Vasant holds a BA Chemical Engineering from the Indian Institute of Technology and an MS and PhD in Artificial Intelligence from the University of Pittsburgh. He previously worked at Morgan Stanley, where he created the Data Mining Group focused on predicting financial markets and customer behavior.

He founded SCT Capital Management, one of the first machine?learning?based, automated hedge funds bringing predictive modeling and AI directly into real?world trading. Today Vasant is a professor at New York University’s Stern School of Business and also a professor at NYU’s Center for Data Science.

Thinking With Machines is one of the useful books on AI that I have read recently. Vasant is sharing with the reader not just what AI is, but how we can make decisions in today’s world that is now shaped by intelligent machines.

He succeeds delivering a practical guide for anyone trying to figure out when to trust AI—and when not to and insist on human judgment.

A clear understanding from early AI advancements

Vasant walks readers through AI’s early evolution that is more storytelling rather than a college textbook. This is a very important and often overlooked point. He traces AI back to its 1956 birth at Dartmouth College. This is perhaps a strong indicator that many today think AI is new. I recently attended an AI Summit event. The keynote speaker indicated AI has been used in business for more than 75 years. It’s 2026 today, not 2031.

Vasant is offering reader a very usable framework for deciding when AI can be trusted and when human oversight is mandatory. His examples include finance, healthcare, education, and most importantly the cost of errors. Perhaps his strongest lesson is when in today’s age of immediacy, users simply consume AI outputs due to social media influencers and the “AI hype cycle” found within LinkedIn, YouTube channels and other prosumer websites where influencers need sensationalized headlines to turn clicks into profits.

In conclusion, I highly recommend Thinking With Machines without hesitation. It’s that rare book that makes you feel both smarter and more responsible by the time you turn the last page—and it gives you a vocabulary and framework you’ll keep returning to whenever you encounter a new AI system in your life.

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Adam holds a BA in Classical Civilizations from UCLA and JD from the University of Washington School of Law. He started his career as a corporate attorney before becoming Starbucks’ first Chief Digital Officer and EVP of Global Retail Operations before departing to become President, Chief Experience Officer, and co?CEO at J.Crew. Andy holds a BA from Brown University and MBA from MIT’s Sloan School of Management. Andy co-founded Forum3 with Adam.

They position the book as a strategic manual for organizations embracing artificial intelligence and business. In fact, the book functions as simply promotional of the world’s enthusiasm surrounding AI. The book wants to convince readers that adopting an “AI-first” mindset is non-negotiable for any modern organization.

Rather than educating readers with concrete examples, Adam and Andy leans heavily on name-dropping and aspirational catchphrases.

Name dropping goes nowhere

The tone is set from the opening chapter, which eagerly cites conversations with Sam Altman, Bill Gates, and Sal Khan. However attributing the graphical user interface (GUI) to Bill Gates was a mistake, so they never met Steve Jobs who made the Macintosh GUI stand out from Windows.

I was just waiting for the next chapter to begin with their conversation with Elon Musk. They missed with really meaningful insights and focused on celebrity to woo the reader. It missed badly. Adam is recognized for his Starbucks digital work. Andy certainly knows how to sell an idea. They are selling the reader on AI’s potential and inevitability.

They praised insights from Sam, Bill, and Sal, all who used ChatGPT 4.0 just as the world was learning of ChatGPT 3.5. So, this leads the reader to believe they have an exclusive inside scoop to the next killer AI release from OpenAI. They never address how ChatGPT writes suicide notes for teenagers. This would dampen their wild enthusiasm for AI.

In conclusion, AI First is all hype, and overconfident in scope. This will certainly appeal to executives seeking to be social media influencers showing how they are keeping pace with this evolving technological change. The book’s title is AI First, but make this ‘AI Last’ on your reading list.

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As 2023 was coming to a close, I found myself diving into HBR’s 10 Must Reads for 2024. It was a great way to get a feel for the management ideas shaping the year ahead. Then came the 2025 edition, and now, here I am again—starting 2026 with HBR’s latest collection.

This is for both new and experienced leaders seeking insights, and advice to propel their organizations forward in the new year.

To no surprise, AI remains a key read for organizations. The focus “Bring everyone on board with your AI efforts” is addressing the much needed broad participation in AI adoption. This is even more important in 2026 as models have been updated in 2025 to bring new reasoning models that can propel organizations forward, only if the deployment touches all employees.

However a risk based approach will save organizations from overspending and unauthorized egress of organizational data.

What is emerging and important in 2026?

Topics include eliminating dysfunction in your leadership team, which reveals if your organization’s leadership is a shark tank or petting zoo. In fact, the topic addressing how to apply the right kinds of questions to strategic decisions is even more insightful for organizations regardless of marketplace.

This also piggybacks on the topic of building a corporate culture that truly aligns with your values. There is more than meets the eye. The concluding read, The Strategic Genius of Taylor Swift, will be surprising for some, and not to be missed.

Consider HBR’s previous annual publications for the new year:

In conclusion, HBR’s 10 Must Reads 2026 simply continues to deliver insights that employees and managers must learn, understand, and perhaps deploy in order to find success in the new year, with a flurry of changes ahead it remains best to be prepared.

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Ed holds a BS in Electrical Engineering from the University of Michigan and a MS in Information Networking from Carnegie Mellon. Today he is the President of the SANS Technology Institute. Paul holds a M.Div.from Gordon-Conwell Theological Seminary and PhD in Political Science from Claremont Graduate University. Today he is the President of Montreat College.

I found this to be one of the most insightful books on cybersecurity and a must read for anyone in the fields of IT, AI, or IR. Jon outlines key elements for cybersecurity teams to X: Espionage (Bletchley Park), Sabotage (Stuxnet), Subversion (2016 US Election Interference), and Cyber Power (China).

It is very welcoming to see Mariano addressing machine learning for predictive cybersecurity. Mariano introduces statistical methods and machine learning easily accessible to security teams with Bayesian inference. Examples predictive detection, anomaly identification, and early warning signs is proving how AI is moving any cybersecurity program from reactive to proactive.

Mariano is including Python scripts and Jupyter-based workflows, providing technically savvy security teams a direct path to experimentation and deployment in their internal test deployments.

