How Ksenia Laputko—A Committed AI Leader—is Putting Humans Back at the Center of the Artificial Intelligence Revolution?
The future gets transformed gradually, in phases. It never changes suddenly. Our decision-making, our responses to the changing scenarios, and our actions in present, along with how and what we anticipate the repercussions of our actions will be, are the crucial factors in that transformation. Critical thinking is the secret here. It is in this regard that the power of the most iconic women leaders like Ksenia Laputko becomes extremely important. Astutely aware of the eventual transformation of times, and with a career uniquely intersecting AI governance, global data protection, and legal academia, Ksenia Laputko reflects that an organic evolution shaped her legal foundation rather than a single defining moment.
An AI Governance Officer and Global Head of Data Protection at Joblio, Ksenia Laputko spoke in an exclusive interview with The Women Globe, where Ksenia Laputko shared how she is putting humans back at the center of the AI revolution.
Your career uniquely intersects AI governance, global data protection, and legal academia. What defining moment convinced you that the future of innovation must be built on responsible governance rather than speed alone?
I’d say it wasn’t a single defining moment, but rather an organic evolution shaped by my legal foundation. As a lawyer, I’ve been trained to think several steps ahead – to anticipate consequences, not just immediate outcomes. Long before terms like ‘trustworthy AI’ or ‘AI governance’ entered mainstream discourse, I was already examining this technology through a fundamental legal lens: compliance, safety, and accountability.
My perspective has always been straightforward: it doesn’t matter how innovative or fast-moving a technology is – if it harms people, violates rights, or creates systemic risks, it’s not ready for deployment. We may have used different terminology then, but the core principle remained constant: those who develop and deploy AI systems bear responsibility for ensuring these technologies are safe for users, for society, for democratic institutions, and for our environment.
My legal training naturally positions me to see innovation not as separate from governance, but as fundamentally dependent on it. The two must advance together, or neither advances sustainably.
As an AI Governance Officer and Global Head of Data Protection, how do you reconcile business-driven AI innovation with evolving regulatory expectations across regions such as Europe, North America, and Asia?
I actually don’t see these as competing priorities, and that mindset shift is crucial. In my experience, the best innovation happens when compliance is baked in from day one, not added as an afterthought. My role is to help teams see governance not as a roadblock, but as a foundation for sustainable growth.
Here’s how I approach it:
I engage early. I sit with product teams during design, not after launch. When developers understand what’s required upfront, they build smarter solutions that work across markets. It saves time, reduces risk, and actually speeds innovation.
I focus on principles, not just rules. Yes, Europe has the AI Act, the US takes a sectoral approach, and Asia is diverse, but they all care about transparency, fairness, and accountability. I build frameworks around these shared values, then adapt for local requirements. That creates consistency without bureaucracy.
I reframe regulation as guidance. These frameworks aren’t barriers; they reflect what society expects from us. When leadership understands that good governance protects reputation and builds trust, it becomes a business asset, not a compliance burden.
At the end of the day, my goal is simple: make responsible AI the path of least resistance. When that happens, innovation and governance move forward together.
Many organizations still treat AI governance as a compliance checkbox. From your experience, what mindset shift is essential for leaders who want AI to become a sustainable competitive advantage?
Well, a lot of things in the age of AI have started to become just a checkbox.
If you observe how people actually behave, you’ll notice something worrying: many stop engaging deeply. They skim instead of reading. They click instead of thinking. They “comply” instead of truly understanding. But the cure for individuals, for businesses, and for professionals building strong AI governance is actually very simple: Stay human. Think about humans. As I often remind my students, Michael Jackson once said: “We are here to make the world a better place.” And honestly, that is exactly what responsible AI governance is about. Ask yourself a very practical question:
Would you want the product you use every day to be unsafe, poorly prepared, or intrusive to your privacy? Of course not. So the governance mindset is straightforward but powerful:
Design and govern AI systems as if you and the people you love will live with their consequences every day. That is how responsible AI starts.
