AI-Powered Threat Analysis: Using Large Language Models for Automated Root Cause Analysis

AI in Security Operations

The integration of large language models into Security Operations Centers represents a paradigm shift in how security teams handle incidents. SuperClue uses large language models to provide instant, expert-level threat analysis that would traditionally require hours of manual investigation.

How AI Threat Analysis Works in SuperClue

Trigger Points

AI analysis is available at key moments during incident response:

1. Breach Detection: When the SOC agent detects a breach pattern, an analyst can run an AI root cause analysis with one click
2. Vulnerability Discovery: After a DAST scan finds vulnerabilities, AI provides fix suggestions
3. Manual Request: Analysts can request AI analysis on any detected breach at any time
4. Re-analysis: Analysts can run the analysis again as the investigation develops

Analysis Pipeline

When a security event triggers AI analysis, SuperClue feeds the model a structured prompt containing:

• Breach data: The breach patterns detected, the number of indicators, and the breach description
• System context: The name and host of the affected server
• Data context: The DPDP Act data category at risk
• Compliance context: Applicable regulatory requirements (CERT-In, DPDP Act)

What AI Analysis Produces

1. Root Cause Analysis

The AI generates a detailed breakdown of what happened and why:

• Attack Vector Identification: How the attacker gained initial access
• Exploitation Chain: Step-by-step reconstruction of the attack sequence
• Affected Assets: Systems, data, and users impacted
• Data Classification: Types of data potentially compromised (per DPDP Act categories)
• Attack Attribution Indicators: TTPs (Tactics, Techniques, Procedures) mapped to MITRE ATT&CK

2. Immediate Containment Steps

AI provides 5-7 prioritized containment actions:

1. [CRITICAL] Isolate affected server from network
2. [CRITICAL] Rotate all credentials stored on compromised system
3. [HIGH] Block identified C2 IP addresses at firewall
4. [HIGH] Disable compromised user accounts
5. [MEDIUM] Enable enhanced logging on adjacent systems
6. [MEDIUM] Scan related infrastructure for lateral movement
7. [LOW] Notify affected third-party service providers

The prompt asks for specific, actionable steps. Check each one against your environment before you act on it.

3. CERT-In Report Narrative

SuperClue's AI generates a complete incident narrative formatted for CERT-In submission:

• Incident Description: Plain-language explanation of the breach
• Timeline: ISO 8601 timestamps of key events
• Impact Assessment: Scope, severity, and data categories affected
• Containment Measures: Actions taken to limit damage
• Root Cause: Technical explanation of the vulnerability exploited
• Remediation Plan: Steps to prevent recurrence

You can use this narrative as written or have the DPO edit it before you file with CERT-In. SuperClue records how you submitted it; it does not file with CERT-In for you.

4. Recovery Recommendations

Long-term recommendations to prevent similar incidents:

• Specific configuration changes
• Patching requirements with CVE references
• Architecture improvements
• Process and policy changes
• Employee training recommendations

5. Risk Assessment

Quantified risk impact including:

• Data Principal Impact: Estimated number of affected individuals
• Financial Exposure: Potential regulatory penalty assessment
• Reputation Risk: Public disclosure implications
• Operational Impact: Business continuity considerations

Choosing Your AI Model

SuperClue lets you choose between three AI back-ends. Different kinds of model suit different jobs:

Narrative-Focused Models

• Best for: Comprehensive, detailed analysis
• Strength: Excellent at generating human-readable narratives and regulatory reports
• Use case: CERT-In report generation, executive briefings

Reasoning-Focused Models

• Best for: Precise technical analysis
• Strength: Strong at step-by-step reasoning through attack chains
• Use case: Root cause analysis, forensic investigation support

Fast, Low-Latency Models

• Best for: Fast initial triage
• Strength: Low latency for real-time analysis
• Use case: Automated first-response analysis, high-volume alert processing

Template-Based Fallback

When AI is not configured or a provider call fails, SuperClue provides a clearly labelled template-based analysis:

• A draft root cause built from the detected breach patterns
• Structured checklists for containment and recovery
• A draft CERT-In report narrative marked for you to verify before filing
• A risk line based on the data category at risk

Templates ensure continuity of operations regardless of AI availability.

Best Practices

1. Use AI for First Response

Let AI generate the initial analysis while human analysts focus on validation and decision-making.

2. Review Before Submission

Always have a human review AI-generated CERT-In reports before submission.

3. Feed Back Results

When AI analysis is incorrect or incomplete, note the correction in the incident record and update your playbooks, so the next analyst does not repeat the mistake.

4. Combine with Playbooks

Use AI analysis results to select and run the right incident response playbook.

AI-powered threat analysis doesn't replace human security analysts. It amplifies their capabilities, providing instant expert-level analysis so they can focus on strategic decision-making and complex investigations.