This book certainly provides cybersecurity teams with a practical roadmap for driving a data-driven mindset. This can be an excellent resources for organizational leaders to understand business in the age of aggressive malware and data breach announcements.

Start aligning data with organizational objectives

In fact, security teams today must be focusing on data that directly impacts organizational goals. Mariano is addressing how teams can find the right metrics and how to avoid the noise of vanity statistics that do not reduce or transfer risk.

Another title, Grokking Algorithms is book that I would highly recommend as a companion:

A major strength is the emphasis on actionable implementations. Mariano is guiding readers through building a data-driven metrics program, designing dashboards, and visualizations that organizational leaders can quickly understand.

The ATLAS (Alert Threshold Lifecycle Assessment System) methodology is a solid example, which offers a proven way to reduce alert fatigue by adjusting thresholds based on real data. This is certainly for CISOs, cybersecurity managers, and security analysts. Today they must justify budgets and prioritize tools with limited resources in the new era of AI threats.

Non-technical stakeholders (organizational leadership) also benefit. Mariano provides chapters focusing on communicating risk, explaining data analysis in business terms and aligning security with any organization’s strategic objectives.

In conclusion, Data-Driven Cybersecurity is spot on linking high-level cybersecurity strategy with the day-to-day security data metrics. Frameworks including ATLAS make a strong choice for several organizations seeking to further develop their security programs with data.

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Mariano holds a Masters in Cyber Defense and Information Security from Temple University. She is the former Director Governance, Risk, and Compliance at Layer 8 Security, and CISO at Mattei. Today Mariano is VP Cybersecurity and CISO at Azzur.

It is very welcoming to see Mariano addressing machine learning for predictive cybersecurity. Mariano introduces statistical methods and machine learning easily accessible to security teams with Bayesian inference. Examples predictive detection, anomaly identification, and early warning signs is proving how AI is moving any cybersecurity program from reactive to proactive.

Mariano is including Python scripts and Jupyter-based workflows, providing technically savvy security teams a direct path to experimentation and deployment in their internal test deployments.

This book certainly provides cybersecurity teams with a practical roadmap for driving a data-driven mindset. This can be an excellent resources for organizational leaders to understand business in the age of aggressive malware and data breach announcements.

Start aligning data with organizational objectives

In fact, security teams today must be focusing on data that directly impacts organizational goals. Mariano is addressing how teams can find the right metrics and how to avoid the noise of vanity statistics that do not reduce or transfer risk.

Another title, Grokking Algorithms is book that I would highly recommend as a companion:

A major strength is the emphasis on actionable implementations. Mariano is guiding readers through building a data-driven metrics program, designing dashboards, and visualizations that organizational leaders can quickly understand.

The ATLAS (Alert Threshold Lifecycle Assessment System) methodology is a solid example, which offers a proven way to reduce alert fatigue by adjusting thresholds based on real data. This is certainly for CISOs, cybersecurity managers, and security analysts. Today they must justify budgets and prioritize tools with limited resources in the new era of AI threats.

Non-technical stakeholders (organizational leadership) also benefit. Mariano provides chapters focusing on communicating risk, explaining data analysis in business terms and aligning security with any organization’s strategic objectives.

In conclusion, Data-Driven Cybersecurity is spot on linking high-level cybersecurity strategy with the day-to-day security data metrics. Frameworks including ATLAS make a strong choice for several organizations seeking to further develop their security programs with data.

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Jon holds a PhD in political science from the Massachusetts Institute of Technology and an M.S. in computer science and B.S. in symbolic systems from Stanford University. He served in the US Navy with operational assignments in Europe, Latin America, and the Middle East. Today Jon is an Associate Professor at the School of Cybersecurity and Privacy at Georgia Tech. His research explores the role of emerging technology in global security.

I found this to be certainly one of the most insightful books on cybersecurity and a must read for anyone working in the fields of IT, AI, or IR. He reveals how cybersecurity has elevated to impact national security and international relations, resulting in secret statecraft. In fact, Jon introduces the statecraft of cybersecurity by revising Espionage (Bletchley Park), Sabotage (Stuxnet), Subversion (2016 US Election Interference), and Cyber Power (China).

He begins by updating the history of espionage at Bletchley Park. This will certainly be an amazing insight to many readers who only know the Turing story or the 2014 movie The Imitation Game, of the early efforts at Bletchley Park revealing the challenges the British faced in confronting Enigma.

Cybersecurity is now front and center across the world

It is definitely interesting to see today how cyber threats by criminals and nation states are shifting the global balance of power. Jon is revealing a path for you to travel. This allows countries with limited resources to challenge the global superpowers. And today commercial industries are now learning their factories are now on front line of national defenses.

Another title, Sandworm and Digital Empires are books that I would highly recommend as a companion:

We are in fact living in a new era of deception, for better and worse. Intelligence threats are now emerging in organizations, policies, and national jurisdictions.

In conclusion, Age of Deception is a must read book for understanding the reality of the connected world when criminals and nation states can leverage cyber threats to shift the balance of power beyond the global stage into your backyard. Do you understand the impact?

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Shreyas holds a PhD in Aerospace Engineering from Purdue University and MS in Mechanical Engineering from Wright State University. He is the former Director of Research at Robust Analytics. Today he is Principal Data Scientist at Amazon Web Services.

Here is a good, very practical guide for those who seek to build and deploy cost-effective LLM-based solutions. From selecting a model, pre-and post-processing, prompt engineering, and fine tuning. Shreyas is certainly providing insights for optimizing inference and affordable architectures for typical applications. So today, generative AI value is found at the intersection of performance and cost. Howver organizations must optimize their infrastructure in order to reduce cloud costs.

Shreyas is certainly emphasizing the “biggest” model is not always the best. Model Selection and Foundation should be a wise, smaller approach provides developers to focus on domain-specific models. This requires less computational resources.

Addressing the real LLM challenge

This book offers a deep focus on Fine-Tuning efficiencies with Parameter-Efficient Fine-Tuning. In fact, LoRA (Low-Rank Adaptation) and P-tuning demonstrate these methods allow for model specializations sans those huge costs of full-parameter training. Furthermore, to reduce operational expenses, the techniques including quantization(reducing the precision of model weights) and pruning (removing redundant parameters) are outlined. These solutions enable models to run faster and on cheaper hardware.

