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.
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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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