You have worked extensively with GDPR, ePrivacy, PIPEDA, and US privacy frameworks. How do you see global privacy laws shaping the next generation of AI systems?
They’re not just shaping it, they’re already defining it. The reality is straightforward: the moment AI touches personal data, privacy law applies. There’s no workaround. If an AI system trains on, processes, or uses personal data in any way, it must comply with applicable data protection regulations. That’s non-negotiable.
But here’s what’s critical to understand: we’re only at the beginning of this regulatory evolution. Beyond privacy, we’re already witnessing the emergence of more targeted legal frameworks. AI-specific legislation like the EU AI Act is establishing risk-based requirements. Intellectual property rules are expanding to address AI-generated content and training data rights. Dedicated liability regimes are being developed to clarify accountability when AI systems cause harm. These aren’t theoretical discussions—they’re actively being drafted and enacted.
In other words, privacy law is the floor, not the ceiling. The legal ecosystem around AI is expanding rapidly, and organizations that treat compliance as a checkbox exercise are already behind. The organizations that will thrive are those that embed governance early, anticipate regulatory shifts, and understand that responsible AI isn’t a burden, it’s a strategic foundation for sustainable innovation.”
What are the most common governance blind spots you observe when companies deploy AI at scale, and how can leadership proactively address them?
I consistently see three major blind spots.
The first is treating governance like a checkbox. Companies get approval, deploy the system, and assume they’re done. But AI doesn’t stay static. I’ve watched chatbots that performed perfectly in testing start producing biased results months later because user behavior shifted. Leadership needs to build in continuous monitoring -quarterly reviews for high-risk systems, not just annual audits.
The second is what I call shadow AI. Marketing adopts ChatGPT for content. HR tries out a resume screening tool. Nobody loops in governance because these seem like small experiments. Then we discover they’re processing personal data or making decisions that affect people’s lives. Leadership needs to create a simple registration process where any AI tool gets flagged before deployment. It doesn’t need to be bureaucratic, but it needs to exist. You can’t govern what you don’t know about.
The third is mixing up technical explainability with real transparency. Being able to diagram how a neural network functions doesn’t help someone understand why their loan application was rejected. Leadership needs to ensure there are plain-language explanations that actual users and regulators can understand, not just technical documentation for data scientists.
What connects all three? Organizations treat AI like any other software when it actually needs active, ongoing governance. The companies that get this right don’t just delegate AI oversight – their leadership actively champions it.
As a privacy educator and mentor, what core skills do you believe future privacy and AI leaders must develop beyond legal and technical expertise?
I believe the core skill set has shifted, and today the two most critical capabilities are analytical thinking and critical reasoning. What concerns me is that I increasingly see a gap in both, especially among students. Alongside this, communication and broader soft skills are also weakening. In AI governance and privacy work, technical knowledge alone is not enough. You must be able to translate complex concepts across the organization to speak with engineers in their language and then clearly explain the same issues in a boardroom to non-technical leaders. If you cannot communicate effectively, you simply cannot make governance work. And here is the paradox of the AI age: while our tools are becoming more sophisticated, these human skills are quietly eroding.
Many people now prefer interacting with AI over engaging with colleagues. Even more concerning, the use of large language models is increasingly being confused with genuine critical thinking. But they are not the same. AI can assist thinking, but it cannot replace the responsibility to think.
You are deeply involved in training and certifying privacy professionals worldwide. How has the role of a Data Protection Officer evolved in the age of AI-driven decision-making?
Right now, with the rise of artificial intelligence, every privacy professional needs at least a foundational understanding of how AI works. They should understand how AI systems make decisions, why large language models behave the way they do, and how AI agents operate. Without this baseline literacy, it is very difficult to properly identify where privacy risks may emerge.
Even though many organizations formally separate the roles of Data Protection Officer and AI Governance Officer, in practice, the overlap is significant. Anyone working in privacy today must understand AI because AI is no longer a niche technology. And importantly, AI is not magic. It is a system that creates very real, very traceable risks. From a career perspective, this is also a major growth opportunity. I mentor many privacy professionals seeking advancement, and one of the most effective paths is building competence in AI governance. For organizations, the logic is equally clear.
