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.
Click below to show all applications:
If Application Sleeping then Just Click On it to Wake It Up:
Iran’s Critical Energy Infrastructure Intelligence Dashboard is a web‑based analytics platform designed to provide deep visibility into Iran’s power generation infrastructure, including location, fuel type, operational capacity, and spatial distribution. It ingests both user‑uploaded datasets and synthetic fallback data, filters by fuel type and generation capacity, and presents the information through interactive maps, graphical charts, and summary metrics that highlight energy asset distribution and resilience. The platform leverages modern data tools including Streamlit for user interface, Plotly Express for geospatial and chart visualizations, and PDF generation libraries for robust reporting capabilities. By mapping latitude and longitude to major nearby cities, the dashboard enhances situational awareness and allows policy makers, defense analysts, and infrastructure planners to evaluate the geographic spread of critical assets. Fuel types such as gas, oil, hydroelectric, solar, and wind are explained for their operational relevance, helping analysts understand energy dependencies and vulnerabilities. Advanced filters and KPI tiles quantify total plant count, cumulative capacity, and the proportion of renewable sources, enabling rapid assessment of Iran’s energy portfolio structure. The interactive map displays plant locations with visual cues for size and fuel category, supporting deep geospatial analysis of energy infrastructure. Capacity by fuel and top 10 plant charts offer insights into strategic nodes that may be critical for national energy security. In the context of the current Iran–United States conflict, where energy facilities have been discussed as potential targets and where global energy routes such as the Strait of Hormuz are geopolitically sensitive, the dashboard adds operational value by identifying infrastructure clusters most likely to affect civilian supply, export capabilities, or regional stability if disrupted ([Encyclopedia Britannica][1]). The platform’s export tools allow stakeholders to download filtered data and analytical summaries in CSV, JSON, and PDF formats, supporting cross‑agency interoperability and documentation needs. Such exports are valuable for briefings, contingency planning, risk modeling, and historical comparison. Overall, the dashboard supports evidence‑based decision making by illuminating where energy assets are located, how they contribute to national capacity, and how they relate to broader geopolitical and security considerations during times of heightened tension. It transforms raw energy infrastructure data into actionable intelligence that supports resilience planning, critical infrastructure protection, and energy‑security strategy. [1]: https://www.britannica.com/event/2026-Iran-war?utm_source=chatgpt.com "2026 Iran war | Explained, United States, Israel, Strait of ..."
Open ApplicationThe Kalsnet (KNet) AI Controlled War Command Platform is an advanced AI-Driven Decision Support System designed to enable real-time operational awareness, intelligent threat evaluation, and optimized mission execution. The application integrates Role-Based Access Control (RBAC), allowing Commanders to focus on strategic decisions while Analysts concentrate on data-driven intelligence and pattern recognition. It leverages Streamlit for an interactive web interface, combined with powerful data processing libraries such as Pandas and NumPy to generate and manage operational datasets. The platform applies Graph Theory using NetworkX to model decision pathways, threat relationships, and logistics networks, enabling structured Decision Intelligence and Predictive Analytics. Visualization capabilities powered by Matplotlib and Folium provide Geospatial Intelligence (GEOINT) and dynamic operational dashboards for situational awareness. The integration of YOLO (You Only Look Once) introduces Computer Vision for real-time object detection, supporting surveillance and target identification use cases. Key analytical engines such as the COA (Course of Action) Engine, Threat Scoring Model, and Logistics Optimization Module utilize mathematically driven formulas to deliver Actionable Insights and mission success probabilities. The platform also supports Multi-Format Data Export (CSV, JSON, PDF, WORD), ensuring seamless reporting and interoperability across enterprise systems. In real-world environments, this solution delivers significant value by enhancing Operational Efficiency, improving Threat Prioritization, enabling Faster Decision Cycles, and supporting Data-Driven Command & Control (C2). Its modular and extensible architecture makes it highly adaptable for defense, cybersecurity, and critical infrastructure applications requiring Real-Time Intelligence, AI Enablement, and Mission-Critical Decision Making.
