Project Info
This project did not submit a demo video on Devpost.
Inspiration
Alert fatigue is one of the most critical issues facing modern Security Operations Centers (SOCs). Security analysts are constantly bombarded by logs, many of which are false alarms, while critical threats remain obscured due to missing forensic context. We wanted to build a system that mimics a real SecOps team—allowing specialized agents to not only analyze logs but also critique each other's work and automatically query the database for missing context, just like a human investigator would.
What it does
SIFT Guardian is an autonomous SecOps incident response system. When a telemetry payload (like an encoded PowerShell script, a malicious Word document spawning child processes, or a web shell injection) is submitted: The Investigator Agent performs initial threat signature analysis. The Skeptic Agent audits the findings. If confidence is low or telemetry categories (like Parent Process, Persistence, or Network Activity) are missing, it triggers a self-correction loop. The Investigator automatically performs reanalysis, querying the enterprise log database for the missing logs. The Verifier Agent approves the findings against telemetry sources. The Reporter Agent compiles a comprehensive, peer-reviewed Markdown intelligence advisory report.
How we built it
Backend: Built using C# on the latest .NET 10.0 SDK. We designed a decoupled transient/singleton dependency injection structure for specialized agent personas coordinated by a central AgentOrchestrator workflow manager. Frontend: Constructed with Vanilla HTML5, CSS3, and JavaScript (ES6). It features a glowing cyberpunk grid command-center theme with a real-time topology flow-graph illustrating active agent states and self-correction cycles, and a custom logs terminal.
Challenges we ran into
Orchestrating agent collaboration dynamically without getting stuck in infinite loops. We solved this by developing structured context state maps that pass target feedback parameters (missing evidence classes) back to the investigator's reanalysis handler.
Accomplishments we're proud of
Designing a visual dashboard that is highly premium and responsive, providing immediate visual feedback on multi-agent collaboration. Implementing a fully functional self-correcting logic loop that dynamically gathers additional forensic evidence on-the-fly.
What we learned
We learned how to design contrasting agent personalities (a positive investigator vs. an aggressive skeptic) to achieve rigorous output validation that minimizes false positive rates.
What's next
Integrating real-time SIEM connectors (Splunk/Elasticsearch). Enabling automated containment tasks (e.g. blocking IPs, isolating endpoints, or revoking OAuth tokens).
🛡️ SIFT Guardian: Self-Correcting Multi-Agent Incident Response
SIFT Guardian is an advanced, autonomous Security Operations Center (SecOps) incident response system designed to automate forensic investigation and intelligence advisory compilation. Built for UC Berkeley's AI Hackathon, SIFT Guardian solves the challenge of alert fatigue and rigid automation by utilizing a cognitive, self-correcting multi-agent architecture.
🌟 The Core Problem & Our Solution
Modern SOC analysts are overwhelmed by security alerts. Traditional Security Orchestration, Automation, and Response (SOAR) playbooks are fragile, brittle, and unable to deal with ambiguity. When telemetry is incomplete, a human must manually dig through logs.
SIFT Guardian changes this. By orchestrating a team of specialized AI agents in a self-correcting cognitive loop, the system can:
- Conduct initial analysis on telemetry payloads.
- Critique its own findings using a skeptic agent that flags gaps in forensic evidence.
- Automatically route back for reanalysis to pull missing logs (e.g. parent processes, persistence mechanisms, or network connections).
- Verify the finalized evidence tree.
- Compile and export a professional, peer-reviewed Intelligence Advisory Report.
🗺️ System Topology & Agent Architecture
SIFT Guardian implements a directed cognitive pipeline with an active feedback loop:
graph TD
A[Case Intake / Telemetry Payload] --> B[Investigator Agent]
B -->|Initial Finding & Confidence| C[Skeptic Agent]
C -->|If Confidence < 80% & Evidence Missing| D[Self-Correction Loop]
D -->|Request Missing Forensic Categories| B
B -->|Reanalysis / Query Telemetry DB| C
C -->|If Confidence >= 80% & Approved| E[Verifier Agent]
E -->|Verify Telemetry Integrity| F[Reporter Agent]
F -->|Compile Markdown Report| G[Intelligence Advisory Output]
style D fill:#f59e0b,stroke:#d97706,stroke-width:2px,color:#fff
style B fill:#00f0ff,stroke:#0891b2,stroke-width:2px,color:#000
style C fill:#f59e0b,stroke:#d97706,stroke-width:2px,color:#000
style E fill:#10b981,stroke:#059669,stroke-width:2px,color:#000
style F fill:#a855f7,stroke:#7c3aed,stroke-width:2px,color:#000
🔍 Tactical Agents
- Investigator Agent: Parses incoming payload data, detects threat classifications, and queries the enterprise telemetry database to retrieve detailed indicators of compromise (IoC).
- Skeptic Agent: Acts as the Quality Assurance auditor. It challenges the initial analysis, checks for missing evidence categories (like Process Tree, Parent Process, Persistence, or Network Activity), and triggers the self-correction cycle if threshold standards are not met.
- Verifier Agent: Inspects the gathered forensic evidence directly against the logging endpoints to approve credibility.
- Reporter Agent: Formulates the peer-reviewed report into structured markdown, providing summaries, timeline flows, and actionable remediation steps.
🚀 Key Features
- Tactical Cyber-Defense Dashboard: A beautiful command-center interface using a modern dark theme with custom scanline details.
- Dynamic Topology Map: Live visual highlights illustrating which agent is active, including active loop indicators when the self-correction cycle is running.
- Real-time SecOps Terminal: A simulated command-line output showing exact step-by-step logs and decision reasoning of each agent in color-coded lines.
- Advisory Export: Quick button to export the finalized markdown file for local archival.
- Built-in Threat Profiles: One-click loaders for common attack scenarios:
- Suspicious PowerShell Execution (Encoded command execution calling home).
- Phishing Attachment Execution (Word document spawning cmd and certutil).
- IIS Web Shell Backdoor (Web application process injecting ASPX shell).
💻 Tech Stack
- Backend: ASP.NET Core Web API (.NET 10.0 C#)
- Frontend: Vanilla HTML5, CSS3 (Tactical Grid & Glowing Glassmorphism), and JavaScript ES6.
- Design & Typography: Space Grotesk (headers) & JetBrains Mono (monospaced logs).
⚙️ Project Setup & How to Run
Prerequisites
1. Clone & Build
# Navigate to the project directory
cd SIFTGuardian
# Build the project
dotnet build
2. Run the Server
# Start the web server with the HTTP profile
dotnet run --launch-profile "http"
The application will launch on:
- URL:
http://localhost:5049
3. Open in Browser
Open http://localhost:5049 in your web browser, choose one of the Threat Profiles (e.g. PowerShell Execution), and click Deploy Tactical Agents to witness the self-correcting orchestration in real-time.
Analysis
View
Metric
- 5
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- C#In code
- CSSIn code
- HTMLIn code
- JavaScriptIn code
4 of 4 appear in the indexed code.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
107 KB
Source files
40
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
sananmalik/SIFTGuardian
47 files · 116 KB · @ 2660828
Structure
Interface
2 files · 4%Screens, components and styles rendered to the user.
API & routing
1 file · 2%Request entry points: routes, handlers and controllers.
Application logic
16 files · 34%Domain rules, services and shared utilities.
Data & schema
5 files · 11%Schema definitions, migrations and data access.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- Markdown37%
- C#31%
- CSS15%
- JavaScript10%
- HTML7%
Share of indexed source by file size. Binary and vendored files are excluded.
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