Shreyas is providing really important, practical insights to Retrieval-Augmented Generation (RAG). He also highlights how important of data pre-processing and post-processing to ensure model outputs remain valuable and cost-controlled. Finally, beyond the models themselves, Shreyas explores the infrastructure layer. He is addressing memory management, caching strategies, and LLMOps, providing to no surprise a framework for monitoring and scaling applications in Amazon’s cloud environment.

In conclusion, This guide is essential for data scientists and technical leaders who need to justify AI ROI. The book is guiding readers through the entire lifecycle of an LLM project with a focus on efficiency and cost.

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Chuck holds an MA from the University of Chicago. He serves as President of Brooks Consulting International addressing cybersecurity, emerging technologies and government relations. Chuck also serves as an Adjunct Professor at Georgetown University in the Cyber Risk Management Program. He has briefed the G20 on energy cybersecurity, served on two National Academy of Science Advisory groups and addressed USTRANSCOM on cybersecurity.

Chuck serves on an industry/government Working group for DHS CISA focused on security space systems. Chuck is also a contributor to Forbes, The Washington Post, Dark Reading, Homeland Security Today, and Security Information Watch.

He is providing readers a roadmap for understanding the “Fourth Industrial Revolution.” The book serves as a bridge between complex technical advancements and their practical implications for business, government, and individual privacy.

Readers will learn that society is entering a new state of ”cyberflux” where digital and physical (analog) worlds are rapidly converging. This has created new vulnerabilities yet also new opportunities. He reveals AI, 5G, and related tools offer enhanced efficiencies, empowering new business models that offer new innovations across finance, healthcare, transportation, and government. He is focusing on what they do and why they matter rather than jumping into technological formulas or programming code.

Three areas of shifting technology

And yet in fact, these exact rapid transformations are enlarging the attack surface which is now driving data breaches, supply?chain attacks and the compromise of copyrighted and intellectual property data. As a result, this requires organizations to understand cloud and edge computing, encryption, and zero?trust, so that organizations stay resilient. The three areas outlined include:

1. Artificial Intelligence: This is presented as a double-edged sword. Chuck clarifies that AI enables enhanced cybersecurity services by automating threat detection and incident response. However it also empowers criminals and hackers to create new types of malware, automate phishing, and sophisticated deepfakes.
2. 5G and IoT: 5G’s communication advancements is the conduit for the Internet of Things (IoT). We now live in a hyper-connected world. These two technologies drive smart cities and new efficiencies in global supply chains. However this “attack surface,” is now baked into home appliances and even critical infrastructure.
3. Quantum Computing: A looming “quantum apocalypse” is such a shift in computational power that can easily break current encryption standards.

So now organizations of all sizes and markets must adopt new governance to protect themselves of these disruptive technologies. In fact, the biggest lesson for organizational leaders is that cybersecurity is no longer a “cost center” for IT departments but is now a critical business enabler.

In conclusion Inside Cyber is an excellent primer addressing how converging technologies are transforming privacy and security, and what organizational leaders must understand.

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Sinan holds both a BA and MA in Pure Mathematics from Johns Hopkins University. His graduate work focused on algebraic geometry with applications to cybersecurity. He was Founder and CTO of Kylie.ai and Legion Analytics. Today Sinan is the Founder and CTO of LoopGenius. He is also a former Lecturer/Adjunct Professor, teaching graduate-level Business Analytics, Mathematics, and Computer Science.

Sinan is addressing a very critical, yet often overlooked stage of machine learning pipelines. A transformation of raw data into informative features will make or break your efforts. He is advocating the quality of the input data is the true measurement for any model’s performance.

He is presenting to readers six hands-on projects to upgrade your training data using feature engineering. This “Bookcamp” thereby prioritizes project-based curriculum over theory since structures the text is the true craft for data scientists. Instead of focusing on mathematical transformations, Sinan asks the reader to solve business problems, such as predicting flight delays.

LLMs need high quality data

By learning code-driven case studies, readers will have insights to social media classification, COVID detection, recidivism prediction, and even stock price movement detection. These lessons can result in key improvements to any organization’s machine learning pipelines. Theses ideas prove how a poorly chosen transformation introduces data leakage resulting in models gaining data outside of real-world scenarios. This also saves time and money fine-tuning parameters. This is in fact, clearly written for experienced machine learning engineers deploying with Python. A basic understanding of Pandas and Scikit-Learn is also required,

In conclusion, Feature Engineering Bookcamp is really key for machine learning engineers looking to build robust, production-ready LLMs. In the era of GenAI, Sinan reveals the ability to engineer high-quality data remains the key valuable skill.

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Ben holds a MSc in Criminology and Criminal Justice from the University of Edinburgh and a PhD in Criminology from the Scottish Centre for Crime and Justice Research. He is a Cambridge Cybercrime Centre postdoctoral researcher and long-term collaborator with this interdisciplinary center, focusing on online hacker communities and cybercrime markets. Today he is a Senior Lecturer in Digital Methods at the University of Edinburgh within the Department of Science, Technology, and Innovation Studies (STIS).

The Dark Web is a subset of the internet that was designed to be hidden from search engines and requires specific software, like Tor. Well known for illegal marketplaces (Silk Road) for drugs, firearms, murder for hire, stolen data, selling breached data, and hacking services, yet in fact is also serving as a privacy resource. Here forums allow users to exchange data regarding whistleblower platforms and also permits journalists to communicate in countries with strict censorship.

Insert irony: the very markets trafficking in illicit goods today rely on a architecture engineered by the U.S. Naval Research Laboratory.

Tor 101: a history lesson

Ben is writing a biography of Tor, which holds both a cultural and technology history of power, privacy, international relations. His interviews with the original core designers, developers, and activists, permits a unique history between the US Government and hackers known as Cypherpunks. Ben is in fact revealing how each group is using Tor:

Activists: Civil libertarians and NGOs (Non-Governmental Organizations including the Electronic Frontier Foundation) who reframed Tor as a human rights tool.

Engineers: The original architects focused on the mathematical and technical robustness of onion routing.

Volunteers: Those relay operators who provide the physical infrastructure at significant legal and personal risk.