Open ApplicationThe 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 ApplicationThis Agentic AI Financial Fraud Prevention application is an interactive risk analytics dashboard designed to simulate how autonomous AI agents assist in fraud and financial crime detection. Built using Streamlit, the platform provides a web-based interface for generating or uploading transaction data and dynamically assessing risk. Python, Pandas, and NumPy are used for data processing, synthetic transaction generation, and risk calculations. The system assigns a baseline Risk Score to each transaction and enhances it through an Agentic AI Engine, which adjusts risk dynamically based on selected scenarios such as Fraud Detection, AML Monitoring, Sanctions, or Insider Risk. Scenario-based multipliers emulate how intelligent agents adapt decision-making in real time. Plotly is used to visualize risk distributions, alert trends, and agentic risk scores for analyst awareness. The platform automatically classifies transactions into Auto-Clear, Review, or Escalate decisions, reducing false positives and analyst workload. Operational efficiency metrics quantify analyst hours saved and alert reduction. Explainable AI insights describe why actions were taken, supporting transparency and trust. ReportLab enables one-click generation of compliance-ready PDF reports. In the real world, this approach improves detection accuracy, accelerates investigations, enhances regulatory compliance, and demonstrates how Agentic AI can modernize enterprise fraud and AML operations.
Open ApplicationThis 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 ApplicationThe 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 DashboardThis interactive Streamlit-based program enables SOC teams and researchers to visualize, detect, and explain potential cyber anomalies. It generates synthetic or loads real log/EDR datasets, detects threats such as credential stuffing, suspicious exfiltration, or command execution, and maps them against MITRE ATT&CK techniques. Tools used include Python, Pandas, NetworkX, PyVis, Matplotlib, Streamlit, with optional OpenAI integration. Users can toggle light/dark mode, simulate attacks, and export CSV/JSON/Excel/PNG. Benefit: Hands-on training, clear attacker visualization, and professional reporting for SOC analysts, trainers, and enterprises.
Open ApplicationThe 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 ApplicationAllows users to explore autonomous AI agents for cybersecurity. Users can run demo agents for threat hunting, adaptive response, phishing defense, and cyber deception. Built with Streamlit, Pandas, NumPy, Matplotlib/Plotly, LangChain, Scapy, and Shodan. Benefit: Provides live threat analysis, automated defense workflows, and educational experience in autonomous AI security operations.
Open ApplicationComprehensive AI/ML-driven platform for monitoring, analyzing, and responding to cyber threats. Uses Streamlit, Pandas/NumPy, Matplotlib/Plotly, and custom ML models. Benefit: Centralized threat monitoring, predictive alerts, automated responses, and continuous improvement of security posture.
Open ApplicationMonitors networks, endpoints, and cloud workloads for anomalies using Streamlit, Pandas, anomaly-detection ML models, Matplotlib/Plotly. Reduces false positives and adapts in real-time. Benefit: Enables proactive defense against cyberattacks.
Open ApplicationReal-time dashboards to track and visualize threat activity using Streamlit, Matplotlib/Plotly, and MCP pipelines. Benefit: Quickly detects abnormal behaviors and supports rapid response.
Open ApplicationVisualization-first monitoring of cyber threat intelligence with AI-driven anomaly detection. Built with Streamlit, Plotly, and data pipelines. Benefit: Provides executives and analysts a clear view of enterprise-wide threats.
Open ApplicationAI-powered platform for managing cybersecurity incidents. Generate/upload event data in CSV/JSON, visualize incidents with pie/bar/time-trend charts, simulate responses (host isolation, IP blocking), and export results. Built with Streamlit, Matplotlib, Pandas, NumPy, scikit-learn, Paramiko, and cloud SDKs. Benefit: Faster, standardized, and safe incident response for SOC teams.
Open ApplicationHow 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.
GLOBAL-THREAT-6G-Use-CaseThis 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.
