Hey, everyone. This is Gopika Shah. Thank you for joining today's session. We will be diving into your topic, from data to decisions and building responsible AI through strong governors.I'm a technology officer at Falcon Risk Services. I'm a technology officer at Falcon Risk Services, and I lead the strategic development, development of enterprise technology, data architecture, analytics, and AI capabilities. Today's topic we will be discussing more about AI and how data governance becomes the critical role on today's organizations. That makes help us make decisions, engage with customers, manage the risk, and curate the business value. Right? But behind every successful AI initiatives, right, it's something far less visible. It's trusted data, strong governors, and responsible oversight.

Today's while many organization, are racing to adopt an AI, far fewer are fully, far fewer are fully prepared to go on at its scale. Right? And the gap between AI adoption trust is becoming one of the biggest challenge, challenge that the leader face. In today's session, I would like to explore how AI and data governance are no longer, a separate conversations. They are becoming deeply interconnected because ultimately, AI is no longer a technology differentiator or enable. It's becoming more of a leadership facility for all of us. This is more of a, you know, inside the AI spectrum. Right? And this slide covers that's something we already know. Right? AI AI has evolved significantly over the period of the time. Right? From traditional artificial intelligence to machine learning to the deal deep learning to generative AI.

And now we are more onto a large language models. Each layer is actually as we exposed to it. Right? It's increasing complexity capability and business impact. While organizations are rapidly advancing on AI technologies like GNEI or large language model, many are still building the foundational governors, data qualities, and operational framework that require them to scale this capability more responsibly. Right? The more the intelligent, the more the intelligent AI system becomes, the critical, the more, it the critical transparency, explainity, and governance will become. Right? Because organization not only leverage AI effectively effectively, but it's also need to ensure the decision that's made by all these AI system are trusted, ethical, and accountable. This is more of a data workflow, what we call it, right, where GenAI rule output, stays. And what this is actually showcasing us is that most organizations, right, we all focus on the outcome, of the AI itself.

The content generation automations to InsightXR, but the real business value and the risk actually exists within the underlying the data workflow. The governance process is an operational life cycle supporting through the AI. Responsible AI requires organizations to govern, the entire workflow end to end. Right? Including data solutions, application usage, model evaluations, optimizations, ongoing monitoring to ensure transparency, security, compliance, and trust. For me, AI is not an a one time implementation. Right? It's a continuous operational capability that depends on the strong governance, high data quality, and human oversight. Right? There there's all conversations human on the loop, human in the loop, and continuous optimizations to deliver the scalable and trust trustworthy outcome. So now moving to the next slide, this is more focused about the data and the AI governance.

So it's organizations are rapidly accelerating the AI adoption, but governors' maturity is not going on the same, pace. And because of that, it's creating growing risk around the trust, comp complexity, compliance, explain explain duty, and operational accountability. Traditional, when we think about, right, the traditional data governance framework were designed for more structured data environments, but modern AI, requires now a more advanced and integrated governance approach that requires data quality, model oversights, ethics, security, privacy, and continuous risk, continuous monitoring.

It's all about now the responsible AI. It's not only about the the governing the AI models, but it's also about using AI to strengthen the governors itself. So all of you, us are, like, kind of familiar now with the the traditional pillars of the data governors. Right? So traditional data governors are primary heavily designed around the structured enterprise data, Focusing more about the foundational capabilities such as metadata management, data warehousing, data quality, reference data, master data, etcetera. Right? These pillars, we went still essentials. But since AI is now placing significantly, but now for each of these functions, now AI is just gonna put a significant and pressure on each of them. Right? Now AI introduced a new governance challenge at a scale the traditional framework were never built to handle.

That includes increasingly messy data volumes, unstructured content, algorithm biases, and increasingly regulatory explanations on AI driven decisions. Many organizations struggles because governance often tutor. Right? It's a one time cleanup activities rather than a continuous operational discipline, and that's gonna become critical if any organization wants to use AI within their organization. Now we're gonna focus more about that. Since we are introducing AI, what kind of, disruptions that is gonna cause. Right? So now think about it. AI trust systems are as trustworthy as the data they are trained on. Right? Today, now, and all of the organizations are facing the challenges around the volume of the data, poor data quality, regulatory compliances, and algorithm biases. The traditional government governance approach that we all had are now struggling to keep the pace with the scale and complexity of the modern, AI environments. Right?

