Strategic AI Integration and Dependencies (Video Course)

Description

6+ Hours of Video Instruction

Unlock enterprise transformation with a proven AI integration framework to govern, deploy, and scale AI securely, sustainably, and strategically.

Overview

This course empowers professionals to adopt, implement, and scale artificial intelligence (AI) responsibly within enterprise environments. As AI reshapes industries, this course provides a structured framework to help you implement AI in a way that is secure, scalable, compliant, and aligned with long-term business strategy.

This course offers a deep dive into the strategic, architectural, and operational aspects of AI adoption, bridging the gap between executive vision and technical execution. You'll explore key concepts such as predictive versus generative AI, AI use case development, AI maturity assessments, and AI governance policies, with real-world examples to help you apply them directly in your organization.

You'll gain critical insights into enterprise AI infrastructure readiness, including the design of scalable compute and storage systems, and the implementation of AI model strategies that support both traditional machine learning and cutting-edge generative models such as LLMs, GANs, Transformers, and more. You'll also understand the dependencies AI has on data quality, trustworthiness, observability, and secure deployment.

The course also addresses key areas of AI risk management and security, including threat modeling for LLMs, Zero Trust in AI environments, data governance, and secure AI development practices. You'll learn how to defend against AI-specific attack vectors such as prompt engineering, vector store poisoning, and training data tampering.

In addition, you'll explore AI observability and AIBizOps--critical for monitoring model health, business KPIs, and operational performance across your AI systems. You'll also master AI incident management processes, including detection, mitigation, and response, as well as gain strategies for sustainable and ethical AI practices that meet compliance and ESG goals.

By the end of this course, you'll have the tools and knowledge to drive enterprise-wide AI transformation, design resilient and governed AI systems, and unlock the full value of AI while minimizing risk, waste, and technical debt.

About the Instructor

Avinash Naduvath is a renowned security architect in the Customer Experience (CX) Security Services division at Cisco Systems. As part of CX-Security, he has delivered multiple solutions to help secure customer networks. The range of services included incepting secure architectures, designs, technology advisories, best practice recommendations, and security assessments.

Prior to his current role in Cisco, Avinash was part of the technical services for security in Cisco-Bangalore and has helped troubleshoot and secure networks for multiple customers. He is a subject matter expert in next-generation firepower technology. Previous to this, Avinash was part of the professional services team in Cisco-Bangalore as a network consulting engineer.

Avinash has more than 10 years of experience in the information security domain, having worked on multiple aspects of security such as secure engineering and secure architecture. He has a passion for offensive security and has spoken on various topics at conferences such as Cisco SECCON and the Offensive Summit held at Cisco. Avinash has also contributed to and created multiple automation projects that have helped accelerate the security business. He is currently based in Singapore and enjoys presenting topics relevant to Zero Trust and its adoption.

He holds a master's degree in software systems from BITS Pilani, and is a Certified Information Systems Security Professional (CISSP), Cisco Certified Internetwork Expert--Security (CCIE), CompTIA Advanced Security (CASP+) practitioner, SABSA Charted Architect--Foundations and has acquired Cloud Security Alliance's Certified Competence in Zero Trust (CCZT), among the many security-based certifications he has accumulated during the course of his career. Avinash is a Certified Forrester's Zero Trust Adoption practitioner and is also the author of the award-winning fictional novel Mindbender (Literary Titan Silver Book Awardee and a Feathered Quill finalist).

Additional books and courses from Avinash Naduvath on O'Reilly.com: In Zero Trust We Trust (book)

Learn How To

• Design a comprehensive AI adoption strategy by aligning enterprise goals with AI capabilities, assessing organizational readiness, and integrating use cases across business functions.
• Differentiate between core AI paradigms--Artificial Intelligence, Machine Learning, and Generative AI--while understanding their historical evolution, business applications, and roles in predictive versus generative tasks.
• Identify, evaluate, and prioritize high-impact AI use cases using a structured methodology that factors in feasibility, ROI, business relevance, and technology fit.
• Build a scalable AI strategy by connecting enterprise-wide objectives with targeted AI use cases and integrating them into long-term digital transformation plans.
• Conduct AI risk and maturity assessments using frameworks such as NIST and Unified Maturity Models to evaluate preparedness, manage risk, and prioritize investments.
• Apply a structured AI adoption framework to transform business operations, align cross-functional teams, and ensure iterative success through feedback and refinement.
• Establish strong AI governance by implementing policies, metrics, and frameworks that ensure transparency, accountability, and regulatory compliance throughout the AI lifecycle.
• Develop a model strategy for AI applications by selecting appropriate machine learning and generative model architectures, and aligning them with business goals, scalability, and performance needs.
• Ensure infrastructure readiness for AI at scale by transforming compute, storage, and networking architectures and aligning them with AI workload requirements and operational goals.
• Embed AI into enterprise IT and business operations through sustainable practices, observability, secure deployments, incident management, and AI-driven service management frameworks.