Another title, Tracers in the Dark is recommend as a companion to understand in detail the types of global criminal activity on the Dark Web:

In conclusion, Tor is essential reading for anyone interested in understanding the recent past and the future of the internet. Ben’s focus on a criminology perspective revealing how Tor’s infrastructure can be a resource for state power and a resource of individual resistance. To many access to Tor is a simple black and white issue. However we live in a grey world.

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Alex holds a BS in Chemical Engineering and Biochemistry from the University of California, Los Angeles (UCLA)., where he graduated in 2014. He co-founded Codesmith and led its Data Science and Machine Learning research group. He has also been involved in advancing TensorFlow.js, focusing on enabling browser-level deep learning and computer vision applications. Today Alex is a Machine Learning Engineer at Amazon AI.

Reinforcement Learning (RL) is certainly one of the most intellectually demanding subfields of Machine Learning. Alex takes a very pragmatic approach by writing this book for the developer who wants to understand the “why” engaging with the “how.” He is utilizing PyTorch as the vehicle, providing a clean, Python interface to very complex algorithms.

Humans learn best from feedback. We are encouraged to take actions that lead to positive results. This reinforcement is applied to computer programs allowing them to solve more complex problems. Deep Reinforcement Learning in Action teaches readers how to program AI agents that adapt and improve based on direct feedback from their environment.

Real demands for RL

This is certainly not an introductory Data Science 101 book. Readers will benefit from having working knowledge of Python at an intermediate level. Regarding Math, Basic Calculus and Linear Algebra is needed in order to understand gradients and tensors. Finally, a base knowledge of Neural Networks for backpropagation is required and I find this fascinating.

Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. For instance, Alexander is explaining the Bellman Equation as a way of “back-propagating” future rewards to current actions. This helps the reader gain a sense of how a neural network “learns” a strategy over time.

In conclusion, Deep Reinforcement Learning in Action makes the “black box” feel transparent. The fundamental principles covered remain the bedrock of modern AI systems.

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John holds a MS from the MIT and PhD in Business Administration (DBA) from Harvard. He has been a long tenured professor at HBS. He co-founded Kotter International, his leadership and strategy implementation firm. Leading Change published in 1996 was named one of the most influential business books of all time by Time Magazine.

While John introduced Leading Change in 1996, this book is moving the reader to accept our the world has moved from episodic change to a state of permanent, volatile flux. This falls into perfect alignment with modern IT’s Continuous Delivery.

The focus is on behavioral science, the barrier to organizational change is not bad management, but human nature. As a result, John presents two distinct “channels” for organizations. The Survive Channel: Triggered by threats is reactionary and negatively increasing anxiety. A Thrive Channel: Triggered by opportunities will drive curiosity, collaboration, and creativity. Yet John is outlining modern corporate KPIs and quarterly targets are shutting down innovations organizations need today in order to to stay relevant.

An 8 step accelerator for organizations

John’s original 8-Step Process is now being the 8 Accelerators:

1. Create a Sense of Urgency Around a Big Opportunity: Focus on the “why” instead of a crisis. Identify a window of strategic opportunity that inspires people to move..
2. Build a Guiding Coalition: Gather a diverse group of employees from all levels and departments as your “volunteer army” who hold credibility, influence, and passion to lead the change.
3. Form a Strategic Vision: Clearly define your organization’s future that is distinct from its past. It must be a vision that is easy to communicate and must resonant.
4. Enlist a Volunteer Army: Change never works when forced down from the top. Selecting at least 10% of your workforce who are internally motivated will be the driving vision forward.
5. Enable Action by Removing Barriers: Identify friction across your organization (outdated processes, silos, and legacy mindsets) and empower your volunteers to destroy dismantle them all.
6. Generate Short-Term Wins: Document and publicize small, visible victories early on to validate the effort and keep the Survive channel from regaining control. Remember crawl, walk, run.
7. Sustain Acceleration: Use the momentum from early wins to tackle bigger, more complex organizational systems.
8. Institute Change: Ensure the new behaviors are woven into the “way we do things here” motto of the organization. This does indeed updating hiring, training, and promotion criteria to reflect the new culture.

Perhaps the most important feedback for any organization is that in order to move forward organization leaders need to support the unregulated high trust of their volunteer army.

In conclusion, today’s world demands all organizations deliver more. To be successfully leaders must embrace John’s Change lessons. I highly recommend this book because it teaches organizations how to form a strategic vision that actually enables action, allowing your organization to drive acceleration and achieve those hard-to-imagine results.

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David holds a PhD in Philosophy from the University of Massachusetts Amherst. He is a leading figure in the Python and data science communities. He is the former Director of the Python Software Foundation.

This book revolves around 24 puzzles that range from basic string manipulation to very complex regular expression (regex) tasks. David is providing his expert solutions in Python. And with the explosion of AI Chatbots, he then prompts GitHub Copilot and ChatGPT to solve the same problems.

These lessons provide insights to enhance your productivity by writing regular expressions. David also offers AI best practices, showing how smart prompts return better results. By the end, you’ll be a master at solving your own Regex puzzles, whether you use AI or not.

Rather than being a traditional, dry academic textbook, he shows the puzzles in a “man vs. machine” competition. This is perhaps a modern AI version of the song Big John in American folklore..

Its all about the power of Regex

Perhaps in fact, the interesting perspective is watching the AI fail. Today most LLMs are remarkably good at generating code, however David is honest in addressing the tendencies to hallucinate regex features that don’t exist.

So he helps readers understand how to be a better “AI pilot by Prompt Engineering refinements to get better regex output, the real necessity of human testing of AI-generated code, to quote Alan Kay, AI should be a “bicycle for the mind” rather than a replacement.

In conclusion, Regular Expression Puzzles and AI Coding Assistants is less of a “how-to” for beginners and placing more emphasis on critical thinking. If you want to increase your regex skills or understand how integrating AI into your daily workflows, David is providing readers a balanced, well thought out perspective that avoids all of the typical hype surrounding generative tools.

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Kelly holds a BA in Economics from Vassar College. She previously serving as Vice President of Security Strategy at Capsule8 (acquired by Google Cloud) and management roles at SecurityScorecard. She co-founded IperLane, a security startup acquired by CrowdStrike. Today Kelly is a Senior Principal in the Office of the CTO at Fastly. She is a frequent speaker at major conferences like Black Hat, RSA, and O’Reilly Velocity.

