AI SECURITY ARCHITECT – K2 Cyber Technologies

Whisk_403a0d448cb8c27a0204186d56ae1e2bdr

Teacher

Categories

Amazon Web Services, Cyber Security, DevOps, Web Development

Overview

Overview

Course Features

Lecture 0
Quiz 0
Duration 10 weeks
Skill level All levels
Language English
Students 0
Assessments Yes

Curriculum

Curriculum

31 Sections
0 Lessons
10 Weeks

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PHASE 1: SECURITY & AI FOUNDATIONS
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MODULE 1: CYBERSECURITY FUNDAMENTALS
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MODULE 2: CYBERSECURITY FRAMEWORKS & COMPLIANCE
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MODULE 3: PYTHON PROGRAMMING FOR SECURITY
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MODULE 4: AI & MACHINE LEARNING FUNDAMENTALS
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PHASE 2: CORE AI SECURITY CONCEPTS
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MODULE 5: AI/ML THREAT LANDSCAPE
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MODULE 6: SECURING ML PIPELINES
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MODULE 7: LLM SECURITY & PROMPT ENGINEERING
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MODULE 8: PRIVACY-PRESERVING AI
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PHASE 3: AI FRAMEWORKS, TOOLS & SECURITY
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MODULE 9: MACHINE LEARNING FRAMEWORKS SECURITY
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MODULE 10: MLOPS & AI PLATFORMS SECURITY
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MODULE 11: AI SECURITY TESTING TOOLS
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PHASE 4: AWS CLOUD AI SECURITY
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MODULE 12: AWS SECURITY FUNDAMENTALS
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MODULE 13: AWS AI/ML SERVICES SECURITY
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MODULE 14: AWS DATA SECURITY FOR AI
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MODULE 15: AWS NETWORKING FOR SECURE AI
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PHASE 5: DEVSECOPS FOR AI/ML SYSTEMS
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MODULE 16: CI/CD PIPELINE SECURITY
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MODULE 17: INFRASTRUCTURE AS CODE SECURITY
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MODULE 18: CONTAINER & KUBERNETES SECURITY FOR ML
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MODULE 19: SECURE SOFTWARE DEVELOPMENT FOR AI
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PHASE 6: ADVANCED SECURITY ARCHITECTURE
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MODULE 20: THREAT MODELING FOR AI SYSTEMS
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MODULE 21: AI SECURITY MONITORING & DETECTION
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MODULE 22: AI INCIDENT RESPONSE
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MODULE 23: ENTERPRISE AI SECURITY ARCHITECTURE
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CAPSTONE PROJECTS
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CERTIFICATION REQUIREMENTS
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Instructor

Instructor

Requirements

• 3+ years experience in cybersecurity or software engineering • Basic understanding of programming (Python preferred) • Familiarity with cloud computing concepts • Understanding of networking and system administration • Recommended: AWS Cloud Practitioner certification or equivalent

Features

Upon successful completion of this program, participants will be able to: 1. Design and implement secure AI/ML architectures 2. Apply cybersecurity frameworks (NIST CSF, AI RMF, MITRE ATLAS) to AI systems 3. Secure machine learning pipelines and model deployments 4. Implement DevSecOps practices for AI workloads 5. Deploy and secure AI services on AWS cloud 6. Conduct threat modeling and risk assessment for AI systems 7. Respond to and mitigate AI-specific security incidents 8. Design enterprise-scale AI governance frameworks

Target audiences

• Security Engineers transitioning to AI/ML security roles • Software Architects designing AI-powered systems • DevOps Engineers implementing secure ML pipelines • Data Scientists requiring security awareness • Cloud Architects working with AI services • Cybersecurity Professionals expanding into AI domain