AI Cybersecurity
This job-oriented course covers cybersecurity fundamentals, AI-powered defense, LLM and agent threats, OWASP risks, secure RAG and AI agents, AI-powered SOC operations, threat hunting, model security, governance, Zero Trust architecture, monitoring, and enterprise security dashboards.
Curriculum Areas
- Module 1: Cybersecurity Fundamentals
- Module 2: AI & Generative AI Fundamentals
- Module 3: AI for Cybersecurity
- Module 4: LLM Security
- Module 5: OWASP Top 10 for LLM Applications
- Module 6: Securing RAG Applications
- Module 7: AI Agent Security
- Module 8: AI-Powered SOC
- Module 9: AI for Threat Hunting
- Module 10: AI Security Operations
- Module 11: AI Model Security
- Module 12: AI Governance & Compliance
- Module 13: AI Security Architecture
- Module 14: AI Security Monitoring
AI Cybersecurity Curriculum
- CIA Triad
- Threats, vulnerabilities & risks
- Authentication & authorization
- Network security fundamentals
- Endpoint security
- Identity security
- Security Operations Center (SOC)
- SIEM, SOAR & XDR fundamentals
- AI, ML and Deep Learning
- Generative AI concepts
- LLM architecture
- Transformers
- Tokens & embeddings
- Foundation models
- AI APIs
- Open-source vs commercial AI models
- AI-powered threat detection
- Machine learning for anomaly detection
- Malware detection using AI
- Phishing detection
- Spam and fraud detection
- User/entity behavior analytics
- Threat intelligence with AI
- Automated security investigation
- LLM security architecture
- Prompt injection
- Indirect prompt injection
- Jailbreak attacks
- Data leakage
- Sensitive information disclosure
- Insecure output handling
- Excessive agency
- Model manipulation
- Supply-chain risks
- LLM application attack surface
- Prompt injection
- Insecure output handling
- Training/data poisoning
- Model denial of service
- Supply-chain vulnerabilities
- Excessive agency
- System prompt leakage
- Vector/embedding weaknesses
- Unbounded consumption
- RAG security architecture
- Document security
- Data poisoning
- Malicious documents
- Vector database security
- Access control for retrieval
- Data leakage prevention
- Secure embeddings
- RAG monitoring
- AI Agent architecture
- Tool/function calling risks
- Agent permissions
- Excessive agency
- Agent identity
- Agent-to-agent security
- Memory security
- Secure MCP/tool integrations
- Human approval workflows
- AI Agent monitoring
- AI in SOC operations
- Alert summarization
- Alert correlation
- Threat hunting with AI
- Incident investigation
- Automated incident response
- Security Copilots
- SOC automation
- AI-assisted threat intelligence
- Threat hunting fundamentals
- AI-assisted log analysis
- IOC analysis
- TTP identification
- MITRE ATT&CK mapping
- Behavioral analysis
- Detection engineering
- Threat intelligence enrichment
Microsoft Security
- Microsoft Security Copilot
- Microsoft Defender
- Microsoft Sentinel
- Microsoft Defender XDR
- Microsoft Entra security
- Microsoft Purview
Cloud Security
- Azure security
- AWS security
- Google Cloud security
- Cloud SIEM/SOAR
- AI workload security
- Model security lifecycle
- Training data security
- Data poisoning
- Model theft
- Model extraction
- Adversarial machine learning
- Adversarial examples
- Model supply-chain security
- Model access control
- Responsible AI
- AI risk management
- AI governance
- Data privacy
- Security policies
- AI audit requirements
- Model risk management
- Human oversight
- Enterprise AI security policies
- Secure AI reference architecture
- Zero Trust for AI
- Identity for AI Agents
- API security
- Secrets management
- Network segmentation
- Encryption
- Data Loss Prevention
- AI gateway architecture
- AI application logging
- Security telemetry
- Model monitoring
- Agent monitoring
- Prompt/response monitoring
- Anomaly detection
- Security alerts
- Incident response
- AI security dashboards