Master the foundational concepts, technologies, and operational life cycle of
artificial intelligence (AI) to understand how modern AI systems are built, deployed, and scaled responsibly.

What You will Learn

• Understand core principles, evolution, and components of AI
• Apply real-world AI applications across industries
• Apply AI project life cycle, MLOps, and DataOps
• Apply AI technology stack, infrastructure, and deployment models

Master ethical AI principles and frameworks to ensure responsible AI development and deployment across your organization.

What You will Learn

• Understand key ethical, societal, privacy, and security concerns in AI
• Understand fundamental AI ethics principles and global standards
• Apply Responsible AI usage practices for safe and accountable AI
• Apply Responsible AI development life cycle and governance integration

Develop structured AI strategies and roadmaps that align organizational goals with
responsible, scalable, and value-driven AI adoption.

What You will Learn

• Set an AI vision and assess organizational readiness
• Prioritize use-case and develop an AI roadmap
• Modernize data, technology, and infrastructure
• Manage AI pilots, scaling strategies, culture, and performance

Design and implement enterprise-wide AI governance structures that ensure
accountability, transparency, compliance, and trust.

What You will Learn

• Understand AI governance concepts, operating models, and roles
• Define AI governance policies, decision rights, and controls
• Apply global AI governance frameworks and life cycle governance
• Manage AI asset management, documentation, human oversight, and
  tooling

Execute and analyze adversarial machine learning, privacy, and model extraction
attacks to assess AI system robustness, trustworthiness, and risk, and apply defensive strategies to mitigate them.

What You will Learn

• Identify core adversarial machine learning attack classes
• Execute practical adversarial input attacks across data modalities
• Apply privacy, inference, and model extraction attack techniques
• Evaluate robustness, trustworthiness, and risk evaluation methods
• Implement defensive strategies for model privacy and resilience

Identify, assess, and manage AI-specific risks, threats, and vulnerabilities across the AI life cycle.

What You will Learn

• Understand AI threat landscape, vulnerabilities, and adversarial attacks
• Apply AI risk identification, assessment, and prioritization methods
• Apply AI risk management frameworks and standards
• Conduct threat modeling and attack surface analysis for AI systems

Manage vendor, supplier, and ecosystem risks across AI procurement, deployment, and life cycle operations.

What You will Learn

• Understand third-party AI risk categories and supply chain threats
• Conduct AI vendor due diligence, evaluation, and contract governance
• Apply regulatory obligations and vendor compliance requirements
• Implement continuous vendor monitoring, assurance, and incident response

Design secure-by-design AI architectures that protect models, data, pipelines, and runtime environments.

What You will Learn

• Understand AI security architecture principles and frameworks
• Apply secure AI design patterns and defense-in-depth strategies
• Implement secure coding, model protection, and deployment controls
• Apply runtime security, API protection, and continuous monitoring

Embed privacy, transparency, trust, and safety into AI systems to enable ethical and user centric AI experiences.

What You will Learn

• Understand privacy-enhancing technologies and data protection techniques
• Apply AI privacy risk assessment and mitigation strategies
• Apply transparency, explainability, and trust building mechanisms
• Implement ethical design, fairness assurance, and trust monitoring

Build AI-specific incident response, resilience, and recovery capabilities to sustain trust and business operations.

What You will Learn

• Understand AI-focused incident response frameworks and workflows
• Conduct AI incident detection, containment, recovery, and reporting
• Develop AI business continuity and disaster recovery planning
• Apply testing, simulations, and continuous readiness improvement

Establish robust assurance, testing, and audit mechanisms to validate trustworthy,
compliant, and reliable AI systems.

What You will Learn

• Understand AI assurance principles, frameworks, and governance models
• Apply AI testing strategies across data, models, and systems
• Conduct validation, verification, bias, fairness, and robustness testing
• Apply AI auditing methodologies, evidence management, and reporting