KNet Designed Applications - Randy Singh

About These Applications

Kalsnet (KNet) Programming Hub is a specialized innovation center developed by KNet Consulting Group, focused on building intelligent, secure, and future-ready digital solutions. Our hub brings together expertise in data science, artificial intelligence, cyber defense, and automation to design programs that simplify complex tasks and deliver actionable insights. At Kalsnet (KNet), we have developed a diverse suite of applications ranging from autonomous data analysts, financial forecasters, anomaly detection systems, and visualization dashboards to cybersecurity threat monitoring, AI-powered agents, and workflow automation tools. Each program is carefully architected using modern toolkits such as Python, Streamlit, Pandas, NumPy, Scikit-learn, Matplotlib, Docker, and Flask, ensuring reliability, scalability, and user-friendly design. Our strength lies in leveraging Agentic-AI systems a new paradigm where multiple specialized agents (e.g., ingestors, cleaners, analyzers, anomaly detectors, segmenters, and modelers) collaborate autonomously to handle complex workflows. This agent-based architecture is especially valuable in cyber defense, where real-time monitoring, pattern recognition, and adaptive response are critical. By embedding Model Context Protocol (MCP), we enable seamless integration between AI models, external tools, and secure workflows, allowing our programs to reason, fetch context, and act with precision. MCP ensures interoperability and trust, while Agentic-AI adds autonomy and adaptability. Through these technologies, KNet empowers organizations to defend digital assets, detect anomalies, forecast risks, and optimize decisions without requiring deep technical expertise from users. Every solution is designed to work as a digital co-pilot, automating repetitive tasks while keeping humans in control of strategy. The benefit to users is clear: faster insights, improved security posture, reduced manual errors, and a scalable framework that adapts to evolving business and cyber challenges. In short, KNet Programming Hub transforms raw data and complex systems into intelligent, secure, and easy-to-use solutions giving our clients the confidence to innovate safely in a digital- first world.

Core libraries and tools include Streamlit for interactive interfaces, Pandas/Numpy for data processing, Matplotlib/Plotly for visualization, and scikit-learn or custom ML models for analytics.

Enterprise-AI-Intelligence-Platform - Randy Singh

The KALSNET AI Vision Enterprise Platform is an interactive enterprise-grade AI simulation and analytics application designed to demonstrate real-time risk intelligence, cybersecurity posture assessment, and executive decision support across commercial, federal, and defense environments. Developed using modern Python technologies including Streamlit for web-based user interfaces, Pandas and NumPy for data engineering and synthetic data modeling, Plotly and Matplotlib for advanced visual analytics, and ReportLab for automated executive reporting, the platform enables users to generate or upload datasets and immediately evaluate operational risk through explainable AI scoring models. The application calculates Agentic Risk Scores, organizational risk categories, and FedRAMP-style readiness metrics while mapping simulated activity to MITRE ATT&CK techniques for cyber threat awareness. In real-world scenarios, organizations can use this platform for executive briefings, AI governance demonstrations, cybersecurity training exercises, fraud analytics simulations, proposal demonstrations, and Zero-Trust readiness assessments. By combining visualization, explainability, and exportable compliance reporting into a single dashboard, the solution helps leadership teams rapidly understand enterprise exposure, prioritize mitigation actions, and improve decision confidence across hybrid cloud and mission-critical environments developed by Kalsnet (KNet) Consulting Group.

Open Application

Agentic-AI-BASED-AUTONOMOUS-DATA-ANALYST- Randy Singh

This Autonomous Data Analyst (Agentic-AI) Application is a Streamlit-based application designed to let users quickly explore, clean, and analyze datasets with minimal effort. It combines traditional Data Science Tools like Pandas (for data handling), NumPy (for numerical operations), Scikit-learn (for clustering and regression), SciPy (for anomaly detection), and Matplotlib library (for charts and visualizations) into a single, easy-to-use interface. The program works through a pipeline of specialized Agentic-AI components (or “agents”) that each perform a focused task: the Ingestor Agent loads datasets from CSV/JSON or generates synthetic data, the Cleaner Agent fixes missing values and removes duplicates, the Summarizer Agent provides statistical overviews, the Anomaly Detector Agent flags outliers using z-scores, the Segmentation Agent applies KMeans clustering to discover hidden groups, and the Modeling Agent builds a quick linear regression model to test predictive relationships. These agents are orchestrated in sequence, allowing users to run partial or full analysis with the click of a button. The system also provides interactive visual insights (pie charts, bar charts, line graphs) and allows exporting cleaned datasets and detailed reports. By automating routine analysis steps, this Agentic-AI approach empowers business users, analysts, and decision-makers to detect trends, spot risks, understand customer groups, and build simple predictive models without writing code. The benefit to users is Speed, Clarity, and Repeatability—Turning raw data into Actionable Insights in just a few clicks.

