Revolutionizing Incident Response: How AI-Powered Forensic Analysis Transforms CyberTriage Investigations

By integrating CyberTriage with AI through the Model Context Protocol (MCP), incident responders can now leverage intelligent automation to dramatically accelerate forensic analysis and improve investigation outcomes.

The Challenge: Information Overload in Digital Forensics

Modern cybersecurity incidents generate massive amounts of forensic data. When an incident responder opens a CyberTriage investigation, they’re faced with thousands of files, hundreds of processes, countless event log entries, and numerous artifacts. CyberTriage’s user interface organizes this data into visual views—tabs for Accounts, Logins, Programs Run, Web Artifacts, and more—with automated scoring (Bad, Suspicious, Good, Unknown) to help prioritize analysis.

However, traditional manual analysis still requires:

• Navigating through multiple tabs and views in the CyberTriage UI
• Manually correlating events across different artifact types
• Switching between different views to connect related findings
• Spending hours clicking through filters and drilling into details
• Manually building mental models of relationships between artifacts
• Risking missed indicators due to the need to remember context across multiple views

This process is not only time-consuming but also error-prone. In a field where every minute counts and missing a single indicator can mean the difference between containment and breach, responders need better tools.

The Solution: AI Meets CyberTriage via MCP

The CyberTriage MCP Server bridges the gap between CyberTriage’s powerful visual interface and intelligent AI assistance. While analysts traditionally navigate CyberTriage’s UI tabs and views manually, the MCP server exposes CyberTriage’s underlying forensic data through the Model Context Protocol, allowing AI agents to analyze incident data using natural language—without requiring analysts to write queries or navigate multiple UI tabs.

But this isn’t just about automation—it’s about creating a collaborative partnership where incident responders work in tandem with AI, combining human expertise with AI’s computational capabilities to achieve faster, more thorough investigations.

What is the Model Context Protocol?

The Model Context Protocol (MCP) is a standardized interface that allows AI assistants to interact with external tools and data sources. Think of it as a universal translator between AI models and specialized systems—in this case, CyberTriage’s forensic database.

The Paradigm Shift: From Solo Analysis to Collaborative Intelligence

The integration of AI with CyberTriage doesn’t just automate tasks—it fundamentally transforms how incident responders work. The traditional model of a forensic analyst working alone, manually connecting dots across multiple data sources, is being replaced by a collaborative partnership between human expertise and AI capabilities.

The Old Way: Lone Analyst, Manual Correlation

In traditional forensic analysis using CyberTriage, an incident responder would:

1. Open CyberTriage’s user interface and navigate to the incident dashboard
2. Click through different artifact tabs (Accounts, Logins, Programs Run, Web Artifacts, etc.)
3. Use built-in filters and scoring to identify suspicious items
4. Manually switch between views to correlate findings
5. Click into detail panes to examine specific artifacts
6. Try to mentally connect related artifacts across different tabs
7. Manually build timelines by correlating timestamps from different views
8. Export findings or take notes to document relationships
9. Build mental models of the attack chain by remembering what they saw in each view

This process is linear, time-consuming, and relies entirely on the analyst’s ability to remember and connect disparate pieces of information across multiple UI views. The analyst must constantly switch contexts between different tabs, remember what they saw earlier, and manually piece together relationships. They’re essentially working in isolation, with only their knowledge, memory, and the CyberTriage UI’s visual interface as tools.

The New Way: Tandem Analysis with AI

With the CyberTriage MCP Server, the workflow becomes a dynamic, iterative conversation between the analyst and AI:

1. Analyst opens CyberTriage UI to get an initial overview
2. Analyst asks AI a question about something suspicious they noticed
3. AI accesses the forensic data using appropriate forensic tools (transparently querying the underlying database)
4. AI presents findings with context and correlations
5. Analyst reviews results and asks follow-up questions
6. AI performs deeper analysis based on analyst’s insights
7. Together, they build the complete picture through iterative exploration

This is no longer a solo effort—it’s a collaborative investigation where:

• The analyst brings domain expertise, intuition, and investigative instincts
• The AI brings computational power, systematic analysis, and instant data correlation
• Together, they work in tandem to uncover the truth faster and more accurately

Dual Workflow: CyberTriage UI + AI Assistant

The most powerful aspect of this integration is that analysts don’t abandon the CyberTriage UI—they use both tools together:

How AI Changes the “Connecting the Dots” Process

In traditional CyberTriage forensics, “connecting the dots” means the analyst must:

• Remember what they saw in the Files/Programs Run tab
• Recall process execution times from the Programs Run view
• Switch to the Logins tab and manually match timestamps
• Navigate to Web Artifacts tab to check network activity
• Switch to Events tab to correlate Windows event logs
• Manually cross-reference timestamps across different tabs
• Build mental timelines by remembering details from multiple views
• Keep track of relationships between artifacts in their head
• Constantly switch context between different UI tabs

With AI integration, the AI becomes the analyst’s memory and correlation engine:

• AI remembers everything it has queried across the entire investigation
• AI automatically correlates timestamps, processes, files, and events
• AI identifies relationships that might not be immediately obvious
• AI suggests next steps based on patterns it detects
• Analyst focuses on interpretation rather than data gathering

The Conversational Investigation Model

The new workflow is fundamentally conversational and iterative:

Analyst: “I see multiple failed logon attempts for the admin account. What happened after those failures?”