Who Should Take This Course

This course is ideal for AI architects, practitioners, security professionals, governance managers, and executive leaders seeking to drive strategic AI adoption. It offers tailored insights into AI design, security, governance, and enterprise integration to build a sustainable, scalable AI portfolio across the organization.

Table of Contents

Introduction

Module 1: Introduction to the Effective Adoption of AI for Enterprises
Lesson 1: Introduction to Effective AI Adoption into Enterprise Functions

Module 2: Introspection
Lesson 2: Artificial Intelligence, Machine Learning, and Generative Intelligence Concepts
Lesson 3: AI Use Case Building
Lesson 4: Strategy Building
Lesson 5: Current State AI Assessments

Module 3: Preparation
Lesson 6: AI Adoption Framework
Lesson 7: AI Governance
Lesson 8: AI Roles, Literacy, and Expertise

Module 4: Build the Base
Lesson 9: Model Strategy
Lesson 10: Infrastructure Readiness
Lesson 11: Data Management

Module 5: Architecture
Lesson 12: Storage Architecture
Lesson 13: Infrastructure Architecture
Lesson 14: AI Application and Software

Module 6: Secure AI Deployment
Lesson 15: AI Security
Lesson 16: Offensive AI Security

Module 7: The Road to Autonomy
Lesson 17: AI Observability
Lesson 18: AI Incident Management
Lesson 19: AI Sustainability
Lesson 20: AI Service Management
Lesson 21: The Road to Autonomy
Lesson 22: Wrap-Up

Summary

About Pearson Video Training

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Sams, and Que. Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Video Lessons are available for download for offline viewing within the streaming format. Look for the green arrow in each lesson.

Sample Content

Table of Contents

Introduction

Module 1: Introduction to the Effective Adoption of AI for Enterprises

Lesson 1: Introduction to Effective AI Adoption into Enterprise Functions
Learning objectives
1.1 Purpose of This Course

Module 2: Introspection

Lesson 2: Artificial Intelligence, Machine Learning, and Generative Intelligence Concepts
Learning objectives
2.1 Artificial Intelligence Over the Years
2.2 A Bird's Eye View of AI
2.3 Comparing Predictive and Generative Intelligence
2.4 Key Takeaways

Lesson 3: AI Use Case Building
Learning objectives
3.1 How to Identify a Use case
3.2 Insertion of Artificial InteIligence Use Cases
3.3 Typical Adoption of Predictive Machine Learning
3.4 Typical Adoption of Generative Artificial Intelligence
3.5 Key Takeaways

Lesson 4: Strategy Building
Learning objectives
4.1 Enterprise Strategies
4.2 Tying in Strategy and Use Cases
4.3 Key Takeaways

Lesson 5: Current State AI Assessments
Learning objectives
5.1 Why Assess?
5.2 A Typical Assessment Strategy
5.3 NIST Risk Management Framework
5.4 A Unified AI Maturity Assessment Framework
5.5 Key Takeaways

Module 2 Conclusion: Introspection

Module 3: Preparation

Lesson 6: AI Adoption Framework
Learning objectives
6.1 A Typical Adoption Framework
6.2 Key Takeaways

Lesson 7: AI Governance
Learning objectives
7.1 No Governance, No Rules
7.2 Why Plan Governance First?
7.3 A Unified Data and AI Governance Framework
7.4 Metrics for AI Governance
7.5 Trustworthiness of a Model
7.6 Transparency and Traceability
7.7 Key Takeaways

Lesson 8: AI Roles, Literacy, and Expertise
Learning objectives
8.1 Stakeholder Management
8.2 Typical Skillsets for AI Projects and Adoption
8.3 Key Takeaways