Open Application

Agentic-AI-Dashboard-with-Top-10-Agentic-AI Use-Cases - Designed By Randy Singh

The Agentic AI Dashboard is an advanced, interactive data intelligence platform developed using Streamlit, Pandas, NumPy, and Matplotlib. Designed by KNet Consulting Group, it enables users to explore, analyze, and visualize performance metrics across multiple Agentic AI use cases — including cybersecurity, finance, healthcare, robotics, and more. Through its intuitive graphical interface, users can either generate synthetic data dynamically using a 0–100 slider or upload their own datasets in CSV, Excel, or JSON formats. The system provides real-time visual analytics through bar, pie, and line charts, and allows seamless export of insights in timestamped files for easy documentation or sharing. With color-coded interactive buttons, a clean professional design, and Reset/Refresh controls for rapid scenario switching, the dashboard delivers a powerful, user-friendly environment for AI-driven decision intelligence, research, and demonstration purposes. Benefit: the Agentic AI Dashboard represents a fusion of automation, analytics, and visualization, empowering users to simulate, understand, and communicate complex AI performance data effortlessly.

Open Agentic-AI Dashboard

Agentic-AI-Cyber-Defense-Framework - Randy Singh

The Agentic-AI-Framework is an interactive Python/Streamlit platform where users can explore autonomous AI agents for cybersecurity. Users can upload or generate sample datasets, run demo agents for Threat Hunting, Adaptive Incident Response, Vulnerability Scanning, Phishing Defense, and Cyber Deception, visualize results with dynamic charts, and export outcomes in CSV or JSON. The system leverages Streamlit, Pandas, NumPy, Matplotlib/Plotly, and integrates tools like LangChain, Scapy, and Shodan to provide real-time insights and automated cyber defense actions. Benefit: Users gain hands-on understanding of autonomous AI agents, see live threat analysis, and can test defense workflows safely.

Open Agentic-AI Framework

Agentic-Cyber-Threat-Hunting

The Agentic Cyber ML application is a web-based cybersecurity demo built with Streamlit, designed for threat detection, analysis, and visualization. It allows users to either generate synthetic network logs or upload real logs (CSV, JSON, Excel) containing timestamps, source/destination IPs, protocols, and byte counts. The program creates numeric features from these logs, such as encoded protocol and source-destination pair counts, and applies IsolationForest from scikit-learn for unsupervised anomaly detection. Logs are scored with an anomaly score and categorized as ALLOW, SUSPECT, or BLOCK, while Admin users can auto-quarantine top anomalous IPs. The app provides visualizations including anomaly histograms (via Plotly Express) and network graphs (via NetworkX and Plotly Graph Objects) to highlight traffic patterns and quarantined nodes. Users can download results in CSV, JSON, or Excel formats and maintain audit logs for tracking all actions. The program is role-based: Admin can generate data, train models, score logs, perform auto-containment, reset the session, and view all audit logs; Analyst can generate data, train models, score logs, and upload files, but cannot auto-contain or reset; Viewer can only generate sample data, upload logs, and view results. This structured approach ensures security, accountability, and controlled access, while providing a user-friendly interface for proactive threat hunting, anomaly detection, and network monitoring.

Open Application

MCP-Cybersecurity-Framework - Randy Singh

The MCP (Model Context Protocol) Framework is a comprehensive platform for monitoring, analyzing, and responding to cybersecurity threats using AI/ML models and automated workflows. Benefit: Provides centralized threat monitoring, predictive alerts, automated responses, and continuous learning to improve overall cybersecurity posture.

Open MCP Framework

Agentic-AI Cyber Threat Detector - Randy Singh

Uses Agentic AI agents to monitor networks, endpoints, and cloud workloads for threats. Built with Streamlit, Pandas, anomaly-detection ML models, and visualization tools, it reduces false positives and adapts in real-time. Benefit: Enables proactive defense against sophisticated cyberattacks.

Open Application

MCP-Cyber-Threat-Monitor - Randy Singh

Provides real-time dashboards to track and visualize threat activity across domains. Built using Streamlit, Matplotlib/Plotly, and the MCP pipeline. Benefit: Helps security teams quickly detect and respond to abnormal behaviors.

Open Application

MCP-Cyber-Threat Dashboard - Randy Singh

Focused on visualization-first monitoring of cyber threat intelligence and AI-driven anomaly detection. Developed with Streamlit, Plotly Dashboards, and data pipelines. Benefit: Gives executives and analysts a clear view of enterprise-wide threats.