API-Discovery-and-Risk-Analytics-PlatformThe 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 ApplicationThis 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 ApplicationThis 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 ApplicationThe Real-Time Battlefield Intelligence & Target Prioritization System is an interactive AI-driven application built using Streamlit that simulates how modern defense systems analyze and prioritize targets in real time. The application generates synthetic battlefield data using NumPy and structures it through Pandas DataFrames, representing key intelligence attributes such as Threat Level, Strategic Value, Distance, and Civilian Risk. A core feature of the system is its AI-based Priority Scoring model, which applies weighted logic to rank targets by combining threat severity and mission importance while reducing scores for high civilian risk, thereby ensuring ethically informed decision-making. The user interacts with the system through an intuitive graphical interface where actions like -Attack Simulation-, -Defense Mode-, and -Resource Optimization- can be triggered with a single click. The Attack Simulation selects the highest-priority targets for engagement, mimicking offensive military strategies, while Defense Mode identifies high-threat entities to simulate surveillance and early-warning systems. Resource Optimization further enhances decision-making by selecting targets that offer maximum strategic value with minimal collateral risk, reflecting efficient allocation of limited operational resources. The platform also includes an Analytics Dashboard powered by Matplotlib, which visualizes threat distributions, priority rankings, and threat trends over time, enabling users to quickly interpret complex data patterns. Additionally, the system supports exporting results in CSV, JSON, and PDF formats using ReportLab, making it suitable for reporting, auditing, and intelligence sharing. In real-world scenarios, this type of system provides significant value by supporting military command centers, cybersecurity operations, and emergency response units in making fast, data-driven decisions. Its emphasis on Explainable AI (XAI) ensures transparency, allowing analysts to understand how decisions are derived rather than relying on opaque algorithms. Overall, the application demonstrates how AI, data analytics, and visualization tools can be integrated into a unified platform to enhance situational awareness, reduce risk, optimize mission outcomes, and improve strategic planning in high-stakes environments.
Open ApplicationThe AI-Powered Financial Cyber Range Platform is an advanced web-based simulation environment built using Python and Streamlit, providing an interactive interface for both learning and testing cybersecurity operations. The platform uses Pandas and NumPy to generate synthetic financial datasets, including account IDs, balances, and transaction activity, enabling realistic yet safe experimentation. Red Team operations simulate real-world cyber attacks using vectors such as Phishing, Malware Injection, API Exploits, and Insider Threats, compromising a percentage of accounts and reducing their balances to reflect financial theft. Blue Team functions perform Counter Attack, detecting compromised accounts, marking them as Recovered, and partially restoring lost funds, representing incident response and mitigation strategies. The platform integrates AI-based Fraud Detection, identifying high-risk accounts with unusual transactions or low balances, and Threat Hunting, which highlights suspicious patterns for further investigation. Users can view the SOC Analyst Dashboard, which presents real-time alerts and facilitates continuous monitoring of system security. The platform provides interactive metrics, including total balance, number of compromised accounts, and total losses, along with visualizations using Matplotlib for clear attack impact insights. Attack logs are recorded, allowing users to analyze compromised account details. The platform also supports multi-format export (CSV, JSON, PDF) using FPDF for reporting, auditing, or training purposes. Key features such as Red Team / Blue Team operations, Threat Hunting, AI Detection, and SOC monitoring make this application ideal for cybersecurity training, financial risk analysis, executive briefings, and red/blue team exercises. The benefits of this platform in the real world include Enhancing Situational Awareness, Improving Incident Response, Understanding Attack Vectors, and evaluating mitigation strategies, all in a Safe, Controlled, and Reproducible Environment. This tool allows organizations to Simulate, Analyze, and learn from Cyberattacks without Risking actual financial data, making it a valuable resource for both technical and leadership audiences.