So volume of the data. Right? Earlier, we were just focused on the structured data. Now we have to think about the structured data, and the volume of the unstructured data is huge. Right? So one of the biggest destruction will be unstructured data. And I've much of this data, right, when it comes to an unstructured data, it's it's the struggle is that because the data remains unchanged. It's unclassified. Right? It's not being classified what type of data it is. It's highly exposed to everyone within the organizations and creating, what many organization call as a visibility gap in a governance and security. So challenges with an AI governance. Right? So what are the some of the challenges that we are going to face about it is, we need to have a proper process on approving these datasets. Right?

So we wanna make sure that the data that's feeding to the AI models are being approved, and it's being used to define and maintain the policies. Right? And then it's more about preparing now your data for to feed your models and preprocessing that may include sensitivity masking and de identification. So earlier, we were still feeding all the data to, you know, our reporting, etcetera, that it's being, you know, sensitive or not sensitive has been classified now. Those are the challenges that we're gonna force that we wanna make sure that our data is something, being approved by, by a accountable person to use it in the AI model. Some of the challenges that we are going to face within the data is more about the complexity. Right? So now we are going to create, diverse datasets that includes the structure and unstructured data, and ensuring the data integrity through the cleaning and preprocessing is going to be challenging.

And monitoring the data. Right? So once the model, is deployed in production, ongoing monitoring, of incoming data and predictive performance, it's going to be very critical as well. And the last one, it's gonna it's that's when I feel like it's gonna be the main factor is the volume, velocity, and veracity. So this slide is actually what I call it is a twin accelerant on more about how AI and regulations ties with each other. Right? Organizations are facing two major accelerant, and that is driving for the responsible urgency for the responsible AI. The first one is the shadow AI. And I think this one is heavily used right now, in any organization or any conferences you use. Right? Because it's creating a significant governance risk as employees increasingly use public AI tools and LLMs with a sensitive enterprise data while poorly go internal AI system that can expose confidential information to unauthorized users.

So within my organization, we are being very careful on which are the data that we need to expose to. Plus, I think we're we have very heavily ensuring that the the public tools that within the any, any divisions within our organization we use, we actually know what type of data they're pushing it, and we actually have put lot of, guardrails to ensure that we do not, by mistake, sense it, put sensitive informations to the large language model that's being used by outside.

Right? And another one that's very, now we're gonna face more on the issue is the modern, cyber threats. Right? And evolving regulations are forcing organizations to rethink on the governance strategy. And integrating AI governance, cybersecurity, privacy, and compliance, and data protections into a unified framework that enables trusted and secure AI adoption at scale. So I work in the insurance industry. And, you know, for us, one of the biggest, cyber threat will be that us exposing all our policies, to somebody, that can use it against our renewals. Right? So insurance companies always makes more money through their renewals because that's your they're your existing business. You know that you will be able to renew the policies.

Worse versus, if you expose all your policies, if one of your employer or somebody accidentally got hold of all your policies, that's really gonna cause cause a lot of issues. Right? And due to due to that, what's going to really happen is that, you you we actually have, we actually were able to get a contract with one of the company. It's called dark web I dark web IQ. And dark web IQ. And because of that, it's gonna actually tell us that which which of our data is getting exposed to anybody if it is. Right? The next slide is more focused about the rules of data quality in AI. Right?

And what this is is that, you know, data quality is now going to play, a more important and critical role. Right? Data quality is not on longer just an operational person. Right? It has become a critical business and governance issue. Just one minute. So, everyone, I actually have a young kids, and, one of the kid was actually, crying. So I just had to put them back to bed, because it's very early morning. We were just talking about more on the data quality, and now data quality is, no longer an operational concern. Right? It has become a critical business and governance issue in the AI area. Right?