Aaron holds a BA in Economics from the University of Missouri. He was the Co-founder and CTO of Verica (a chaos engineering startup) and a Distinguished Engineer at Capital One. He is widely recognized as a pioneer in applying chaos engineering specifically to the security domain. A former Chief Security Architect at UnitedHealth Group (UHG), Aaron led the release of ChaoSlingr, an early open-source security chaos engineering tool. He is a frequent keynote speaker at the National Press Club, RSA, and DEF CON.

Both share that traditional security relies on the “fortress” mentality, building thicker walls to keep attackers out. However they state that modern, complex, distributed systems failure is not possible, it’s actually inevitable. So, don’t let the inevitable system failures stall organizational progress. By adopting Security Chaos Engineering, you certainly build the ‘immune system’ your software needs to withstand adverse events, ensuring your business goals and engineering velocity remain on track even under pressure.”

Its all about the power of Regex

So enter Security Chaos Engineering (SCE), a discipline of performing pro-active, controlled experiments to discover systemic weaknesses before your adversaries can exploit them. Why wait for a cyber event when you can “inject” conditions to verify that your security controls actually work. Kelly and Aaron outline Five Capabilities of Resilience:

1. Absorption: The ability to take a hit without failing (e.g., rate limiting).
2. Adaptability: Capacity to change behavior based on new information.
3. Restorability: How quickly a system returns to a known, secure state.
4. Observability: Ability to understand the internal state of a system from its external outputs.
5. Learnability: An organization’s ability to turn failures into institutional knowledge.

As we continue moving to modern platform engineering and DevSecOps, the legacy “siloed” approach to security is certainly falling behind. In fact, Kelly and Aaron reveal a vocabulary and the methodology to integrate security into your organization’s engineering lifecycle.

In conclusion, Security Chaos Engineering is particularly critical of “security theater” the tools and processes that provide a false sense of safety but fail when actual cyber attacks develop. Embrace chaos and organizations can stand up systems that are certainly secure but also robust and is actually antifragile. Organizations can move from a fear-based security to a very mature confidence-based engineering.

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Salvatore holds a EdD in Human Resource Development from North Carolina State University and a Master of Public Administration with a specialty in HR and Organization Development from Indiana State University. He previously served as Vice President and Chief Human Resources Officer for Atmel Corporation and held leadership HR roles at Intel, SAP, and Sun Microsystems. Today Salvatore is a Professor of the Practice at Vanderbilt University.

Salvatore is revealing while AI can certainly augment an HR Recruiter’s efficiency, it simply cannot operate in a vacuum. Similar to many authors, a Human In The Loop (HITL) is absolutely mandatory to prevent a datafication of people from turning into surveillance.

Similarly, his golden rule is that AI should never make a final workforce decision alone. This is understood as ‘Augmentation, Not Replacement’ AI should be used to scan thousands of resumes, but human recruiters must provide the final judgment.

Its all about the power of Regex

Salvatore sees that organizations must move away from “black box” algorithms where the decision-making process is hidden. Through his Seven-Step HR Analytics Cycle, readers will understand “black box” algorithms to transparent, evidence-based practices that prioritize data relevance over vast amounts of data collection.

This is an interesting take when written in February 2024 as the role of Agentic AI was already emerging. Many early use cases are in HR and even in the key elements described below:

1. Explicit Disclosure: Candidates should be informed when AI is being used to evaluate them.
2. Explainability: Recruiters must be able to explain why an AI tool recommended a specific candidate in plain, non-technical language. If you can’t explain the logic, the tool is likely “creepy.”
3. Real-time Voice: Use AI to give candidates a “voice” (e.g., through feedback loops) rather than just treating them as data points to be filtered.

Perhaps Agentic AI’s orchestration changes his position today for the better. Agents are simply executing tasks assigned by HR in order to streamline efforts of understaffed HR teams.

He does outline needed ethics to begin training the AI service, not just the output. He is addressing how organizations must “Understand the DNA” which includes historical data. Naturally if the training data is based on past biased hiring practices, the AI service will simply automate that discrimination. The good news is that he writes about the Relevance Over Availability, Facial expression analysis and social media scraping which all negatively impact the HR outcomes

In conclusion, Creepy Analytics provides readers a roadmap for navigating the ethical minefield human resources. He warns against the “creepy” surveillance that destroys employee trust, advocating instead for a “human-in-the-loop” approach.

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Alejandro holds a MA and PhD in Finance from the University of Pennsylvania.Today he is an Assistant Professor of Finance at the University of Florida.

In the AI-accelerated landscape of financial technology, he is taking a view that legacy quantitative methods face diminishing returns. So here, a new “predictive edge” in Large Language Models has arrived. Perhaps a mix of theory and practice for integrating AI into your investment strategies.

Financial forecasting traditionally relied on structured data: price charts, earnings ratios, and trading volumes. Alejandro is now showing the market’s greatest inefficiency resides in unstructured data. Every day a large volume of news, earnings call transcripts, and regulatory filings have broken those legacy forecasting models. Enter ChatGPT and LLMs which hold the ability to process and perform sentiment analysis at a scale previously impossible.

Financial LLMs have promise

LLMs understand context, nuance, and even now includes “Fedspeak.” This provides investors the ability to leverage LLMs to synthesize complex Information, summarizing in fact, thousands of pages of financial reports into actionable insights. In addition, Backtest strategies can leverage AI simulations regarding historical news events. Finally the ability for LLMs to identify correlated risks which can identify hidden links between global events and unique market sectors missed by financial analysts.

Alejandro is also alerting readers of lingering hallucinations along with the “Black Box” nature of neural networks. This makes it challenging for not only financial analysts to explain why a certain trade was recommended, but any investor using LLMs.

In conclusion, Alejandro views LLMs not as a replacement for financial analysts, but as an intelligent assistant. By offloading the “drudge work” of data processing to ChatGPT, analysts can focus on high-level strategy and ethical oversight.

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Terence is the founder of Hack Future Lab. He is a former Director at Saatchi & Saatchi and McKinsey, and columnist for Inc. Magazine’s Future Proof column. He is Entrepreneur Mentor in Residence at the Massachusetts Institute of Technology and a visiting professor at both IE Business School in Spain and an Entrepreneur Mentor in Residence at London Business School.

