Challenges:

Invinsense, as a security platform, offers numerous features and modules that are rapidly expanding. It is essential to regularly update all new security innovations and requirements. All processes are meticulously documented and stored in a knowledge base. Effectively retrieving specific information from the document repository poses a challenge. The extensive knowledge base increases the risk of missing crucial information, which can be perilous for a security company. Additionally, internal experts sometimes avoid retrieving information due to the time-consuming nature of navigating the large knowledge base.

• Large and rapidly growing knowledge base.
• Risk of missing critical information due to the extensive repository.
• Potential security risks from not accessing the correct information.
• Time-consuming process to retrieve information from the large knowledge base.
• Internal experts sometimes avoid retrieving information due to the lengthy process.
• Regular updates for all new security innovations and requirements.
• Comprehensive documentation of all processes.
• Efficient retrieval of specific information from the documents repository.

Solution for specific Challenges:

Large and Rapidly Growing Knowledge Base:
Solution: Use AI to continuously crawl, index, and categorize the expanding knowledge base to ensure up-to-date and well-organized information.
Risk of Missing Critical Information:
Solution: Employ AI to highlight and prioritize critical information, ensuring that important updates are easily accessible and not overlooked.
Potential Security Risks:
Solution: Integrate AI-driven security checks to ensure that retrieved information is accurate and
relevant, reducing the risk of security breaches due to misinformation.
Time-Consuming Information Retrieval:
Solution: Utilize AI to provide quick and precise search results, minimizing the time spent on
finding relevant information.
Internal Experts Avoiding Information Retrieval:
Solution: Create an intuitive AI-driven interface that simplifies the search process, encouraging
experts to engage with the knowledge base without hesitation.

Proposed Solution

• Implement a generative AI system to maintain and update documentation with new security innovations and requirements automatically.
• Use AI to index and categorize the knowledge base effectively for easier retrieval.
• Develop an AI-powered search tool that can understand and respond to natural language
• Queries, improving information retrieval efficiency.

Solution & Approach

User Authentication:

● SOC Users: They authenticate via AWS Cognito User Pool, which may integrate with various identity providers (e.g., Google) for single sign-on (SSO).
● AWS WAF: Provides security for the frontend applications by filtering and monitoring incoming HTTP/HTTPS requests.
Frontend:
● AWS CloudFront and S3: The React application is hosted in an S3 bucket, served through CloudFront for efficient content delivery.
Backend API:
● API Gateway: Acts as the entry point for all API requests, routing them to appropriate
backend services.
● AWS Lambda (FastAPI): Handles the business logic for the chatbot and other backend functionalities.
External Knowledge Integration:
● S3 Bucket for Documents: Stores external documents and knowledge bases that can be
queried by the chatbot for additional information.
● Internet: External knowledge sources can be accessed and stored in the S3 bucket for
reference.
LLM Integration:
● Amazon Bedrock Claude 2 and Cohere: Provides natural language processing capabilities
for understanding and generating human-like responses.
● Tasks: These LLM services are invoked by the backend to generate responses based on
user queries.
Data Storage and Retrieval:
● Amazon Aurora (Postgres with pgvector): Stores structured data, including conversation
history and bot interactions, enabling efficient querying and retrieval.
● DynamoDB Conversation/Bot Table: Stores real-time conversation logs for quick access
and analysis.
Streaming and Response Handling:
● Streaming Response Lambda: Utilizes API Gateway to provide real-time streaming
responses to user queries.
● Athena: Analyzes conversation logs stored in the S3 bucket to gain insights into user
interactions and bot performance.
Administrator Access:
● Athena and S3 Buckets: Administrators can analyze usage data and logs to monitor
system performance and usage patterns.
Event Processing:
● Amazon ECS: Manages and orchestrates containerized applications and tasks

EventBridge Pipes: Facilitates event-driven processing, linking various AWS services and
ensuring smooth data flow between components.

Architecture Flow

Authentication Layer

• Users authenticate via Amazon Cognito User Pool
• Federation with Identity Providers (Google)
• Secure, role-based access to Gen AI services

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2. Data Ingestion & Processing

• Input Documents are processed using Amazon Textract for text extraction
• Extracted & processed data is stored in Amazon S3
• S3 acts as the primary data lake for AI processing and model interactions

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3. Gen AI Processing Pipeline

• Amazon Bedrock provides foundation models for:
  • Text generation
  • Document summarization
  • Question answering
• AWS Lambda executes serverless AI workflows, orchestration, and prompt handling

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4. Knowledge Management Layer

• Invinsense Knowledge Base acts as a centralized AI-driven knowledge repository
• Amazon Comprehend performs NLP tasks such as:
  • Entity extraction
  • Key phrase detection
  • Content classification
• Amazon DynamoDB stores:
  • Conversation context
  • Metadata
  • Knowledge references

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5. Output & Integration Layer

• Amazon EventBridge enables event-driven AI workflows
• Amazon API Gateway exposes secure REST APIs for AI services
• Amazon Athena provides analytics on AI-generated insights and system usage

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Key Gen AI Capabilities

Document Intelligence

• Automated document processing using Textract
• Intelligent content extraction and classification
• Searchable, AI-ready document repository

Conversational AI

• Natural language understanding using Amazon Comprehend
• Context-aware, intelligent responses powered by Bedrock
• Secure and scalable conversational workflows

Knowledge Discovery

• Bedrock-powered content generation
• Automated knowledge base creation
• Intelligent insight extraction from enterprise data

Analytics & Reporting

• AI-driven data analysis
• Automated insight and summary generation
• Predictive and trend-based analytics

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Data Flow Summary

1. Input: Documents and user queries
2. Processing: Textract → Bedrock → Comprehend → Lambda
3. Storage: S3 → DynamoDB → Invinsense Knowledge Base
4. Output: AI-generated insights delivered via the Invinsense platform
5. Analytics: Athena for performance and insight analysis

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Integration Points

• Frontend: Invinsense Platform UI
• Backend: AWS-native Gen AI services
• Storage: Amazon S3 (documents), DynamoDB (metadata & context)
• Compute: AWS Lambda (serverless processing)
• Analytics: EventBridge + Athena

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This architecture outlines a scalable, secure, and fully serverless Gen AI solution built on AWS services and tightly integrated with the Invinsense platform to enable intelligent document processing, knowledge discovery, and business insights.

Key Benefits

Scalability:
● With the help of a data pipeline newly created documents will be automatically added and available.
● The solution is based on serverless components (Lambda, API Gateway) and managed services (Bedrock, Aurora, DynamoDB) to scale automatically with demand.
● Automated updation to the knowledge base referred by Electromech AWS Gen AI solution highly reduces the risk of missing any critical security information.
Security:
● Electromech leverages AWS cloud computing GEN AI services which is privately available at customer accounts only.
● Customer Data will never be used to train AWS LLM models.
● Customer Data is never publicly available / published over the internet.
Efficiency:
● Automated Data pipeline and Serverless scalable AWS infrastructure design easily allow applications to introduce any updates very fast. Almost at no time and without much effort.
Flexibility:
● Supports integration with external knowledge sources and third-party identity providers.
Real-time Processing:
● Enables real-time data streaming responses and event-driven architecture for responsive
user interactions.

Conclusion

Using AWS GEN AI services, a secure information retrieval system with a simple, intuitive interface has been delivered.
● Employee efficiency has significantly increased, allowing them to retrieve accurate information within seconds.
● This has led to a heightened self-motivation to use the GEN AI-driven system in daily activities.
● Infopercept has seen employee efficiency improve by nearly 2x to 10x across various departments.