Open Application

Agentic-AI-Adaptive-Incident-Response Use-Case - Randy Singh

The Agentic Adaptive Incident Response Streamlit App is an AI-powered platform for managing cybersecurity events. It allows users to generate or upload event data in CSV, JSON, or text formats. The app visualizes incidents with pie charts, bar charts, and time-trend graphs using Matplotlib, Pandas, and NumPy. Events are scored either heuristically or via optional ML models built with scikit-learn. Users can simulate responses like host isolation, IP blocking, or sending SOC alerts. Live actions can be executed via SSH (Paramiko) or cloud SDKs for AWS, GCP, and Azure. Alerts can also be delivered through webhooks or SMTP email notifications. A history log and runbook allow reviewing or replaying all actions. The system supports scalable sample-data generation and dynamic dashboards. Real-world benefits include faster, safer, and standardized incident response, improving SOC efficiency and operational resilience.

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GLOBAL-THREAT-6G-Use-Case - Randy Singh

How the Application Works: The AEGIS-6X application simulates a 6G-enabled, AI-driven threat intelligence platform that ingests real or synthetic threat data, applies machine-learning anomaly detection, and fuses edge and core risk signals into a unified risk score. Role-based access control ensures analysts, commanders, and administrators see only authorized capabilities. The system visualizes threats through interactive dashboards, satellite-style geospatial maps, trend analytics, and digital-twin forecasting. AI models detect anomalies, measure confidence, monitor data drift, and optionally generate human-readable intelligence briefs using LLMs, ensuring explainable, human-in-the-loop decision support. All actions are audit-logged for compliance and NATO/DISA alignment. Tools & Technologies Used: The platform is built using Streamlit for secure web-based visualization and UI, Pandas and NumPy for data processing, and Scikit-learn for machine-learning models such as Isolation Forest and Linear Regression. Plotly provides interactive charts and global satellite-style threat maps. OpenAI LLMs (optional) generate analyst summaries and executive intelligence briefs. FPDF enables export of STANAG-style reports, while JSON-based audit logging ensures traceability and accountability. Real-World Benefits: AEGIS-6X enables faster, more confident decision-making by detecting high-risk threats in real time and presenting them in an intuitive, mission-focused dashboard. It reduces analyst overload through automated ano maly detection and AI-generated summaries while maintaining human oversight. The platform supports JADC2, DISA, and NATO coalition operations by integrating cyber, SIGINT, and kinetic threat indicators into a single operational picture. Its 6G-ready architecture ensures ultra-low latency, edge-based decisions, and survivability in contested environments. Ultimately, AEGIS-6X improves mission readiness, operational resilience, and strategic dominance across multi-domain operations.

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API-Discovery-and-Risk-Analytics-Platform - Randy Singh

This program is a Streamlit-based Enterprise API Discovery and Risk Analytics platform that automatically discovers APIs from traffic data, assesses their security risk, and produces executive-ready reports. It can generate realistic synthetic API traffic or ingest real-world CSV/JSON logs for analysis. The application analyzes HTTP methods, authentication types, and API versions to identify unique endpoints and usage patterns. A risk-scoring engine evaluates each API based on insecure authentication, dangerous HTTP methods, and exposure breadth. Results are presented through interactive tables, metrics, and visual charts for rapid situational awareness. The platform generates downloadable JSON and PDF reports, making findings easy to share with leadership and auditors. Tools used include Python, Streamlit, Pandas, Matplotlib, and ReportLab. In the real world, this solution helps organizations discover shadow APIs, reduce attack surface, improve API governance, support compliance, and proactively manage API security risk across modern enterprise environments.

Open Application

API-Simulation-&-Security-Analytics-Platform - Randy Singh

The API Simulation & Security Analytics Platform is a secure, analyst-driven application designed to support defensive cyber operations, security testing, and operational readiness assessments across Department of Defense environments. The platform enables controlled generation of synthetic API traffic or ingestion of operational log data to evaluate system behavior under normal and adversarial conditions. Built using Python and Streamlit, the application provides an interactive, browser-based interface suitable for rapid deployment in classified, unclassified, or air-gapped networks. Pandas performs structured data ingestion, normalization, and analysis of API request and response activity, while Matplotlib delivers visual analytics to support situational awareness. The system applies deterministic anomaly detection logic to identify indicators of compromise, including authentication abuse, endpoint reconnaissance, and elevated error conditions. Session isolation and reset controls ensure repeatable testing and data integrity. PDF reporting capabilities support auditability, after-action reviews, and compliance documentation. In operational use, the platform enhances API visibility, reduces analyst workload, supports Zero Trust validation, and improves cyber defense readiness without reliance on production systems.

