Security Advisor Technology - Moyer Scientific Research LLC

🎯 Overview

The Automated Security Advisor uses advanced machine learning and statistical analysis to provide real-time security recommendations, automated threat triage, and intelligent incident response guidance. It acts as a tireless security analyst that continuously monitors your environment and provides expert-level insights.

🧠 Core Technologies

Deep Learning

Neural networks trained on millions of security events to recognize attack patterns, anomalies, and normal behavior baselines across diverse environments.

NLP Processing

Natural language understanding of security logs, alerts, and reports enabling semantic analysis and automated correlation of events described in human language.

Graph Analytics

Relationship mapping between assets, users, and events to identify attack paths and lateral movement patterns that traditional tools miss.

Reinforcement Learning

Adaptive system that learns from feedback on recommendations, continuously improving accuracy and relevance of suggestions over time.

Statistical Modeling

Probabilistic models for anomaly detection, risk scoring, and confidence estimation using Bayesian methods and time-series analysis.

Threat Intelligence

Integration with global threat feeds to contextualize local events with known attack campaigns, TTPs, and IOCs from the broader threat landscape.

✅ Key Capabilities

Alert Triage: Automatically prioritize security alerts by risk, impact, and confidence
Root Cause Analysis: Trace incidents back to initial compromise point
Response Automation: Suggest and execute response playbooks
Configuration Review: Continuous security posture assessment
Threat Hunting: Proactive search for indicators of compromise
Compliance Monitoring: Map controls to regulatory requirements

🎓 How It Works

1. Data Ingestion

Collects security data from logs, SIEM, EDR, network monitors, cloud providers, and configuration management systems.

2. Analysis & Correlation

ML models analyze events in real-time, correlating across data sources to identify patterns and anomalies.

3. Risk Scoring

Each finding is scored for severity, confidence, and business impact using contextual information.

4. Recommendation Generation

System generates specific, actionable recommendations with supporting evidence and remediation steps.

5. Automated Response

For high-confidence scenarios, can execute approved playbooks automatically (with human oversight).

6. Learning & Feedback

Incorporates analyst feedback to improve future recommendations and reduce false positives.

📝 Example Scenario

Detection: Multiple failed login attempts followed by successful authentication

Correlation: User typically authenticates from US, this login from Eastern Europe

Enrichment: Threat intel shows IP associated with credential stuffing campaign

Risk Score: 8.7/10 (High confidence account compromise)

Recommendation: Force password reset, terminate session, enable MFA, investigate recent activity

Automation: Session terminated automatically, ticket created for SOC review

Augment Your Security Operations

Currently under development. Join the waitlist for early access.

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