AI: “I found 15 failed logon attempts between 2:00-2:15 AM, followed by a successful logon at 2:16 AM. After the successful logon, I detected:

• 3 new processes spawned (cmd.exe, powershell.exe, certutil.exe)
• 5 files created in C:\Temp\
• 2 outbound network connections to IP 192.0.2.45
• 1 USB device connection event

Would you like me to investigate any of these further?”

Analyst: “Yes, check what certutil.exe did and what files were involved.”

AI: “Certutil.exe was used to decode a base64-encoded file. I found the source file and can show you the decoded content. It appears to be a PowerShell script that…”

This back-and-forth dialogue allows the analyst to:

• Follow their investigative instincts
• Ask natural language questions as they think
• Get immediate answers without context switching
• Explore multiple angles simultaneously
• Build understanding incrementally

Complementary Strengths: Human + AI

The collaboration works because each partner brings unique strengths:

Together, they form a synergistic partnership where the whole is greater than the sum of its parts.

Real-World Impact: The Analyst’s New Superpower

With AI integration, forensic analysts gain what feels like a superpower:

• Instant recall: “What did we find about that process yesterday?” AI remembers everything from the investigation
• Parallel analysis: While analyst reviews one finding in CyberTriage UI, AI can investigate three other angles simultaneously
• Hypothesis testing: Analyst can quickly test theories: “If this was ransomware, we should see X, Y, and Z—can you check?”
• Context switching: Analyst can jump between different aspects of the investigation without losing context
• Documentation assistance: AI can help draft findings, explain technical details, and suggest report sections

Most importantly, analysts are no longer limited by their ability to manually connect dots. The AI handles the heavy lifting of data correlation, allowing analysts to focus on what humans do best: understanding, interpreting, and making decisions.

50+ Forensic Tools at Your Fingertips

The CyberTriage MCP Server exposes over 50 specialized forensic analysis tools, organized into 12 comprehensive categories:

1. File System Analysis (10 tools)

Identify suspicious files, track modifications, and detect anomalies:

• Files modified during incident windows
• Largest files (potential data exfiltration indicators)
• Files in suspicious locations (Temp, AppData, Downloads)
• Deleted files and Recycle Bin contents
• Files with extension mismatches (obfuscation detection)
• Unknown files (potential custom malware)

2. Process Analysis (12 tools)

Understand process execution patterns and identify malicious activity:

• Elevated processes (privilege escalation detection)
• Processes from suspicious locations
• Process trees (attack chain reconstruction)
• Processes with suspicious command-line arguments
• DLLs loaded by processes (DLL hijacking detection)
• Longest-running processes (persistence mechanisms)

3. User Activity & Accounts (11 tools)

Track user behavior and account anomalies:

• Files accessed by specific users
• Recently created accounts
• Failed logon attempts (brute force detection)
• User activity around login events
• Account attributes and security settings

4. Windows Event Logs (8 tools)

Analyze system events for security indicators:

• Authentication failure events
• Events by time range (timeline correlation)
• WinRM events (lateral movement detection)
• Events around specific incidents
• Keyword searches across event payloads

5. Network Activity (3 tools)

Identify network-based indicators:

• Outbound network connections
• DNS to IP mappings
• Network addresses and hostnames

6. Device Connections (2 tools)

Detect unauthorized device access:

• USB devices connected to the system
• Device connections by time range

7. Security Analysis (3 tools)

Identify high-priority security findings:

• Unsigned files (potential malware)
• High-priority artifacts flagged by CyberTriage
• Hashset hits (known malicious files)

8. Artifact Analysis (3 tools)

Examine forensic artifacts systematically:

• Artifacts by type
• Artifact attributes and metadata
• Most common artifact types

9. Web Activity (3 tools)

Track web-based indicators:

• Web history entries
• Web downloads
• Web bookmarks

10. File Integrity & Hashes (3 tools)

Verify file integrity and identify duplicates:

• Files by MD5 hash
• Files by SHA256 hash
• Duplicate files

11. Timeline Analysis (1 tool)

Build comprehensive chronological timelines combining processes, file modifications, and events.

