Full Report
If you’re evaluating AI-powered SOC platforms, you’ve likely seen bold claims: faster triage, smarter remediation, and less noise. But under the hood, not all AI is created equal. Many solutions rely on pre-trained AI models that are hardwired for a handful of specific use cases. While that might work for yesterday’s SOC, today's reality is different. Modern security operations teams face a
Analysis Summary
This article focuses on comparing different types of Artificial Intelligence (AI) models used within Security Operations Center (SOC) platforms, rather than detailing specific malware, attack tools, or traditional TTPs. The primary "technique" examined is the methodology of AI implementation in security triage.
# Tool/Technique: Pre-trained AI in SOC Platforms
## Overview
Pre-trained AI models in SOC platforms are machine learning algorithms trained on curated, labeled historical data for specific, well-understood security use cases (e.g., phishing detection, known malware alerts). They function as specialized assistants to quickly classify known alerts and recommend remediation actions.
## Technical Details
- Type: Technique (AI Methodology)
- Platform: SOC Platforms/Security Software
- Capabilities: Rapid classification, confidence scoring, and recommended actions for *predefined* alert types.
- First Seen: Not specified (Described as "yesterday's SOC" approach).
## MITRE ATT&CK Mapping
As this describes a *security control/tooling capability* rather than an adversary TTP, direct offensive mapping is not applicable. However, its *limitations* relate to the adversary's ability to use novel techniques:
- **T1562 - Impair Defenses** (Relates to potential blind spots created by reliance on non-adaptive systems)
## Functionality
### Core Capabilities
- Quick triage and classification for high-volume, repeatable alert categories.
- Automation of common security workflows based on learned patterns.
### Advanced Features
- Limited ability to handle alert types that were not explicitly included in the initial training sets.
- Requires vendors to develop, test, and deploy new models for every new or evolving use case.
## Indicators of Compromise
N/A (This describes a defensive technology comparison)
## Associated Threat Actors
N/A (This describes a defensive technology comparison)
## Detection Methods
N/A (This describes a defensive technology comparison)
## Mitigation Strategies
N/A (This describes a defensive technology comparison)
## Related Tools/Techniques
- Traditional Rules Engines (SOARs that execute actions based on pre-configured playbooks)
***
# Tool/Technique: Adaptive AI in SOC Platforms
## Overview
Adaptive AI represents an evolution in SOC technology designed to handle the entire spectrum of security signals, including novel or never-before-seen alerts. It continuously learns, researches new alerts in real-time, and triages them without requiring prior explicit training for every specific use case.
## Technical Details
- Type: Technique (AI Methodology)
- Platform: SOC Platforms (e.g., Radiant's platform)
- Capabilities: Real-time research of novel alerts using semantic classification, triage, response automation, and integrated log management.
- First Seen: Described as the modern, evolving approach.
## MITRE ATT&CK Mapping
As this describes an advanced defensive capability, direct offensive mapping is not applicable, but it counters advanced threats:
- **T1497 - Time-Based Evasion** (Adaptive systems are less susceptible to techniques designed to bypass static training sets)
## Functionality
### Core Capabilities
- Handling *any* alert type, including novel threats, through real-time investigation.
- Semantic classification to assess similarity to previously seen alerts.
- Continuous learning capability.
### Advanced Features
- Powered by multiple specialized Large Language Models (LLMs) and coordinated research/triage agents.
- Integration of response automation to slash Mean Time to Respond (MTTR).
- Integrated, affordable log management, reducing reliance on legacy SIEMs.
## Indicators of Compromise
N/A (This describes a defensive technology comparison)
## Associated Threat Actors
N/A (This describes a defensive technology comparison)
## Detection Methods
N/A (This describes a defensive technology comparison)
## Mitigation Strategies
- Adopting security platforms utilizing adaptive ML/AI over static, pre-trained models to maintain coverage against evolving threats.
- Implementing integrated response automation alongside investigation capabilities.
## Related Tools/Techniques
- Advanced Machine Learning (ML) systems
- Security Orchestration, Automation, and Response (SOAR)
- Modern Log Management solutions