Terence is introducing the “fail principle” and seeking to move organizations to relook at failure as a source of data and resilience. He sees failure as a source of innovation.

Organizational leaders who embrace this will not only survive disruption but leverage this for long?term value creation. Terence is sharing his key message of “unlearning” by stating “good leaders learn, but great leaders unlearn.”

So Unlearning must be a deliberate act by leadership. This including challenging assumptions, removing obsolete processes and introducing new behavioral models. The major obstacle to agility is the organization’s culture.

The DARE Mindset

So enter Security Chaos Engineering (SCE), a discipline of peThe DARE framework introduces leaders to address change:

Data: Utilizing Artificial Intelligence and Machine Learning to move beyond intuition toward evidence-based decision-making.

Agility: Analyze and Break down “talent-crushing bureaucracies” to foster speed.

Risk: Move from a culture of fearing failure to acknowledging that “standing still” is more dangerous.

Evolution: Adopting a biological approach to business where adaptation is a constant state rather than a one-time reaction.

Too many organizations have “Zombie Leadership” linked to the outdated Industrial Age mindsets. That mindset fails to deliver “nutritional value” to modern workforces. Perhaps to no surprise, old playbooks is a key indicator organizations fail to adapt and accordingly this results in a slow “decay” in both employee performance and relevance.

In conclusion, this serves as a playbook for “future-readiness,” providing organizational leaders to shift their culture to one of courage. Terence makes it very clear technology is in constant, fast change while humans do not. To succeed and thrive requires embracing constant change.

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Amit holds an MSc in Software Engineering from the University of Oxford and a BSc in Computer Science from the University of Pune. He previously served as Chief Technology Innovation Officer at Avanade. He is also a co-author of Pro WCF: Practical Microsoft SOA Implementation. Today he serves as a Principal Group Product Manager for the Azure AI engineering team at Microsoft.

Generative AI in Action certainly provides a high-level roadmap for integrating Large Language Models into organizational enterprise environments. He is linking the “magic” of AI’s hype cycle with the demands of production architectures across diverse organizations.

This is not a “how to use ChatGPT” guide for non-technical users. This is however a good book for experienced software developers, architects, and data scientists who are new to AI. It explains the core concepts from scratch and provides a learning path sans PhD in mathematics. So, generating quick prompts from poetry to dinner recipes, the demands of building enterprise, scalable AI services is rather challenging.

Gen AI in three phases

Amit presents the book in three phases. First, he starts with foundational concepts, showing differences between LLMs and Small Language Models and Machine Learning. Second, he outlines advanced techniques including Retrieval-Augmented Generation (RAG). He positions RAG as a “secret weapon” for enterprises, since this models are structured to access private company data without the demands of retraining’s large costs. It is in fact, key that here he is addressing “chain-of-thought” reasoning which reveals how to reduce hallucinations. The final segment is Operationalizing AI. He is revealing cost management, latency, security, and ethics.

In conclusion, Alejandro views LLMs not as a replacement for financial analysts, but as an intelligent assistant. By offloading the “drudge work” of data processing to ChatGPT, analysts can focus on high-level strategy and ethical oversight.

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Caitlin holds a MFA in Creative Writing from Lesley University. She is the Founder of Mindwise and teaches courses on digital wellbeing, technology, and storytelling at Stanford University. She has also advised Google, LinkedIn, the US State Department on leadership and experience design.

Instead of viewing technology as the typical “necessary evil” to avoid where possible, Caitlin conveys a position that digital tools can be amazing resources for human growth when implemented with positive intention.

Digital Wellbeing presents readers with the innovative notion that technology needs alignment and enrichment. Caitlin shares the idea of a “Digital Wonder” suggesting that in the age of Artificial Intelligence that we all have an opportunity to use technology to expand both inner and outer connections. There is a unique view that we leverage the new advancements in technology to bolsert our humanity.

Embrace new technology to become more human

Rather than focus on the well written insights of Virtual Reality (VR) and Augmented Reality (AR), Caitlin indicates these can and should be be used as “empathy engines” to provide new insights to increase our body awareness and presence. Caitlin is addressing a large portion on the “loneliness epidemic” of technology which obviously just generates “noise” while minimizing real connections.

So Caitlin reveals frameworks including the Presence Pyramid and the Imagination Index which help organizational leaders and educators to evaluate whether digital habits are creating a shared sense of belonging or merely contributing to digital fatigue.

Furthermore, Caitlin seeings the rise of AI not as a threat to humans, but rather as a partner to create new forms of expression. Readers are encouraged to leverage AI to automate manual tasks to free up time for “high-touch” human interactions.

In conclusion, Digital Wellbeing serves as a call to action for identifying and promoting “humane” technology which prioritizes empathy and ethics. Caitlin sees that as our world is rapidly embracing technology advancements, our society will both remain and become more deeply human.

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A.K. holds a PhD in Engineering from the University of California, Berkeley. He is the Founder and CEO of Sensori.AI, Founder and former CEO of NeuroFocus, a consumer neuroscience company acquired by Nielsen. He was Founder and CEO of MachineVantage which focused on applying AI and machine learning to product development and marketing.

Anirudh holds a PhD in Theoretical and Mathematical Physics from the University of Nottingham and is Chief AI Officer at Sensori.AI. Rajat holds a PhD in Mechanical Engineering from the University of Saskatchewan and is Chief Operating Officer at Sensori.AI.

Neuroscience-powered GenAi enables a very large impact on medicine to marketing, entertainment to education and much more. Blending neuroscience with AI can according to neuroAI place humanity at the center of GenAi. For anyone seeking to understand neuroAI; what it is, how it works, and of course how to put it to work for your organization to gain a competitive advantage within your markets.

neuroAI: memory structures and emotional triggers

Readers can gain an understanding of how to apply neuroscience-powered GenAI to appeal to, according to the authors the hidden driver of 95% of consumer behavior: the non-conscious mind. They suggest this will allow innovators, creatives, and organizational leaders to gain a blueprint of how to utilize neuroAI at scale across their organization. You can gain insights to how the human brain works within your employees.

