Control Plane Architecture (AI-assisted)
Three types of AI-assisted agents working together
AI-Powered Continuous Improvement
Iron Eye's AI engine follows a continuous learning loop that improves correlation quality and response behavior over time.
| Ingest | Normalize alerts from SIEM, EDR, network, cloud, and identity tools. |
| Correlate | Link related signals into high-signal incidents using multi-signal relationships and behavioral patterns. |
| Enrich | Add context from threat intelligence, asset metadata, and user activity to prioritize what matters. |
| Respond | Execute automated containment and remediation workflows under team-defined policies and guardrails. |
| Learn | Incorporate analyst feedback and incident outcomes to refine correlation logic and automation decisions. |
How It Works
Three types of AI-assisted agents process security information and orchestrate containment and remediation workflows across your security stack.
Ingestion Agents
Automatically ingest and normalize alerts from security tools
- Real-time alert ingestion from a wide range of security tools
- Automatic schema normalization across different alert formats
- Data quality validation and enrichment
- Multi-format support (JSON, XML, Syslog, CEF)
Correlation Agents
Identify relationships between alerts using AI/ML
- Cross-tool alert correlation
- Temporal pattern analysis
- Behavioral anomaly detection
- Threat intelligence integration
Response Agents
Execute automated response actions across security tools
- Automated containment (isolation, blocking, account suspension)
- Firewall rule modification
- EDR endpoint actions
- Cloud security policy updates
Intelligent Correlation Engine
Multi-signal correlation identifies relationships between alerts across your security stack, reducing false positives while maintaining broad threat coverage.
Our correlation engine is designed to significantly reduce false positives under defined operating conditions. Actual results may vary based on environment configuration, alert volume, and threat landscape.
Illustrative example showing how correlation consolidates raw alerts. Numbers are for demonstration only; actual impact varies by environment and configuration.
Contextual Analysis
Higher weight for alerts on critical systems, understanding normal business operations vs. anomalies
Multi-Signal Correlation
Cross-tool validation requiring signals from multiple tools before flagging as threat
Continuous Learning
Built for continuous learning — incorporating analyst feedback and incident outcomes to refine correlation over time
Technical Foundation
The core technology and architecture that powers Iron Eye's AI-assisted security operations platform.
Control Plane Architecture (AI-assisted)
AI-assisted agents process information and execute actions using intelligent correlation and pattern analysis — built to leverage ML models and LLMs as the platform scales
Includes agentic components for investigation and response orchestration. Automated actions include isolation, firewall rule updates, and account suspension—executed based on threat context and severity. All response agents operate under policies, approvals, and guardrails defined by your security team.
Intelligent Correlation Engine
Multi-tool alert pattern analysis with MITRE ATT&CK framework mapping
Continuous learning from customer environments enables significant false positive reduction while maintaining broad threat coverage.
Deep API Functionality
Bidirectional API architecture (inbound + outbound) designed for rapid response
Deep integrations with major security tools—including FortiGate, CrowdStrike, and ServiceNow—enable automated response workflows across your stack.
Standards Alignment
NIST Incident Response lifecycle automation and MITRE ATT&CK technique mapping
Industry framework compliance ensures compatibility with existing security operations workflows.
Why Iron Eye Works Better
Capabilities that help your team cut through alert noise and focus on real threats.
Multi-Signal Correlation
Correlates signals across SIEM, EDR, network, cloud, and identity tools to identify high-signal incidents.
Context-Aware Scoring
Uses asset, user, and environmental context to prioritize what matters most to your team.
Analyst-Guided Automation
Executes automated workflows under team-defined policies and guardrails, keeping analysts in control.
Continuous Improvement
Learns from analyst decisions and incident outcomes to refine detection quality over time.