Modern security teams face a dual challenge: they’re bombarded with alerts while still missing critical signals that indicate real threats. Fidelis Active Threat Detection tackles this problem by correlating weak signals across multiple phases of attacks, transforming them into actionable intelligence. Let’s examine the technical mechanics behind this capability within the Fidelis Elevate® platform.
The Technical Foundation of Active Threat Detection
Fidelis Active Threat Detection™ operates as an integral component of the Fidelis Elevate® XDR framework. Rather than functioning as a standalone solution, it leverages multiple data streams from across the security infrastructure to build comprehensive threat intelligence.
Deep Session Inspection: The Core Detection Engine
At the foundation of Fidelis Elevate®‘s threat detection capabilities is its patented Deep Session Inspection® technology. Unlike conventional traffic monitoring systems that evaluate only headers or basic packet data, this technology:
Processes traffic at speeds up to 20 GB through specialized 1U sensors
Inspects content across all network ports and protocols
Reconstructs and analyzes complete sessions rather than isolated packets
Detects threats concealed within nested files and complex data structures
Seamlessly integrates with third-party decryption technologies to examine encrypted traffic both in-line and out-of-band
Monitors ephemeral and containerized workloads that frequently escape detection
This deep inspection creates the raw signal data that feeds into the threat detection correlation system.
Continuous Terrain Mapping and Risk Assessment
For accurate threat detection, Fidelis Elevate® first establishes comprehensive visibility through:
Real-time inventory collection across on-premises and cloud environments
Asset classification and risk profiling
Network traffic pattern analysis
Data flow monitoring
Configuration assessment
This environmental awareness creates the contextual backdrop against which potential threat signals are evaluated.
The Signal Correlation Mechanism
The actual mechanics of Active Threat Detection involve several distinct technical processes:
Signal Aggregation from Multiple Sources
Fidelis Elevate® aggregates data from multiple detection vectors:
Network traffic analysis alerts from Fidelis Network®
Deception environment triggers from Fidelis Deception®
Endpoint detections from Fidelis Endpoint® or third-party EDR solutions
Sandbox execution results for suspicious files
Proprietary Correlation Algorithms
These signals then undergo analysis using proprietary algorithms that:
Identify potentially related events across different security layers
Apply temporal correlation to establish potential attack sequences
Evaluate indicators against baseline behavioral patterns
Filter out noise and false positives using contextual intelligence
Identify connections based on threat actor profiles and TTPs
MITRE ATT&CK Framework Mapping
A crucial technical element is the automatic mapping to the MITRE ATT&CK framework, which:
Categorizes observed techniques according to established attack patterns
Supplements proprietary detection with industry-standard classification
Enables defenders to understand the attack stage and potential next steps
Provides a common language for threat analysis and response
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Insight into the detection-to-remediation process
Mapping threat signals
The Technical Workflow of Active Threat Detection
In practice, Active Threat Detection follows a defined technical process flow:
Signal Collection: The system continuously gathers indicators from across the security environment, generating a stream of potential threat data.
Initial Filtering: Basic noise reduction removes obvious false positives and low-significance events.
Correlation Engine Processing: The remaining signals undergo correlation analysis using the proprietary algorithms described above.
Confidence Scoring: Each potential threat pattern receives a confidence score based on the strength, number, and relationships between correlated signals.
Conclusion Generation: For high-confidence detections, the system generates an “Active Threat” conclusion containing detailed evidence and attack context.
Response Integration: These conclusions integrate with automated response workflows and analyst investigation tools.
Technical Integration with the Security Stack
As an open XDR platform, Fidelis Elevate®‘s Active Threat Detection integrates with existing security infrastructure via:
REST APIs for bidirectional data exchange
Out-of-the-box connectors for common security platforms
Custom integration options through the Fidelis API
Webhook-based alert forwarding
This integration ensures that Active Threat Detection enhances rather than duplicates existing security investments.
Real-World Technical Implementation
In practical deployment, Active Threat Detection demonstrates several key technical capabilities:
Threat Pattern Recognition
The system recognizes complex threat patterns, including:
Multi-stage attack sequences spanning days or weeks
Living-off-the-land techniques using legitimate tools
Supply chain compromise indicators
Command-and-control communications hidden in legitimate traffic
Real-Time Processing Architecture
The underlying architecture enables:
Parallel processing of multiple threat signals
Near real-time correlation of disparate events
Dynamic updating of threat conclusions as new evidence emerges
Continuous recalibration of detection algorithms based on new intelligence
Forensic Evidence Collection
For each Active Threat detection, the system automatically preserves:
Relevant network traffic captures
Process execution logs
File access records
Authentication events
Configuration changes
This evidence collection happens automatically as threats are detected, creating a comprehensive record for investigation.
Technical Benefits of the Approach
The technical design of Active Threat Detection offers several distinct advantages:
Reduced False Positives
By correlating multiple signals before generating alerts, the system dramatically reduces false positives compared to traditional point solutions.
Increased Detection Confidence
The confidence scoring mechanism ensures analysts receive high-quality alerts with sufficient supporting evidence for immediate action.
Enhanced Investigation Efficiency
Detailed contextual information and evidence preservation streamline the investigation process, reducing time-to-remediation.
Continuous Security Improvement
The system’s intelligence grows over time through:
Machine learning algorithms that refine detection patterns New correlation rules based on emerging threats Automatic incorporation of threat intelligence Feedback loops from analyst investigations
Conclusion
Fidelis Active Threat Detection™ represents a sophisticated technical approach to the modern threat detection challenge. By correlating weak signals across multiple security layers, mapping findings to known attack patterns, and providing rich contextual intelligence, it empowers security teams to detect and respond to threats that might otherwise go unnoticed. The integration of this capability within the broader Fidelis Elevate® platform creates a comprehensive security solution that addresses the full attack lifecycle, from initial detection through investigation and response.
The post How Fidelis Elevate® Achieves Active Threat Detection appeared first on Fidelis Security.
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