12. Statistics (2 tools)

Get high-level investigation metrics and file type distributions.

Real-World Use Cases: Collaborative Analysis in Action

Scenario 1: Rapid Triage After a Suspected Breach

Traditional Approach: An incident responder spends 2-3 hours manually navigating through CyberTriage UI tabs, clicking through different artifact views, using filters to find relevant data, and manually correlating events across multiple tabs. They work alone, constantly switching context between Accounts, Logins, Programs Run, Events, and Web Artifacts tabs, trying to remember what they saw in each view to connect findings.

Collaborative AI-Assisted Approach:

Analyst (working in CyberTriage UI): Notices some unusual activity in the process view.

Analyst (to AI): “Show me all elevated processes executed in the last 48 hours, along with any files they accessed, and correlate this with authentication failure events.”

AI: Automatically orchestrates multiple tool calls:

1. Calls get_elevated_processes with appropriate time filters
2. Retrieves files accessed by those processes
3. Fetches authentication failure events
4. Correlates the data and presents a unified analysis with timestamps

Analyst: Reviews AI findings, cross-references with CyberTriage UI to verify specific artifacts, then asks follow-up questions based on what they see.

Time saved: 2+ hours → 5 minutes of AI work + analyst review time

Key Difference: Analyst is no longer connecting dots alone—AI handles correlation while analyst focuses on interpretation and decision-making.

Scenario 2: Identifying Data Exfiltration Indicators

Traditional Approach: Analyst manually navigates to the Files tab in CyberTriage UI, uses filters to find large files, switches to the Network/Web Artifacts tab to check connections, opens the Web Downloads view separately, then tries to mentally correlate timestamps and relationships across these different views. This requires constant context switching between tabs and mental juggling of information from multiple UI views.

Collaborative AI-Assisted Approach:

Analyst (to AI): “Find the largest files created in the last week, check for any outbound network connections during that period, and identify any web downloads that might be related.”

AI: Orchestrates multiple tool calls simultaneously, correlates timestamps automatically, and presents findings showing potential exfiltration patterns.

Analyst: Uses CyberTriage UI to drill into specific files AI identified, verifies file hashes, and reviews network connection details. Then asks AI: “Can you check if any of these large files were accessed by processes that also made network connections?”

Key Difference: Analyst and AI work together iteratively—AI handles the heavy correlation work, analyst provides investigative direction and verifies findings in the UI.

Scenario 3: Process Tree Analysis

Traditional Approach: Analyst manually traces parent-child process relationships by clicking through the Programs Run tab in CyberTriage UI, examining each process’s detail pane, trying to remember parent/child relationships, taking notes, and building the attack chain mentally by correlating process execution times across multiple UI views.

Collaborative AI-Assisted Approach:

Analyst (notices suspicious process in CyberTriage UI): “Show me the process tree starting from this suspicious executable and identify all child processes, their DLLs, and any files they modified.”

AI: Calls get_process_tree, get_process_dlls, and get_files_by_time_range, then presents a complete attack chain visualization.

Analyst: Reviews the process tree, asks AI to investigate specific branches: “That child process looks interesting—what files did it access and what network activity did it generate?”

Key Difference: Complex multi-step analysis becomes a conversational exploration. Analyst doesn’t need to manually trace relationships—AI maintains the full context and can answer follow-up questions about any part of the tree.

Technical Architecture: How It Works

The CyberTriage MCP Server is built on a robust, type-safe architecture:

Core Components

• MCP Server (index.ts): Handles protocol communication via stdio
• Database Layer (database.ts): Manages SQLite connections with read-only access
• Tool Registry (registry.ts): Dispatches tool calls to appropriate handlers
• Category Modules: Organized tool implementations (fileSystem, processes, users, events, etc.)

Security Features

• Read-Only Access: Database opened in read-only mode to prevent accidental modifications
• Input Validation: All tool parameters are validated before execution
• SQL Injection Protection: Parameterized queries used throughout
• Error Handling: Comprehensive error handling without exposing sensitive information

Performance Optimizations

• Uses better-sqlite3 for high-performance synchronous queries
• Read-only mode reduces database locking overhead
• Result limiting prevents memory issues with large datasets
• Efficient query patterns leveraging database indexes

Getting Started: Integration Made Simple

Setting up the CyberTriage MCP Server is straightforward:

1. Installation

npm install
npm run build

2. Configuration

Set the database path via environment variable:

export CYBERTRIAGE_DB_PATH=/path/to/incident_data.ind

3. Connect Your AI Agent

Configure Claude Desktop or another MCP-compatible client:

