of eight evasion behaviors are present
Decision
How much should we believe it?
Alert class profiling
which evidence model an alert is scored under
Decision signal weighting
the winning signal, the blocker, the confidence
Confidence calibration
score to probability, only if a holdout gate passes
Detection fidelity
how often this rule has actually been right
False-positive discriminator mining
why a rule is noisy, as a checkable pattern
Detection reasoning
the confirmation steps that separate noise from target
Coverage & risk
What are we allowed to claim?
Telemetry coverage
what the cluster can see, complete-or-refuse
Host coverage
what this alert's narrative may claim
Risk & impact
likelihood and impact as two axes, never blended
Tactic mapping
Tactics are not a badge on the alert. Each one routes to a lifecycle phase and carries the specific question that phase has to answer.
Entry
how it arrived
How the activity arrived on the host.
Execution
what ran
What ran and who asked for it — and what visibility the activity avoids or removes.
Actions
what it did
What survives a reboot, which boundary was crossed, which secret was reached, what was enumerated, what was destroyed.
Movement
where it went
Which remote boundary was crossed, what was staged, what left the host, and which channel stayed open.
Containment
how to stop it
Playbook containment for the observed shape — analyst-executed, never automatic.
Reconnaissance and Resource Development carry kill-chain weight for ordering and impact but map to no execution phase — they are counted in the 14, and deliberately not drawn above.
Alert types
A threshold alert has no process to reason about. Scoring it with the process lens would invent confidence, so each class is scored under the evidence it actually has — and capped accordingly.
Full process pipeline
Signer, lineage, descendants, workflow fit. The only class where the process lens applies.
Constituent sequence
Measured tactic sequence across the grouped alerts. Confidence capped at 82.
Threshold magnitude
Observed count against the rule's own threshold, plus tactic alignment. Capped at 82.
Entity context
Severity, risk score, and first-occurrence context. Capped at 65.
Entity context
Entity history where no process lens exists. Capped at 65.
Entity context
Anything with no process to reason over. Capped at 65.
Entity types
Every key carries how much it proves, and that depends on the class. A signer match is real evidence between vendor applications and worth nothing on a LOLBin.
Dispositive
links on its own
Supporting
corroborates only
Context only
never scores
Most queues treat each alert like the first time the team has seen it. ClarityPipeline keeps the repeat work out of the way, then forces fresh analyst attention when the evidence no longer matches the known-good pattern.
Analysts re-check signed, unchanged files because the queue forgets what the team already verified.
A trusted executable can still be a LOLBin, masquerade, injection path, or BYOVD signal.
A confident score is not useful when the reason, source fields, and review path are hidden.
The model phrases explanations. It never reaches a verdict, closes an alert, deploys a detection, or acts without approval — and it is optional: switch it off and the deterministic pipeline returns the same decision.
Elastic remains the source for alert ingress, base suppression, rule context, and validation. ClarityPipeline adds application intelligence, related activity, and rule-tuning handoff around that workflow — broader SIEM and EDR intake is an integration path, not a claim of current parity.
Consulting is the on-ramp into the platform: assessments, detection review, and implementation sprints scoped to your stack and analyst queue.
Identify queue friction, repeated known-good review, noisy detections, and process gaps that slow analysts down.
Review detection quality, false positive patterns, coverage gaps, and application-context blind spots across SIEM or EDR.
The core offer: design, integrate, and tune the ClarityPipeline platform around Elastic-led alert review and the adjacent tools in scope.
ClarityPipeline™
Bring a representative alert workflow, an Elastic detection problem, or a repeated triage pain point. The first conversation is about whether a walkthrough, assessment, or implementation sprint is the right next step.
Share the current stack, the alert pattern that wastes the most time, and whether Elastic is part of the workflow. Sanitized examples are enough to start.
Share your SOC workflow, security stack, and the repeated alert problem you want to reduce.