Open ApplicationThe 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 ApplicationThe **Agentic AI Cyber Range Platform** is a web-based cybersecurity training and simulation environment designed to demonstrate how modern organizations detect, investigate, and respond to cyber threats. The application is developed using the Python framework Streamlit, which provides an interactive dashboard interface that allows users to simulate cyber-attack scenarios and defensive operations in real time. The platform contains a structured **attack library of common enterprise cyber threats**, including phishing, ransomware, SQL injection, insider threats, and cloud account compromise. Each attack scenario includes a clearly defined objective, target systems, entry method, and vulnerability, enabling the system to simulate how adversaries exploit weaknesses in enterprise environments. When a user initiates an attack, the system simulates **Red Team offensive activity**, representing adversarial tactics used to compromise systems. The platform then demonstrates how defensive security operations would respond to the attack. Security monitoring and detection are represented through **Blue Team capabilities**, showing how security monitoring tools such as Splunk or Microsoft Sentinel detect abnormal behavior through log analysis and network monitoring. The platform further simulates the work of a **Security Operations Center (SOC)** analyst who investigates alerts, analyzes security logs, identifies indicators of compromise, reconstructs the attack timeline, and determines the root cause of the incident. Once the attack is confirmed, the system demonstrates the **incident response process**, including isolating compromised systems, resetting credentials, applying security patches, and restoring systems to a secure operational state. The platform also includes a **threat hunting capability**, where analysts proactively search enterprise systems for hidden attacker activity that may not have triggered alerts. All activities performed by the simulated cyber defense teams are recorded in structured logs using the Python data analysis library pandas. These logs are visualized through interactive security dashboards generated using Plotly, enabling users to analyze attack response patterns and team activities. In addition, the system provides the ability to export incident results and reports in multiple formats including CSV, JSON, and PDF using ReportLab. In real-world environments, cyber-range platforms like this are used by enterprises, government agencies, and cybersecurity training programs to Simulate cyber incidents in a safe and controlled environment. They enable security professionals to practice cyber-defense operations, improve incident response readiness, and better understand how attackers exploit vulnerabilities. By modeling the full lifecycle of a cyber incident—from attack initiation through detection, investigation, containment, and recovery—the platform provides a realistic representation of how modern cybersecurity teams operate within enterprise Security Operations Centers.
Open ApplicationThis program is a Streamlit-based Enterprise Cyber Range SaaS simulation platform designed to emulate real-world Security Operations Center (SOC) environments in a safe, controlled setting. It allows users to generate synthetic cyberattack data using Faker, simulate multi-tenant SaaS environments, and analyze attack activity across different organizational roles (Admin, Instructor, Analyst). The system calculates dynamic risk scores based on severity, attack success, and response time, providing a realistic threat-scoring model. It integrates Scikit-learn’s Isolation Forest algorithm to perform AI-driven anomaly detection, identifying suspicious activity based on behavioral patterns. The dashboard presents enterprise SOC metrics, attack simulations, and visual analytics using Pandas, NumPy, and Matplotlib, offering pie charts and real-time operational insights. The platform maps attack types to MITRE ATT&CK techniques, enhancing compliance alignment and cybersecurity training realism. Users can export reports in CSV, JSON, and PDF formats (using ReportLab), making it suitable for documentation, compliance reviews, and executive briefings. Real-world value: This application serves as a hands-on cyber training environment for workforce development, SOC analyst training, tabletop exercises, and academic cyber labs. It demonstrates multi-tenant SaaS architecture concepts, AI-based threat detection, automated risk scoring, and enterprise reporting capabilities. Organizations can use this type of platform to improve cyber readiness, test incident response workflows, evaluate AI anomaly detection effectiveness, and strengthen overall cybersecurity posture without exposing live production systems.
