Certified Advanced AI-Enabled Battle Management System Operator (CAIBMS-O)

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Certified Advanced AI-Enabled Battle Management System Operator (CAIBMS-O)

Operational AI Red Teaming & Security for Next-Gen Battle Management Systems (BMS)

Duration: 2 Days
Certification: Certified Advanced AI-Enabled Battle Management System Operator (CAIBMS-O)
Level: Advanced
Delivery: Instructor-led (Virtual or In-Person)
Target Audience:

  • DoD AI specialists & BMS engineers
  • C4ISR & Joint All-Domain Command and Control (JADC2) professionals
  • Cyber warfare & electromagnetic spectrum operations (EMSO) experts
  • ISR analysts, SIGINT professionals, and defense technologists
  • Air, Space, and Cyber domain specialists
  • Defense contractors & system integrators

Prerequisites: Background in cybersecurity, AI/ML, electronic warfare (EW), SIGINT, ISR, or command & control (C2) systems is recommended. Familiarity with AI-driven battle management, autonomous decision support, and multi-domain operations is beneficial but not required.

 

The CAIBMS-O certification is a next-generation training program designed to equip military and defense professionals with expertise in AI-enabled Battle Management Systems (BMS). This course integrates AI-driven decision support, automated threat analysis, multi-domain electromagnetic spectrum operations (EMSO), and adversarial testing methodologies to ensure resilient and secure AI-driven C4ISR and JADC2 capabilities.

Participants will engage in AI red teaming and adversarial testing exercises, learning how to identify vulnerabilities, execute adversarial AI attacks, and implement countermeasures for securing AI-powered battle management systems in air, land, sea, space, and cyber warfare domains.

Learning Objectives

By the end of this certification program, participants will be able to:

  • Master AI-Enabled Battle Management Systems (BMS) in Multi-Domain Operations (MDO): Understand how AI, ISR, SIGINT, EMSO, and autonomous decision support integrate into modern battle management.
  • Identify AI, Cyber, and RF Threats in AI-Driven Battle Management Systems: Recognize cyber-electromagnetic attacks, adversarial AI manipulation, and data poisoning threats targeting BMS.
  • Execute AI-Enhanced Red Teaming Tactics Against BMS: Apply AI deception, adversarial cyber-physical attacks, and spectrum-based SIGINT exploitation against real-time BMS decision-making systems.
  • Conduct Red Teaming for AI-Powered C4ISR & JADC2 Networks: Perform adversarial testing of AI-enhanced C2 networks, ISR fusion nodes, and real-time threat assessment platforms.
  • Develop Defensive & Offensive Strategies for AI-Driven Battle Management: Implement countermeasures for AI adversarial attacks, spectrum-based deception, and cyber-electromagnetic security in next-gen BMS.

Program Curriculum

Part 1: Foundations of AI-Enabled Battle Management Systems

Module 1: Introduction to Next-Gen AI Battle Management Systems

  • Evolution of BMS: From Legacy to AI-Driven Decision Support
  • Role of AI in modern C4ISR & JADC2 architectures
  • Threat landscape: AI-enabled spectrum warfare, ISR deception, cyber-manipulation of autonomous decision systems
  • Case Study: AI-driven BMS vulnerabilities in modern battlefield engagements

Module 2: AI-Driven Decision Support & Threat Analysis in BMS

  • Neural network-based real-time battlefield decision-making
  • AI-powered ISR fusion: Autonomous threat identification & classification
  • Case Study: Attacking an AI-powered ISR fusion system with adversarial ML techniques

Module 3: Adversarial AI Attacks Against BMS & ISR Decision Loops

  • AI model poisoning & backdoor vulnerabilities in battle networks
  • Data manipulation attacks on AI-driven targeting and sensor fusion
  • Case Study: Injecting false data into a real-time AI-driven threat analysis engine

Module 4: AI-Driven Spectrum Warfare & EMSO Integration in BMS

  • Using AI for RF signature detection & spectrum-based targeting
  • Exploiting EMSO in contested environments using adversarial AI techniques
  • Case Study: Adversarial AI exploitation of real-time electromagnetic battle management

Part 2: Advanced Red Teaming & AI-Enabled Threat Simulation

Module 5: RF & AI-Driven Jamming & SIGINT Exploitation

  • Machine learning for RF signature recognition & deception attacks
  • Deploying AI-powered jamming & interference in BMS communications
  • Case Study: Attacking an AI-driven spectrum analysis system using adversarial ML techniques

Module 6: Cyber-AI Red Teaming Against AI-Enabled Battle Networks

  • Attacking AI-powered battlefield networks & command and control (C2) nodes
  • Machine learning-based penetration testing for AI security in BMS
  • Case Study: Simulating an AI-powered cyber attack against a JADC2 mission network

Module 7: AI-Driven SIGINT & ISR Exploitation

  • Exploiting AI-driven SIGINT & ELINT systems using adversarial techniques
  • Using generative adversarial networks (GANs) for ISR data spoofing
  • Case Study: Red teaming AI-powered SIGINT assets in battle management

Module 8: AI-Enabled Autonomous Systems in BMS

  • Machine learning & deep reinforcement learning in autonomous battle management
  • Vulnerabilities in AI-driven autonomous targeting & ISR systems
  • Case Study: Conducting an adversarial AI attack against an autonomous ISR drone system

Part 3: AI Security & Defensive Strategies in Battle Management Systems

Module 9: AI Security in Next-Gen BMS

  • Adversarial training & AI robustness testing in battle networks
  • Zero-trust AI architecture for multi-domain battle management
  • Red Team AI assessment frameworks for DoD AI security compliance
  • Case Study: Implementing AI adversarial defenses in real-time C4ISR networks

Module 10: Counter-AI Warfare & Secure AI Deployment

  • Cyber-physical AI attacks against autonomous decision-making systems
  • Deploying AI-powered countermeasures against EMS-based cyber threats
  • Case Study: AI-powered counter-BMS operations in modern conflicts

Final Red Teaming Exercise

  • Live AI-Driven Red Team Attack Simulation: Participants execute a multi-layered AI red teaming attack on an AI-powered battle management system, documenting vulnerabilities and proposing mitigation strategies.

Certification Exam Details

  • Exam Format:
    • 60-question multiple-choice test (40%)
    • Red Teaming Analysis and Report Submission (60%)
  • Passing Score: 80%
  • Exam Domains:
    • Domain 1: AI Fundamentals in Battle Management Systems (20%)
    • Domain 2: AI-Driven ISR, SIGINT & Space-Based Threats (25%)
    • Domain 3: Cyber & Spectrum Warfare in AI-Enabled BMS (30%)
    • Domain 4: Defensive Countermeasures & AI Security in C4ISR (25%)

Final Exam & Certification

  • Final Red Teaming Exercise: Participants conduct an AI-powered attack simulation against a military AI-enabled BMS, documenting their findings.
  • Certification Assessment:
    • 60-question multiple-choice exam on AI red teaming, spectrum warfare, cyber-electromagnetic attacks, and adversarial AI techniques.
    • Red Team Report Submission: Participants document their adversarial testing results and recommendations.
  • Successful candidates receive the  Certified Advanced AI-Enabled Battle Management System Operator (CAIBMS-O) certification.
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