AI governance talent is in high demand and expensive to hire externally. The most efficient strategy is often to upskill existing privacy professionals, especially DPOs, who already understand data flows, risk, and compliance. That’s why I have mentorships, courses, and have written books on AI and privacy.
As a woman leading at the intersection of law, technology, and policy, what barriers have you personally encountered, and how did they shape your leadership philosophy?
I would say the barriers themselves have not changed much.
There are still moments when some people, often men, do not immediately see the professional in you or are reluctant to hear their decisions challenged from a privacy or AI governance perspective.
This is especially true in AI, which is still a relatively new field that many stakeholders do not fully understand. You often have to educate while you advise — and sometimes you are doing both in rooms that were not historically designed to listen to women.
But the solution has always been the same
When you demonstrate deep expertise, clear understanding, and strong communication, and sometimes a good sense of humour, perceptions shift. Once people see you as the subject-matter expert, resistance tends to fade.
Professional credibility, consistently demonstrated, is still the most powerful equalizer.
Through your academic and professional work, you influence both boardrooms and classrooms. How important is education in closing the gap between AI innovation and ethical accountability?
Education is absolutely fundamental, but I don’t mean education as memorizing and repeating information. To me, real education means teaching people how to think critically and how to analyze. As I’ve mentioned, this is especially crucial in the age of AI governance. When people truly think and analyze, they begin to see the risks AI can introduce. They can identify the specific risks within the projects their organizations are building and, most importantly, they can mitigate and prevent those risks early. That is why, when I teach young professionals, my goal is not to turn them into parrots who recite frameworks. I teach them to reason. Because in real-world governance, cases are rarely clean or straightforward. The work almost always comes down to communication, judgment, and the ability to navigate complex, imperfect situations. Governance is not about memorizing answers – it is about learning how to solve hard problems.
Looking ahead, which emerging AI risks concern you most from a data protection and human rights perspective?
I would say these two perspectives are deeply interconnected and largely stem from a lack of AI literacy. Many people still treat AI as a harmless tool, almost like a toy, and as a result, they underestimate the real implications. They often do not worry about automated decision-making until the impact becomes personal. But the moment you explain that they may not have received a job interview because their CV never reached a human reviewer, the concern becomes very real. From a governance standpoint, the most significant risks emerge at the intersection of privacy intrusion and automated decision-making without meaningful human oversight. These risks reinforce each other: the more data-driven and opaque systems become, the greater the potential impact on individuals’ rights and opportunities.
In my view, the two most critical areas of concern remain the growing intrusion into private life and the gradual erosion of meaningful human review in high-impact decisions.
What advice would you give to organizations that are just beginning to formalize their AI governance and privacy frameworks?
The easiest path is to build compliance from the start. Don’t fall into the trap of thinking that if there are no fines yet, AI governance can wait. That mindset is exactly what leads to costly mistakes and eventually, major regulatory and reputational problems. The earlier you embed compliance, and the sooner you invest in educating your teams on AI governance and privacy, the stronger and more trustworthy your product becomes. More importantly, early governance creates a real competitive advantage. And let’s be practical: building it right from the beginning is always far cheaper than trying to fix and patch systems later, especially when fines, enforcement, and public scrutiny arrive. Proactive governance is not overhead. It is a smart strategy.
Finally, what legacy do you hope to build as one of The Most Iconic Women Transforming the Future, particularly for the next generation of women entering AI, law, and governance?
It’s not about legacy – not yet. But as a mom, I truly hope my example becomes a benchmark for my daughter and for the girls I teach. I want them to see that we really can have range. We can be present moms and, at the same time, lead compliance and governance in major organizations. We can write books that travel the world. We can build careers that matter.
You don’t have to shrink your dreams to fit someone else’s expectations. You can become whatever you truly dare to be.