Open ApplicationThe proposed Cybersecurity Attack Simulation and Visualization Platform is an interactive, web-based solution designed to demonstrate the Top 8 most common cyberattack scenarios and their real-time impact on organizational environments. The application enables users to generate realistic synthetic attack data or upload customer-provided CSV datasets for analysis, supporting training, demonstrations, and executive-level briefings without exposure to sensitive production systems. Developed using Python and Streamlit, the platform delivers an intuitive dashboard featuring executive metrics, graphical threat distributions, and detailed attack analytics. Data processing and correlation are performed using Pandas and NumPy, while Matplotlib provides visual representation of threat severity and trends. The solution includes automated CSV and PDF report generation to support documentation, leadership reviews, and compliance reporting. The platform aligns with NIST 800-53 and FedRAMP continuous monitoring principles by demonstrating logging, visibility, incident awareness, and reporting workflows. Intended users include DoD organizations, federal agencies, enterprise security teams, educators, and managed security providers. Real-world benefits include improved cyber awareness, enhanced communication between technical and non-technical stakeholders, support for Zero Trust initiatives, and risk-informed decision-making through clear, actionable cybersecurity insights.
Open ApplicationThe proposed Kalsnet Top 8 Cyber Attacks Interactive Defense Demo is designed to align with key NIST SP 800-53 and FedRAMP Moderate control families by demonstrating continuous monitoring, threat visibility, and incident awareness capabilities in a controlled environment. The platform supports AU-2 (Event Logging) and AU-6 (Audit Review, Analysis, and Reporting) through structured data ingestion, visualization of security events, and exportable audit-ready reports. Risk scoring, severity categorization, and threat trend visualization support RA-3 (Risk Assessment) and RA-5 (Vulnerability Monitoring and Scanning) by enabling organizations to assess exposure and prioritize remediation activities. Interactive attack scenario analysis and severity classification align with IR-4 (Incident Handling) and IR-5 (Incident Monitoring) by demonstrating detection, analysis, and response workflows. The dashboard’s executive metrics and reporting features support CA-7 (Continuous Monitoring) by providing ongoing visibility into security posture and threat trends. User-driven data uploads and synthetic data generation enable safe demonstration of PL-2 (System Security Plan) concepts without the use of sensitive operational data. The platform’s role as a training and awareness tool supports AT-2 (Security Awareness Training) by visualizing common attack vectors and their business impact. Overall, the solution provides a FedRAMP-aligned demonstration capability that supports ATO readiness discussions, Zero Trust awareness initiatives, compliance validation activities, and cybersecurity governance decision-making.
Open ApplicationThe proposed Defense-in-Depth Cybersecurity Strategy Demo is a web-based, interactive platform designed to illustrate layered cybersecurity controls and operational risk management in accordance with DoD RMF and ATO readiness requirements. The application enables the generation of realistic synthetic security events or ingestion of customer-provided CSV datasets, ensuring demonstration and analysis without exposing sensitive production systems. Developed using Python and Streamlit, the platform delivers an intuitive interface for both technical and executive stakeholders, providing real-time visibility into user activity, threat events, and control effectiveness. The solution simulates core defense-in-depth layers including Identity and Access Management (IAM), Firewalls, Endpoint Detection and Response (EDR/NDR), IDS/IPS, SIEM, Cloud Security, DLP, Incident Response, and Security Awareness, enabling organizations to map operational capabilities to NIST SP 800-53 Moderate/FedRAMP controls required for RMF authorization. Logging, correlation, and incident simulation demonstrate AU-2 (Event Logging), AU-6 (Audit Review), IR-4/IR-5 (Incident Handling/Monitoring), RA-3/RA-5 (Risk Assessment/Vulnerability Monitoring), and CA-7 (Continuous Monitoring). Security awareness and attack visualization support AT-2 (Security Awareness Training), while synthetic data generation aligns with PL-2 (System Security Plan), protecting operational data. Intended users include DoD components, federal agencies, SOC analysts, enterprise security teams, and cybersecurity instructors. Real-world benefits include enabling ATO readiness exercises, tabletop threat simulations, executive cyber posture visualization, identification of detection gaps, and support for risk-informed decision-making. The platform provides a repeatable, safe environment for compliance validation,threat demonstration, and strategic planning, bridging technical cybersecurity operations with governance and risk management priorities required for RMF-based authorization.
