Tune
Eliminate false positives.
Proprietary ML filters out noise so only the alerts that matter reach your team. Precision exceeding human accuracy, kept under your control.
Up to 97% fewer false positivesReal operational value
Numbers that move when Tune kicks in.
Tune learns from your specific alert patterns and CTI signals. The result is lower
noise, faster triage and confident decisions backed by measurable outcomes.
Every false positive takes about 32 minutes to handle. Tune removes up to 97% of them - drag to see what that frees up.
Based on 32 minutes per false positive and an average analyst cost of €7,400/month (≈ €46/hour). Tune removes up to 97% of false positives.
Capabilities
Everything Tune handles for you.
Six purpose-built capabilities, each tied to a specific moment in the alert lifecycle.
No fluff. No overlap.
Threat intelligence enrichment from multiple CTI sources
Every alert is cross-referenced against open and commercial CTI feeds. Recency, feed quality, consensus and MITRE phase are weighted into a single risk dimension.
Dynamic risk-based prioritization
Alerts are ranked against your actual environment rather than a static severity label. The ones most likely to matter to you bubble to the top automatically.
ML classification of FP, TP and undetermined
A purpose-built neural network classifies each alert as true positive, false positive or undetermined. Quality is tracked via F1 scores, confusion matrices and confidence monitoring.
1-click & auto-close of false positives
Confirmed false positives are closed in one click, or automatically when confidence is high enough. Your analysts spend their time on real threats, not on chasing noise.
Focus most risky alerts
The SOC sees the alerts that matter, in the order they matter. Lower noise, faster triage, more confident decisions, day in and day out.
Automatic Incident reporting
Easy and fast incident reporting based on multiple inputs done with AI and masking of data. NIS2 compliant out of the box.
How Tune works
From raw alert to priority decision.
Tune combines multi-source threat intelligence (CTI) enrichment with a purpose-
built neural-network model to classify alerts more accurately.
Built on a custom neural network
A custom neural network, purpose-built for cybersecurity data and grounded in university research. At onboarding it is fitted to your environment, then monitored continuously for accuracy.
Multi-source threat intelligence
Threat intelligence from multiple open and commercial feeds, scored across weighted dimensions into a single risk score - so alerts are prioritized for your actual environment, not a static severity label.
Why a purpose-built model, not an LLM
Built for classification, not language processing.
Why model fit matters
Too many features reduce efficiency. The goal is an optimal feature set.
How quality is monitored
Confusion matrices, F1 score, recall/precision tracking and confidence monitoring.
How threat context is added
Recency, feed quality, consensus, MITRE phase and targeting context.
Close false positives your way.
Two ways to clear a false positive - and you stay in control of both.
Close them yourself
Review alerts you have rated as false positives and close them in one click.
Automate the close
Create automation rules that auto-close false positives on specific alert rules.
Either way, you always keep control.
Same alerts. Better verdicts.
An illustration of how Seculyze classifies Microsoft Sentinel and Defender alerts. Five stages from raw alert to final verdict, with the noisy ones auto-closed.
The same intelligence, used twice.
Threat intelligence does double duty in Tune. It sharpens the model that classifies your alerts, and the same intelligence prioritizes everything that is not a confirmed true positive - so the threats that matter most rise to the top.
Sharpen the model
CTI signals are part of the feature set the neural network learns from, making classification more efficient and accurate.
Prioritize the rest
The same intelligence scores every alert that is not a confirmed true positive - including false positives - so analysts always see the most relevant threats first.
Recency
Fresh, active sightings outrank stale indicators.
Feed quality
Commercial and corroborated feeds carry more weight.
Confidence & consensus
Independent agreement across feeds raises reliability.
MITRE phase
Later attack stages score higher than early reconnaissance.
Targeting context
Industry, geography and sector relevance adjust the score.
More pillars
The full Seculyze product.
Tune is one of four pillars. Each works on its own. Together, they reshape how your SOC runs.
Calibrate
Right logs in, wrong logs out.
Explore CalibrateCost
Cut Sentinel spend without losing detection.
Explore CostUnify
One console for every tenant.
Explore UnifyBefore you ask.
How does Seculyze Tune reduce false positives in Microsoft Sentinel?
Tune classifies every Sentinel alert with a purpose-built neural network as true positive, false positive or undetermined. Confirmed false positives are closed in one click - or automatically when confidence is high. Customers see up to 97% fewer false positives while keeping a 99.7% recall rate.
Does Tune use a generic LLM like ChatGPT, or a purpose-built model?
Tune runs on a custom neural network designed specifically for SOC alert classification, not a general-purpose large language model. Model accuracy depends on the right alert features (rule context, threat intel, environment baseline, history) rather than model size.
How long does it take Tune to start working in our environment?
Tune is API-native to Microsoft Sentinel and Defender, so there is nothing to deploy. CTI enrichment is live from day one. The ML noise-reduction layer stabilises inside 72 hours of onboarding as the model learns your specific alert patterns.
What threat intelligence sources does Tune use?
Every alert is enriched against multiple open and commercial CTI feeds. Tune weighs recency, feed quality and indicator overlap when calculating risk, so analysts see actionable context instead of raw feed dumps.
Does Tune change how my analysts work inside Sentinel?
No. Tune sits natively on top of Sentinel - your team keeps the workflow they already know. Tune adds enrichment, classification and dynamic priority in the background, and your analysts investigate inside Sentinel as usual.
Can analysts override Tune's verdicts?
Yes. Every classification carries a confidence score and an explainability trail. Analysts can override any verdict in one click, and overrides feed back into the model so it gets sharper over time.
Ready when you are
Let AI do the heavy lifting.
See how Seculyze transforms a live Sentinel workspace in a 30-minute demo. No slides, just the product.