Module 3 Conclusion: Preparation

Module 4: Build the Base

Lesson 9: Model Strategy
Learning objectives
9.1 Why Build Model Strategy First?
9.2 A Primer on Machine Learning Models
9.3 GenAI Use Cases for Image Processing: GAN, Diffusion
9.4 GenAI Use Cases for NLP: Recurrent Neural Networks
9.5 GenAI Use Cases for NLP: GRU and LSTM
9.6 GenAI Use Cases for NLP: Transformer Architecture for LLMs
9.7 Exploring Decisions Impacting Model Strategy
9.8 Key Takeaways

Lesson 10: Infrastructure Readiness
Learning objectives
10.1 Focus Metrics for AI Models
10.2 Typical Infrastructure Transformation
10.3 AI Infrastructure Requirements Based on Market Profile
10.4 Critical Requirements for Effective Infrastructure Transformation
10.5 Correlating Infrastructure Requirements and Use Cases
10.6 Benefits of AINetOps
10.7 Key Takeaways

Lesson 11: Data Management
Learning objectives
11.1 Data Is the Key
11.2 Data to Information Transformation
11.3 Data Management
11.4 Big Data and AI
11.5 Key Takeaways

Module 4 Conclusion: Build the Base

Module 5: Architecture

Lesson 12: Storage Architecture
Learning objectives
12.1 Lossless Communication
12.2 Typical Data Processing Architecture for AI Applications
12.3 Key Takeaways

Lesson 13: Infrastructure Architecture
Learning objectives
13.1 Infrastructure for Training, Fine-Tuning, and Inference
13.2 Typical Compute Architecture for AI Applications
13.3 Physical and Logical Segmentation
13.4 Key Takeaways

Lesson 14: AI Application and Software
Learning objectives
14.1 LLM Application Architecture and Dependencies
14.2 Model Serving Infrastructure Abstraction and Coding
14.3 Selecting an AI Framework
14.4 AI Benchmarking
14.5 AIDevOps
14.6 We are Getting There . . .
14.7 Key Takeaways

Module 5 Conclusion: Architecture

Module 6: Secure AI Deployment

Lesson 15: AI Security
Learning objectives
15.1 Threat Surfaces for AI Systems
15.2 Threats to Training Data
15.3 Threats to Vector Datastores
15.4 Protection of Data and Datastores
15.5 Handling Confidential Data
15.6 Threats with Chat Agents and LLMs
15.7 Zero Trust in AI Environments
15.8 Secure AI Application Development (NIST 800-218A)
15.9 Key Takeaways

Lesson 16: Offensive AI Security
Learning objectives
16.1 Best Practices Before Exploit Testing
16.2 Using AI for Selecting Assets (AI as a Tool)
16.3 Exploiting AI Applications (AI as a Target)
16.4 Key Takeaways

Module 6 Conclusion: Secure AI Deployment

Module 7: The Road to Autonomy

Lesson 17: AI Observability
Learning objectives
17.1 Why Measure the AI Initiative?
17.2 Critical Metrics to Measure AI Initiative Efficacy
17.3 Monitoring Established Metrics
17.4 Risk and Revenue
17.5 Tangible Deliverables to Drive Growth
17.6 AI-Driven Observability
17.7 AIBizOps
17.8 AIBizOps in Action
17.9 Key Takeaways

Lesson 18: AI Incident Management
Learning objectives
18.1 AI Incidents and Hazards
18.2 AI Incident Examples
18.3 AI Incident Response
18.4 Management of AI-Triggered Incidents
18.5 Key Takeaways

Lesson 19: AI Sustainability
Learning objectives
19.1 Purpose of Sustainability
19.2 Critical Metrics for Sustainability
19.3 Desired Outcomes in Sustainability
19.4 Quantum Machine Learning (QML)
19.5 Key Takeaways

Lesson 20: AI Service Management
Learning objectives
20.1 Information Technology Service Management (ITSM) Basics
20.2 AI Service Management Portfolio
20.3 AI Service Management Success Metrics
20.4 Module Deprovisioning
20.5 AI Service Management Framework
20.6 Key Takeaways

Lesson 21: The Road to Autonomy
Learning objectives
21.1 The Long Road to Autonomy
21.2 Key Takeaways

Lesson 22: Wrap-Up
Learning objectives
22.1 Conclusion

Module 7 Conclusion: The Road to Autonomy

Summary

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