A.K. build on themes from “The Buying Brain” and the authors sketch out both memory structures and emotional triggers. This allows them to reveal how GenAI can be tuned into alignment. They apply a framework across a variety of markets: packaging and visual design, fragrance and flavor, music, television, and wider brand experiences.

neuroAI focuses on four business pillars: consumer understanding, product and brand innovation, sensorial innovation, and desire creation to reveal how this book can indeed be integrated. This includes pricing and ‘volumetrics’ prediction. It should be of interest to readers their discussions of how different “brains” from teens, boomers, Moms, and also the middle-aged all interact with GenAI differently.

In conclusion, if you seek to understand how to establish relationships with data embedded into human psychology, “neuroAI” should be a very rewarding book. The authors are suggesting (of course) that Sensori.AI offers organization’s an accelerated starting point for engagements. A.K. is banking on how successful organizations will be those who understand their employee’s non-conscious mind. This book can be a highly usable guide to marketing in the age of AI.

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Marjorie McShane is a cognitive scientist and computational linguist known for her work on cognitively inspired, trustworthy AI systems that can collaborate with humans in natural language. She is a professor in the Cognitive Science Department at Rensselaer Polytechnic Institute (RPI) and co-directs the Language-Endowed Intelligent Agents (LEIA) Lab.

Sergei holds a PhD in Linguistics from the Hebrew University of Jerusalem and an M.Sc. in Computational Linguistics from Kharkov State University. Jesse holds a PhD in computer science, with a focus on language understanding from Rensselaer Polytechnic Institute. He is a senior researcher in the LEIA Lab, leading the development of content-centric intelligent agent architecture.

Together they work in related areas: knowledge-rich natural language processing, cognitive architectures, and language-endowed intelligent agents,

AI has relied traditionally on machine learning. The position by the authors is that for over thirty years machine learning development, it is not an all-purpose solution to building human-like intelligent systems. One hope for overcoming this limitation is hybrid AI: that is, AI that combines ML with knowledge-based processing.

Agents are the key

The central message is for any reader who care about where AI is heading. The authors argue that if we want AI we can genuinely trust, we need systems that can understand, reason, and explain themselves—not just crunch data.

So, their core idea is to promote “hybrid AI” which is combining today’s powerful machine learning LLMs with explicit knowledge and reasoning systems. Instead of treating AI as a ‘black box,’ the authors suggest focusing on “agents” that have internal models to make decisions, and can tell you why they did what they did.

Another title, Trustworthy AI is book that I would recommend as a companion:

One of their most appealing themes is trust. The authors keep coming back to a central theme: Would you trust an AI doctor, tutor, or assistant if it couldn’t document its choices? They argue that truly trustworthy AI must be able to justify its actions in human terms, not just offer a confident?sounding answer. This makes the book feel less like a technical manual and more like a roadmap for more responsible, ethical AI in daily life.

That said, this book is not easy reading. Non-technical readers may find many sections highly complex. Someone without a background in AI might prefer to dip into selected chapters or just use the book as a way to understand the direction of “trustworthy AI” at a high level, rather than trying to ingest every technical detail.

In conclusion, this is a serious book that pushes beyond hype to ask what kind of AI society actually needs. For nontechnical readers who are patient and curious, this is a demanding but rewarding view into a future AI that actually might become more understandable, reliable, and collaborative.

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Marcello holds a MS in Computer Science from Università di Catania in Catania Italy. He is a former Full Stack Engineer at Twitter and then Microsoft, a former Senior Applied Research Engineer at Apple and today he is Principal Software Engineer at Luware.

Grokking Data Structures is a very engaging introduction to one of computing’s most foundational topics. This book as designed for readers with a basic understanding of Python. Marcello is easily establishing a base understanding of data structures via the Grokking theme of cartoons, real-world examples, and also hands on coding. The benefit for readers is a refreshingly approach to a complex topic.

So, rather than overwhelming readers with a very deep theory, Marcello is in fact guiding readers into the core, simple data concepts including arrays and linked lists. Then he is moving to advanced structures such as graphs and binary search trees. Each chapter is carefully written building upon the previous to ensure readers can fully understand data structures.

Start with Python to explore code samples

All data structure lessons include its own complete Python implementation. So even from readers who are first engaging Data Structures can immediately begin experimenting. In fact, readers can even many use of these coding examples as preparation for coding interviews or even technical review. The suggestion is that students can use code to supplement their computer science courses. Data structures are certainly vital for your data organization knowledge.

Another title, Grokking Algorithms is book that I would highly recommend as a companion:

The key resource for readers is Grokking’s visual storytelling. There are many illustrations and cartoons which make this complex idea of data easy to understand and remember. This is certainly a perfect book perfect for for beginners. In addition, Marcello is demonstrating why data structures really matter and when to best use them. Yet he is emphasizing the importance of understanding trade-offs with time and memory complexity.

In conclusion, Grokking Data Structures is a must read for anyone seeking to learn and develop a strong foundation in data structures. You will not be lost in any chapter lesson regardless of your background or programming experience. Perfect read!

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Micah holds a BA in International Relations from University of Wisconsin-Madison, MA in Security Policy Studies at George Washington University, and PhD in Political Science from Brandeis University. He is the Director of Research and Learning at the McChrystal Group. He is also a Whitehead Senior Fellow at Chatham House and previously served as a Senior Fellow at the Council on Foreign Relations (CFR).

Micah previously served at Harvard University’s Belfer Center for Science and International Affairs, the US State Department’s Office of Policy Planning, the Brookings Institution, and the Congressional Research Service. He is published in The Atlantic, The Guardian, Foreign Policy, and Business Insider.

Red Team is in fact, a very engaging read and should be almost mandatory for organizational leaders and their IT division. The key reason in fact is that Micah is providing the practice of inhabiting the perspective of potential competitors to gain a strategic advantage.

Whats old is now new

To no surprise, today cyber threats are driving our post pandemic world. Yet the key element in fact for organizational leaders is Micah’s introduction of Red teaming. This is not a recent concept. In fact, Micah traces the development of a red team dating back to the Vatican’s Devil’s Advocate. Readers may certainly recall the 11th century Vatican official first charged with discrediting candidates for sainthood. Today red teams are in fact, groups of skilled third parties, who use simulations, vulnerability probes, and alternative data analysis to discover various risks that organizations cannot see via Group Think.

Remember Nixon’s Watergate ?