Open Application

F5-BIG-IP-API-POSTURE MANAGEMENT - Randy Singh

This program is an API Posture Management dashboard designed to simulate, analyze, and assess API traffic using an F5 BIG-IP–style record schema. It generates realistic synthetic API traffic or ingests real F5 BIG-IP log data to discover active API endpoints and evaluate how those APIs are being used across the environment. The application analyzes usage patterns, HTTP status codes, error rates, and response latency to assess security, performance, and operational posture. It identifies abnormal behavior such as excessive failures, authentication issues, rate abuse, and backend instability, then assigns threat severity levels ranging from low to critical. The results are mapped to NIST security controls, DISA STIG requirements, and Zero Trust principles to support compliance and continuous verification. Interactive dashboards with tables, pie charts, and bar graphs provide clear visualization of API health and risk. The platform is built using Python with Streamlit for the user interface, Pandas for data analysis, Matplotlib for visualization, and ReportLab for generating DoD-ready PDF reports. In real-world environments, this solution helps security and DevSecOps teams proactively detect API risks, improve performance and resilience, support Zero Trust architectures, meet regulatory requirements, and translate raw API traffic into actionable security and compliance intelligence.

Open Application

API-RUNTIME-&-POSTURE-MANAGEMENT-PLATFORM - Randy Singh

This API Runtime & Posture Management Platform is a comprehensive, enterprise-grade tool designed to simulate, analyze, and visualize API behavior using both synthetic and real F5 BIG-IP–style data. It generates synthetic API traffic based on a configurable slider, simulating realistic API requests, response times, and HTTP status codes, while also supporting the upload of real-world CSV log data. The platform evaluates API performance and security posture by detecting anomalies in response times, identifying errors, calculating severity levels, and generating Zero Trust trust-scores for each API. It combines API discovery and runtime behavior analysis, mapping findings to NIST controls, DISA STIG references, and MITRE ATT&CK techniques, enabling organizations to align operations with cybersecurity best practices. The applicationleverages Python with Streamlit for an interactive web interface, pandas for data manipulation, matplotlib and seaborn for heatmaps and pie charts, and ReportLab for PDF export of DoD-ready reports. Users can toggle between Executive, SOC, and Engineer views to get tailored insights, while the dashboard highlights anomalies, threats, and provides actionable mitigation recommendations. Heatmaps and pie charts offer visual summaries of risk exposure, anomalies, and status code distributions, facilitating rapid decision-making. A robust reset function ensures fresh data generation, while the PDF export allows seamless reporting and compliance documentation. In the real world, this program empowers organizations to monitor API health, detect security threats, optimize performance, support compliance audits, and enhance situational awareness in both operational and security operations contexts. By integrating threat severity scoring, zero-trust assessment, and compliance mappings, it provides a proactive framework for managing API risks and ensuring resilience in complex enterprise environments.

Open Application

Cyber-Threat-Hunting-Studio-AI/ML - Randy Singh

The KNet Cyber Threat Hunting Studio – AI/ML Enterprise Platform is an interactive cybersecurity analytics application built using Python and Streamlit. It allows users to generate synthetic security telemetry or upload real-world CSV data representing user behavior, API usage, command activity, and data movement. The platform performs rule- based threat hunting to detect suspicious logins, API abuse, command misuse, and potential data exfiltration. These findings are automatically mapped to the MITRE ATT&CK framework, providing industry-standard adversary context. In parallel, an AI/ML anomaly detection model (Isolation Forest) analyzes behavioral patterns to identify unusual activity that may not trigger traditional rules. The application provides record-level explainability, showing exactly why a specific event was flagged. It also generates AI-style remediation playbooks, offering actionable containment and prevention guidance for security teams. Visual analytics, including pie charts and interactive tables, help analysts quickly understand threat distribution. A one-click executive PDF report summarizes findings for leadership and compliance stakeholders. Real-world benefits include faster threat detection, reduced false positives, improved incident response readiness, standardized security reporting, and a practical bridge between SOC operations and executive decision-making.

Open Application

Supply-Chain-Risk-Estimator - Randy Singh

Analyzes SBOMs (Software Bills of Materials) for vulnerabilities, licensing issues, and trust risks. Uses Streamlit, Pandas, Matplotlib, and AI classifiers. Benefit: Allows proactive risk classification into Low, Medium, or High with exportable reports.

Open Application

Dental-Practice-Manager - Randy Singh

A management tool for dental offices supporting scheduling, billing, and patient records. Built on Streamlit, SQLite/Postgres, and task automation APIs. Benefit: Streamlines clinic operations and improves patient care efficiency.

Open Application

Synthetic-F5-NETSCOUT-PCAP-Generator - Randy Singh

Generates synthetic PCAP traffic files for network simulations. Uses Streamlit, Scapy, and traffic emulation libraries. Benefit: Useful for testing enterprise-grade network monitoring tools without real-world risks.

Open Application

Synthetic-Data-Generator - Randy Singh

Creates large, realistic datasets for AI/ML model training and testing. Built with Streamlit, Faker, Pandas, and export modules. Benefit: Provides abundant training data without privacy or compliance issues.

Open Application

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