{
  "mcpServers": {
    "cybertriage": {
      "command": "node",
      "args": ["/path/to/cyberTriageMCP/dist/index.js"],
      "env": {
        "CYBERTRIAGE_DB_PATH": "/path/to/incident_data.ind"
      }
    }
  }
}

4. Start Analyzing Collaboratively

Now you can work in tandem with AI:

• Open CyberTriage UI to get your initial overview and visual context
• Ask your AI assistant natural language questions about what you’re seeing
• AI automatically calls the appropriate forensic tools to answer your questions
• Review AI findings and cross-reference with CyberTriage UI for verification
• Ask follow-up questions to dive deeper—AI maintains full context of the conversation
• Iterate and explore until you have the complete picture

Remember: You’re not replacing CyberTriage UI—you’re enhancing it with an AI partner that works alongside you.

The Impact: Measurable Improvements Through Collaboration

Speed

• Query Time: Complex multi-step analyses reduced from hours to minutes
• Correlation: AI automatically correlates across multiple data sources while analyst focuses on interpretation
• Timeline Building: Instant chronological event reconstruction through AI, verified by analyst in CyberTriage UI
• Parallel Processing: Analyst can work on one aspect while AI investigates others simultaneously

Accuracy

• Consistency: AI agents don’t get tired or miss patterns, providing reliable correlation
• Completeness: Systematic AI analysis ensures no data source is overlooked
• Correlation: Better identification of relationships between events through AI’s perfect memory
• Human Verification: Analyst can cross-check AI findings in CyberTriage UI for quality assurance

Accessibility

• Natural Language: Analysts ask questions conversationally—no need to navigate through multiple UI tabs
• Learning Curve: Responders can leverage AI without extensive training on complex correlation techniques
• Documentation: AI can explain its analysis process, helping analysts understand findings
• Dual Interface: Analysts use familiar CyberTriage UI for visual verification while AI handles complex cross-tab correlation
• Reduced UI Navigation: Less time spent clicking through tabs and filters

Collaborative Benefits

• Context Preservation: AI remembers entire investigation context, analyst doesn’t need to re-explain
• Iterative Exploration: Natural back-and-forth allows following investigative instincts
• Reduced Cognitive Load: Analyst no longer needs to manually track relationships and correlations
• Enhanced Focus: Analyst concentrates on high-level analysis while AI handles data extraction
• Synergistic Intelligence: Human expertise + AI capabilities = better outcomes than either alone

Future Possibilities

The integration of AI with CyberTriage via MCP opens exciting possibilities:

• Automated Report Generation: AI can synthesize findings into comprehensive incident reports
• Pattern Recognition: Machine learning can identify novel attack patterns
• Predictive Analysis: AI can predict potential attack progression based on current indicators
• Multi-Incident Correlation: Compare patterns across multiple investigations
• Continuous Learning: AI agents can learn from each investigation to improve future analyses

Conclusion: The Future of Incident Response is Collaborative

The CyberTriage MCP Server represents a fundamental paradigm shift in digital forensics. It’s not about replacing analysts with AI—it’s about creating a collaborative partnership where human expertise and AI capabilities work in tandem.

In this new model, incident responders:

• Work alongside AI rather than in isolation
• Use both CyberTriage UI and AI as complementary tools
• Focus on interpretation and decision-making while AI handles data correlation
• Engage in iterative, conversational investigations that adapt to findings
• No longer manually connect dots—AI maintains context and correlations
• Respond faster to security incidents through collaborative efficiency
• Identify more indicators through systematic AI-assisted analysis
• Improve investigation quality by combining human intuition with AI’s computational power

As cyber threats continue to evolve and become more sophisticated, tools like the CyberTriage MCP Server become essential for maintaining effective incident response capabilities. The future of cybersecurity forensics is not just about having the right data—it’s about having intelligent partners that work alongside analysts to understand that data faster and more accurately.

The transformation is clear: forensic analysts are no longer working alone, manually connecting dots across multiple data sources. Instead, they’re working in tandem with AI assistants that handle the heavy lifting of data correlation, allowing analysts to focus on what they do best: understanding the bigger picture, making strategic decisions, and uncovering the truth.

Whether you’re investigating a ransomware attack, tracking an APT group, or responding to an insider threat, the collaborative combination of CyberTriage UI and AI via MCP provides the speed, accuracy, and insight needed to stay ahead of adversaries. The analyst brings the expertise and investigative instincts; the AI brings the computational power and systematic analysis. Together, they form a partnership that’s greater than the sum of its parts.

About the Project: The CyberTriage MCP Server is an open-source project available on GitHub. It provides 50+ forensic analysis tools through the Model Context Protocol, enabling AI agents to perform comprehensive incident investigations.

Learn More: Visit the GitHub repository for documentation, installation instructions, and examples.