Groupthink is a psychological phenomenon. Members of a group prioritize consensus over critical analysis and independent third party thinking. This certainly leads to decisions seen as irrational or even dangerous to organizations as the group’s members will suppress dissent, ignore facts, and fail to evaluate the consequences of their current-state position. The Nixon Administration’s Watergate crisis is a perfect example.

So, today Red teams are providing organizations seeking to better understand their rival’s interests, intentions, and capabilities. When understood and properly deployed, red teams certainly deliver results that provide an edge or advantage over their competition. Red teams also filter critical intelligence and also troubleshooting military missions in their planning stages which seek to reduce risk. The case studies from Micah are providing Red Teams a foundational base and insights on to leverage their information.

In conclusion, Red Team is very essential reading for anyone interested in organizational strategy, risk management, or security. Micah is providing deep insights into the value and limits of thinking like the enemy. Another book not to be missed.

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Amanda holds an Associate in Computer Information Systems from North Central State College. Today she is a Senior Product Manager of Cybersecurity at Blumira. She is co-host of Brakeing Down Security, provides training for organizations on creating tabletop and incident response playbook programs. In addition, Amanda is CEO and co-founder of Mental Health Hackers, a non-profit organization that aims to raise awareness about mental health issues in the cybersecurity community.

William is the Director of Security at Modus Create. His previously served at The Walt Disney Company, Raytheon Technologies, and as CISO at Fairfield University. Reyor is also a co-founder of Security BSides Connecticut.

Lee holds a BSc in Media Technology from Teesside University, Middlesbrough, North Yorkshire UK. He is the founding security engineer at OpsHelm and has previously worked across multiple sectors: including finance, telecommunications, hospitality, entertainment, and government.

The authors are recognized for extensive real-world experience and leadership in cybersecurity. This is a perfect update to a cyber world filled with ever increasing high-profile hacks, data leaks, and ransomware attacks. For organizations lacking a formal InfoSec program this provides a baseline and is most helpful for the Information Security community.

Seeing the alphabet soup

The book is addressing techniques to confront breaches, compliance, network infrastructure, password management, vulnerability scanning, and penetration testing for starters. There is a ton of great insights that organizational leaders outside of IT should be aware of in order to further protect their teams.

Amanda, William, and Lee provides insights to incident response, disaster recovery, compliance, and physical security solutions. Their learning includes the basics of pen testing via purple teaming. In fact, they address vulnerability management using automated processes and tools as well as IDS, IPS, SOC, logging, and monitoring. This list of critical topics certainly includes the essentials of logging, monitoring, and the use of IDS/IPS and SOCs. Finally, they do not skip hardening advice for both Microsoft and Unix/Linux platforms. They also address purple teaming, the blend of red and blue team exercises to bridge offensive and defensive security testing.

In conclusion, the updated version of The Defensive Security Handbook is a valuable resource for readers new to information security. At the same time seasoned IT professionals seeking to establish a solid knowledge base will benefit as well as organizational leaders who need to understand risk in today’s modern world.

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Clayton holds a BS in economics from Brigham Young University. He was awarded a Rhodes Scholarship and spent two years studying applied econometrics at Oxford University. He also holds a MBA and PhD in Business Administration from Harvard University. Michael holds an BA in Philosophy from Harvard, an MBA from Western University in London, Ontario Canada, and a DBA from Harvard.

Even in the age of AI’s disruption, as noted by Rudyard Kipling, “Funny how the new things are the old things.” Time and again, I frequently find lessons so well presented, they can easily be adapted to other fields or circumstances. At its core, AI is fundamentally about using technology to create new value as every organization is seeking new growth channels regardless of marketplace.

In our new AI-driven era, Rudyard Kipling’s words ring true: “Funny how the new things are the old things.” As I read books from various fields, I continually encounter well-crafted lessons that, while seemingly fresh, often serve as bridges connecting insights from one field or event to another.

Clayton Christensen’s simply amazing 1997 bestseller, The Innovator’s Dilemma, introduced readers to the groundbreaking idea of disruptive innovation. He demonstrated how even respected companies and organizations can do everything right and yet still lose market leadership.

Emerging Technology needs a framework

In The Innovator’s Solution, published in 2003 and revised in 2024, expands on their idea of disruption. And could AI be anymore disruptive today? They reveals how organizations really must become disruptors themselves. For organizations that seek to survive the AI revolution, this book is vital.

Key lessons:

1. Start innovating early. Organizations fail as they wait for their leaders to reach a stage where it becomes safe innovate. In this case the pressure will be too much for those who do not easily accept the impact of innovation.
2. Innovating early will be ignored by your organization’s competitors. They will find it risky to be in this area. Clayton’s example of the US steel market is the perfect example.
3. They stress the legacy approach of slicing and dicing customers by gender, age group, income group, geography may not succeed all the time. Their fast food research called this out. When those companies abandoned slicing and dicing, they saw a surge in sales.
4. Distribution channels are also impacted by innovation. If organizations do not incentivize, their own employees will not be motivated resulting in a decrease in revenues and profits.
5. Focus on two types of customers: the over-served and the non-users. While one is easy to gain, the second will only work by making the product or service irresistible.
6. Decisions should not be based on what is the “core competency” of the organization, but rather based on what should be the “core competency” of the organization. IBM’s approached PC manufacturing the same way as they did for their mainframes was the perfect example and obviously IBM is no longer a vendor in the PC business.
7. Organizational values also also a key to their success or failure. In most legacy organizations, the resources and processes are fixed and inflexible. Over time those values also become locked and difficult to change.
8. Organizational leaders must drive disruptive innovations to grow in their organization. They need introduce and maintain support for these innovations. Without the right atmosphere, the organization will stumble.

Key lessons for today’s AI innovate solutions

The book in fact reveals just how timely and relevant these ideas continue to be in today’s hyper-accelerating AI environment.
Their insights on crucial decisions certainly provide a path for organizations to embrace disruptive growth and propose guidelines for developing your own disruptive growth engine.

In conclusion, readers will certainly identify elements that cause their organizational leaders to execute bad decisions. Many struggle to embrace, develop, shape new frameworks in order to launch in the right conditions for their organizations. This is a must-read for all senior managers and business leaders responsible for innovation and growth, as well as for members of their teams.

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