NEUROSPYGLASS

🔍 NeuroSpyglass AI + Amazon Bedrock Integration Proposal Cognitive Forensic Reconstruction with Foundation Models

🧠 Executive Summary NeuroSpyglass AI revolutionizes digital forensics through multimodal behavior chain analysis and cognitive modeling. By integrating Amazon Bedrock, the system gains secure, scalable access to state-of-the-art foundation models like Claude, Titan, and LLaMA, enabling robust narrative generation, real-time assistant capabilities, and contextual intelligence — without managing model infrastructure.

🧩 Updated System Architecture with Bedrock csharp CopyEdit [User Session Data] ↓ [Multimodal Event Capture] — CLI, keystroke, voice, file, mouse ↓ [Preprocessing + Embeddings] — Time2Vec, Session2Vec, ECAPA-TDNN ↓ [Temporal Behavior Graph (TBG)] ↓ [GNN Anomaly Detection + Identity Verification] ↓ [Amazon Bedrock: LLM Forensic Narrative Generator] ↓ [Timeline Replay + Investigative Assistant (LLM-powered UI)] ↓ [Investigator Review / Report Generation]

🎯 Key Bedrock-Powered Components

1. 🧾 LLM Forensic Explainer Objective: Translate behavior chains into human-readable forensic narratives Bedrock Model: Claude (Anthropic) / Titan Text / Meta Llama via Amazon Bedrock Prompt Strategy: plaintext CopyEdit You are a digital forensics expert. Given the following behavior chain and metadata, write a clear, structured forensic report indicating anomalies, likely attacker strategy, and confidence score.

Input: Temporal graph nodes, user identity stats, command/event history Output: Paragraph summary with indicators (time, deviation, context, strategy, suspicion level)

1. 💬 Interactive Investigator Assistant (Chat UI) Objective: Allow analysts to ask questions about suspect sessions Bedrock Model: Claude or Titan Functionality: “Who accessed this file at 3:12 AM?”

“Was this typing pattern typical for Alice?”

“Show me suspicious command chains in this session.”

Backend: Store session context

Use retrieval-augmented generation (RAG) to ground LLM answers

Secure model invocation through Bedrock SDK

1. 📊 Narrative-Driven Session Reports Objective: Auto-generate structured forensic case documents Model Use: Summary section

Action timeline

Deviation analysis

Recommended response (severity score, quarantine steps)

🛠️ Technical Stack Integration Layer Tech Role LLM Interface Amazon Bedrock SDK (Boto3 or AWS Lambda) Secure API to foundation models Frontend Next.js + React + Chat + Timeline Connects to Bedrock LLMs for explanations Backend Python/FastAPI + GNN/embedding layers Sends prompts to Bedrock, parses responses Secure Env AWS IAM + Encryption + VPC Endpoints Protect sensitive forensic data

🛡️ Security & Compliance No model training on customer data

All traffic routed via AWS IAM roles and secure endpoints

Data not retained by Bedrock (by default)

Meets enterprise compliance: HIPAA, GDPR, FedRAMP, etc.

📈 Strategic Benefits Feature Traditional Tools NeuroSpyglass + Bedrock LLM integration External or self-hosted Fully managed on AWS Explainability Manual or rule-based AI-generated plain-English reports Post-breach insight Limited to logs Behavioral narrative & attacker logic Interactivity Static reports Chat-based forensic assistant Compliance Varies AWS-native security posture

🚀 Development Plan Phase Task Bedrock Use ✅ Phase 1

MVP: Keystroke + Command Graph

✅ Phase 2

TGN + Identity Drift

▶️ Phase 3 LLM Forensic Explainer Claude/Titan via Bedrock ⏳ Phase 4 Chat UI for Investigators Bedrock chat assistant ⏳ Phase 5 Auto-generated Reports Narrative model from Bedrock ⏳ Phase 6 Voice + File Action Layer Combine with ECAPA embeddings for inputs

💼 Licensing / Commercial Use Offered as SaaS or on-prem tool

White-labeled or licensed into existing XDR/SIEMs

Optional tier with Bedrock-powered explainability and assistant

🧠 Innovation Recap ✅ AI-built “cognitive fingerprints” ✅ Multimodal behavior modeling ✅ Real-time anomaly detection ✅ Human-readable forensic timelines ✅ Now with explainable AI powered by Amazon Bedrock

Built With

• amazon-web-services
• fastapi
• next
• react