Open ApplicationApplication Description: The proposed Defense-in-Depth Cybersecurity Strategy Demo is a web-based, interactive platform designed to illustrate layered cybersecurity controls and operational risk management in accordance with DoD RMF and ATO readiness requirements. The application enables the generation of realistic synthetic security events or ingestion of customer-provided CSV datasets, ensuring demonstration and analysis without exposing sensitive production systems. Developed using Python and Streamlit, the platform delivers an intuitive interface for both technical and executive stakeholders, providing real-time visibility into user activity, threat events, and control effectiveness. The solution simulates core defense-in-depth layers including Identity and Access Management (IAM), Firewalls, Endpoint Detection and Response (EDR/NDR), IDS/IPS, SIEM, Cloud Security, DLP, Incident Response, and Security Awareness, enabling organizations to map operational capabilities to NIST SP 800-53 Moderate/FedRAMP controls required for RMF authorization. Logging, correlation, and incident simulation demonstrate AU-2 (Event Logging), AU-6 (Audit Review), IR-4/IR-5 (Incident Handling/Monitoring), RA-3/RA-5 (Risk Assessment/Vulnerability Monitoring), and CA-7 (Continuous Monitoring). Security awareness and attack visualization support AT-2 (Security Awareness Training), while synthetic data generation aligns with PL-2 (System Security Plan), protecting operational data. Intended users include DoD components, federal agencies, SOC analysts, enterprise security teams, and cybersecurity instructors. Real-world benefits include enabling ATO readiness exercises, tabletop threat simulations, executive cyber posture visualization, identification of detection gaps, and support for risk-informed decision-making. The platform provides a repeatable, safe environment for compliance validation, threat demonstration, and strategic planning, bridging technical cybersecurity operations with governance and risk management priorities required for RMF-based authorization.
Open ApplicationApplication Description: The SIEM Enterprise Platform provides an integrated, defense-in-depth cybersecurity capability designed to enhance situational awareness, threat detection, and compliance across Department of Defense information systems. The solution ingests and normalizes security telemetry from on-premises, cloud, and hybrid environments, including Kafka-based pipelines, AWS CloudWatch, and Azure Sentinel, into a unified analytic framework. Advanced correlation and User and Entity Behavior Analytics (UEBA) identify anomalous activity, insider threats, and compromised credentials that may evade traditional signature-based controls. The platform maps detected events and alerts directly to MITRE ATT&CK techniques and NIST SP 800-53 controls, enabling clear traceability across the Risk Management Framework (RMF) lifecycle. Role-based access controls enforce least-privilege principles while supporting SOC analyst, administrator, and executive workflows. Built-in reporting and visualization capabilities provide actionable intelligence to operators and leadership while generating audit-ready artifacts to support Authorization to Operate (ATO) decisions. This capability strengthens cyber defense readiness, reduces mean time to detect and respond, and aligns operational security outcomes with mission assurance objectives.y operations with governance and risk management priorities required for RMF-based authorization.
Open ApplicationAnalyzes SBOMs (Software Bill of Materials) for vulnerabilities, licensing, and trust risks. Built with Streamlit, Pandas, Matplotlib, and AI classifiers. Benefit: Proactive risk classification with exportable reports.
Open ApplicationManages dental offices (scheduling, billing, patient records) with Streamlit, SQLite/Postgres, and task automation APIs. Benefit: Streamlines operations and improves patient care efficiency.
Open ApplicationGenerates synthetic PCAP traffic files for network simulations using Streamlit, Scapy, and traffic emulation libraries. Benefit: Safely tests network monitoring tools without real-world risks.
Open ApplicationCreates large, realistic datasets for AI/ML training/testing using Streamlit, Faker, and Pandas. It has following features: Generate Data, Discover APIs, Move to Repository, Display Repository, Upload JSON Logs, Clear Repository. Benefit: Provides abundant data without privacy or compliance concerns.
Open ApplicationFetches historical stock data from Yahoo Finance, calculates P/E ratios, dividends, revenue, net margins, and one-year returns. Uses Streamlit, Pandas, NumPy, Matplotlib, PdfPages, python-pptx for reporting. Benefit: Quick side-by-side company comparison, reasoning-based rankings, and professional report exports.
Open Application