Capabilities

AlphaSOC identifies malicious behaviors, data exfiltration, and policy violations by analyzing structured telemetry across hybrid environments. It provides high-fidelity alerts in real time and supports retrospective detection, custom rule creation, and broad interoperability with your security stack.

Platform Coverage

AlphaSOC is data source–agnostic and supports a wide range of environments and log formats:

Source Type	Examples
Cloud	AWS CloudTrail, AWS EKS, AWS Lambda
SaaS Applications	Okta, GitHub, Slack, Confluence, Jira
Endpoints	CrowdStrike Falcon, SentinelOne, Cisco Umbrella
DNS	Internal resolvers, public resolvers, CoreDNS
Network Sensors	Zeek, Suricata, Corelight, Splunk Stream
Network Infrastructure	Firewalls, proxies, gateways, AWS VPC Flow Log
SIEM & Data Lakes	Cribl, Elastic, Splunk, Snowflake

Telemetry is normalized upon ingestion and analyzed consistently across all supported formats.

Detection Capabilities

AlphaSOC combines behavioral analysis, threat intelligence, and active scanning to identify both known and novel threats. Detection categories include:

• Command and control (C2): Beaconing, callbacks, domain fluxing.
• Data exfiltration: DNS tunneling, anomalous uploads.
• Cryptomining: Traffic to mining pools or abused VPS infrastructure.
• Phishing and malware: Lookalike domains, newly registered infrastructure.
• Policy violations: PUPs, outdated browsers, proxy avoidance tools.
• Protocol abuse: Use of cleartext protocols (e.g., FTP, Telnet) and atypical port usage (e.g., SSH over port 443).
• Anonymizing circuits: Tor, I2P, Freenet.
• Low-prevalence destinations: Domains or IPs contacted by only one host across all AlphaSOC deployments—often a hallmark of targeted campaigns.

Each detection is scored for severity based on its risk and confidence, and enriched in real time with threat intelligence and contextual metadata to support informed triage.

Detection Pipeline

AlphaSOC analyzes data across six core processing layers:

1. Active fingerprinting: Live probes to identify C2 and mining infrastructure.
2. Reputation scoring: Real-time integration with APIs like Google Web Risk and Quad9.
3. Prevalence analysis: Flags rare or unique destinations in customer environments.
4. Time series analysis: Detects beaconing, spikes, long-lived connections.
5. Feature classification: Identifies DNS tunneling, DGA, Base64 abuse.
6. Threat intelligence correlation: Uses curated and third-party intelligence updated hourly.

Anomaly Detection

Beyond rule-based detection, AlphaSOC continuously analyzes telemetry for behavioral outliers and environmental deviations. This includes:

• Time-based anomalies: Unexpected session durations, beacon intervals, or access times.
• User and identity outliers: Unusual logins across SaaS platforms, sudden permission changes, or rare user-agent strings.
• Resolver and network deviations: New outbound DNS resolvers or rare ASNs.
• Global rarity: Destinations queried by only one host across the entire customer base.

The system highlights behavioral deviations that signal stealth operations such as command-and-control beaconing, credential misuse, or lateral movement—commonly seen in red teaming and targeted APT activity.

Correlation & Escalation

AlphaSOC aggregates and correlates individual detection events across identities, endpoints, and timeframes to surface meaningful security outcomes.

• Micro-detections that may seem benign in isolation are clustered and scored together.
• Incident modeling highlights coordinated patterns like lateral movement, multi-stage phishing, or long-tailed command-and-control.
• Enriched alerts contain context to reduce false positives and accelerate.
• Triage: asset roles, user identity, rule references, and threat category.

This approach delivers high-confidence, noise-resistant alerts that drive more effective SOC workflows.

Custom Rules

AlphaSOC supports customer-defined detections via the Sigma rule format:

• Extend native detection logic.
• Tune severity and scoring to your environment.
• Create private, shareable, or community-based rules.

Rules are processed alongside native logic and mapped to the same alert pipeline.

Retrospective Detection

AlphaSOC stores normalized telemetry in a customer-specific data lake, enabling:

• Delayed detection of threats via updated rules or indicators.
• Forensics and post-incident reconstruction.
• Threat hunting over long time ranges.

This capability is included in both SaaS and on-premise deployments.

Alert Delivery & Integration

AlphaSOC alerts can be consumed in multiple ways:

• AlphaSOC Web Console: Built-in browser-based interface for alert review.
• Network Behavior Analytics for Splunk: Available via Splunkbase with dashboards and search macros.
• REST API: Pull or webhook-based access to alerts and metadata.
• SIEM and SOAR tools: Cribl, Elastic, Splunk, QRadar, ServiceNow, XSOAR, and more.
• ChatOps and tickets: Slack, Jira, Opsgenie, more added on request.
• File formats: JSON, OCSF, CEF, GELF.

Summary

AlphaSOC is designed to enhance detection accuracy and operational efficiency across modern hybrid environments. It provides:

• Coverage of diverse telemetry sources including SaaS, cloud, endpoint, and network logs.
• Normalized processing and multi-layered detection across six analytical stages.
• Behavioral and anomaly detection for subtle or low-prevalence threats.
• Support for custom detections using Sigma rules and enrichment via threat intelligence.
• Integration options for SIEM, SOAR, and ticketing systems via API, Splunk App, or webhooks.
• Retrospective detection via a customer-specific telemetry lake.

These capabilities help security teams detect, prioritize, and respond to both commodity and targeted threats effectively.

Need help integrating AlphaSOC into your environment? Contact us.