FIND THE SIGNAL.
Silence the noise.

Alpha Level's self-learning detection finds the threats your rules are missing while our statistical models clear up to 95% of the alerts that are overwhelming your SOC. Lower risk, greater efficiency, and much less worrying.

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The Alpha Level Approach

Sleep Better.
Alpha Level is finding the attacks you didn't know about.

Two AI technologies working seamlessly: statistical refinement to eliminate noise and self-learning anomaly detection with targeted agentic AI investigation only where it counts to find what rules miss.

From noise to signal
Alerts
Telemetry
ALERT REFINERY
Statistical models — cheap, fast, accurate. Cuts up to 95% of noise.
AUTONOMOUS HUNTER
Self-learning models find the threats your rules will never catch.
Context Engine
Prepares graph context for efficient investigation.
AGENTIC AI
For only the rarest, most concerning, most ambiguous.
SOC
Insider Threats Lateral Movement Credential Theft Living Off The Land Ransomware Precursors Zero-Day Behavior Data Exfiltration Privilege Escalation
Customer Validation

Trusted by enterprise
security operations

"

We view Alpha Level as a critical capability that enables us to scale our incident response to meet increasing attack volume. They're uniquely aligned with our goal of building a data-centric security operation that fully leverages available telemetry and alerts to address exponential growth in attack volume and sophistication.

Senior Director of Cybersecurity Operations
Fortune 100 Life Sciences Company
"

Alpha Level's machine learning helps by building models that understand what 'good' looks like in our data, allowing us to detect threats faster and more accurately. This not only speeds up detection but strengthens our overall security in an increasingly complex digital environment.

Adam Cartwright, CISO
Australia Post
Products

Available separately or together

Each component solves a distinct problem and integrates with your existing security stack. Deploy one or both with no displacement and no migration.

Novel Threat Detection
Autonomous Hunter
Self-Learning Anomaly Detection

Applies self-learning anomaly detection to raw telemetry. Builds behavioral baselines across users, devices, network traffic, and cloud — then surfaces the attacks no rule will ever find, correlating weak signals into high-fidelity findings.

  • Detects zero-days, living off the land, insider threats, novel TTPs
  • Self-learning models — no signatures, no manual tuning
  • Coverage for unmanaged devices via network monitoring
  • Correlates events into full kill-chain context
  • Delivers explanations and recommended actions, not just alerts
  • No endpoint agent required
Noise Elimination
Alert Refinery
Statistical Alert Labeling

Ingests alerts from any source and applies statistical models to label up to 95% of alerts — instantly, without analyst involvement. Handles the volume problem so only credible, high-signal events reach your team.

  • Eliminates up to 95% of alerts before analysts see them
  • Statistical models — economical, fast, and highly accurate
  • Ingests from any source: SIEM, EDR, data lake, APIs
  • Zero rules to write, zero detectors to tune
  • Labels surface natively in tools your SOC already uses
Case Studies

Hunting Trophies

These are the attacks your current rules are missing right now.

Case 01
Autonomous Hunter
20 IPs. Zero Alerts. Invisible Brute Force.
Autonomous Hunter uncovered a distributed brute force campaign across 20+ international IPs using the OrgIdWsTrust2 legacy auth endpoint — completely invisible to Microsoft SigninLogs, MFA, and Conditional Access. Standard SIEM rules are blind to this vector.
20+
Attacker IPs across 8+ countries
0
Alerts generated in SIEM
33
Failed logons via legacy endpoint
100%
Detection gap in SigninLogs
Legacy authentication endpoints are active blind spots in every SIEM. Without behavioral anomaly detection, these attacks run indefinitely — undetected.
Case 02
Alert Refinery
One of the World's Largest MDRs was Overrun.
Alert Refinery labeled the queue first, scoring every alert statistically and clearing duplicates and known-benign patterns with auditable evidence, no detector changes required.
17.9B
Events across customer telemetry
375,000+
Incidents generated in one month
1,000+
Analysts and engineers on staff
56.9%
Incidents cleared as duplicate or benign
They'd already tried a full AI SOC platform, couldn't run at their scale, and it never made it past the first eval.
Case 03
Autonomous Hunter
DCsync Attack. Uninvestigated for 35 Days.
Two DCsync alerts (MITRE ATT&CK T1003.006) sat in the SIEM with status "New" for over a month — unread, uninvestigated. Autonomous Hunter correlated 4 identity events across 48 hours and reconstructed the full Kerberos kill chain, delivering a verdict in minutes.
35
Days alerts sat uninvestigated
4
Events correlated across 48 hrs
4,273+
Normal vs. 2 anomalous events
Minutes
To verdict vs. analyst hours
Detection without investigation is just noise. Autonomous Hunter builds the case in minutes — not just an alert, a verdict.
Case 04
Autonomous Hunter
Server 2008 R2. 255 Citrix Users. Zero Monitoring.
Autonomous Hunter discovered a Windows Server 2008 R2 (end-of-life since January 2020) serving 255 Citrix users daily with zero telemetry. Found through behavioral network analysis alone — no agent required. Zero patches since 2020.
2009
OS release year (EOL Jan 2020)
255
Daily Citrix users exposed
0
Process events in SIEM
0
Patches applied since EOL
Every environment has servers that fell through the cracks. Autonomous Hunter surfaces them before an attacker finds them first.
Case 05
Autonomous Hunter
110 MB Over Telnet. Median is 17 KB.
Two telnet sessions transferring 100+ MB each to a legacy on-premises device — 6,000x the normal median — off-hours, by a near-ghost user with only 2 total logons ever. No endpoint agent. No Azure presence. Invisible to every traditional tool.
17 KB
Normal median per session
110 MB
Detected transfer (6,000x median)
2
Ghost user's total logons ever
0
Traditional tools could see this
No one knew what this device was, who was using it, or why 200 MB moved over telnet off-hours. Required immediate investigation.
Case 06
Autonomous Hunter
700 Cleartext LDAP Binds. 6 Accounts Exposed.
A management server was authenticating to all 11 domain controllers via cleartext LDAP (port 389) — transmitting 6 service account passwords unencrypted ~700 times per day. No signature existed for this volume. Zero alerts generated.
700
Cleartext binds per day
6
High-privilege accounts exposed
11
Domain controllers at risk
None
Alerts generated
A single packet capture on the network segment would have compromised the entire infrastructure stack. Migrate to LDAPS immediately.
Case 07
Autonomous Hunter
3,625 KMS Calls. One Admin Role.
A 50x spike in KMS Decrypt calls — Z-score of 573 — traced to 59 Lambda functions running with full administrative privileges. A developer elevated for testing and never reverted. Indistinguishable from post-compromise credential exfiltration.
6–160
Normal KMS calls per day
3,625
Calls in 2 hours (Z-score: 573)
59
Lambda functions as AdminRole
375
Severity score (top in env)
Running production Lambda functions as admin is identical to what an attacker does after privilege escalation. Concrete remediation target delivered with full context.
World-Class Team

Deep expertise in cybersecurity AI,
built for the Fortune 500 and MSSPs

Michael Pozmantier
Michael Pozmantier
Co-Founder / CEO
Joshua Neil
Joshua Neil, PhD
Co-Founder / CTO

Get Some Rest.
We've Got You Covered.

Book a Demo
Or reach us directly at hello@alphalevelsec.com  ·  alphalevel.ai