AI models are as only reliable and fair and trustworthy as the data they are trained on. Right? In traditional, analytics environment, right, we were more focused about that. When if there is a poor data quality, it might just, you know, result into an inaccurate results. But now in AI environments, like poor quality or bias data can scale and incorrect decisions and operational risk, reputational damage much faster and across as much larger impact area. Right? And ultimately, it trusted AI's charts with the trusted data. And now data quality will play more critical role than it did before. So how is this going to change? Right? So evaluation of the data and AI governance. Right? So as we spoke about earlier, there's a traditional governance and then how AI is causing a disturbance to the governance.

Now we're going to be focused more about the evaluations to the unified data and AI governance. So its organization can no longer, manage AI governance and data manage, data governance as its two separate functions. So modern AI ecosystem will require a unified governance model that connects the data models, risk management, privacy, and operational oversight into a single integrated framework. Unified governors, right, improves, the enterprise visibility and traceability by creating a centralized data, centralized, data warehouses, modeling the catalogs, and enabled enabling the organizations to better understand how AI models are trained on, what the data they use, and how decisions are being made, and where risk may really exist on.

Ultimately, right, unified governance creates a foundation, for the scalable, trustworthy AI adoption by balancing, innovation with transparency, accountability, and regular alignment regulatory alignment. So this is what, we actually have implemented on our end or more on the AI and data maturity framework that strengthens our foundations for the AI success. So the AI success is not achieved right through the techno technology alone itself. It could result of progressively maturing journey that begins with the strong foundations, in, you know, evolves through the, centralized and governed data in ecosystems and ultimately enabling the predictive insights and measurable business value. So the first one. Right? The first stage really focus on establishing AI readiness through enterprise data strategy, integration, governance, and data quality. Right? Organization first must create a trusted, accessible, well governed data environments before they can effectively scale AI and any analytics initiatives. Right?

The second one is more focused on the centralizing warehouse, creating a single source of truth of data. And the last one is the predictive insights and the business value. Right? So this is the final stage where organization start seeing the real business value from the AI. Leveraging predictive analytics, machine learning, and operational intelligence to improve the decision making and increase efficiently. Right? Across all these maturity stages, a foundational principle remains the concern. Right? So trust on data and transparency and governance and accountability. Right? These principles ultimately determine whether AI becomes scalable, responsible, and sustainable at the enterprise level. And and this is the last slide. Right? That focus more about responsible AI that drives trust, value, and impact. Right? So as we narrow through it, right, responsible AI, it's not only about managing the technology risk.

It's also about trusted system that organizations, customers, or regulators, or or even for us, like, we wanna make sure all of our underwriters can rely on. Right? Trust is becoming the foundation that determines whether AI can set scale successfully across the organization. One of the biggest, leadership, shift happening today is the movement from AI experiment to an AI accountability. Right? Organizations are now expected to explain how the AI decisions are being made, monitor their biases, and furnish and protect the sensitive information and ensure that human oversight, right, remain the part of the critical business decisions on on the workflow. Right? Another impact important evaluation, is the use of AI itself is to strengthen the governance. Right? AI is increasingly helping organization now to automate the data, classify the data. Right? Detect the risk, improve the metadata management. Metadata management were not as critical before as it is right now.

And manage enterprise scale on the unstructured data environments more effectively. As I close today's session, I will leave you with one final thought. Right? So that AI is advancing, and increasingly fast, but long term success will not be determined who adopts the AI at the fastest. It will be determined by who builds AI the most responsibility. Right? The future of AI is not about automation of the or intelligence. It's about the trust and trust within your data. Right? So every organization should focus on invest in the trusted data and strong governors and ethical oversight. And account table, AI practices that will be the best position to innovate confident and scale sustainability. That was one of the survey. I actually have done it before on, like, hun 200 plus organization that will focus a lot of questions related to the AI policies as well as more on understanding that, you know, whether you do get a separate budget or, more focused on do you get a separate purchase from your traditional governance team to expanding now to more about the AI governance team, etcetera.

It was very eye opening that, you know, in the two years back, a lot of organizations were not thinking about it, but now they are think really thinking about investing into an AI governance. I really appreciate, thank you everyone, and I really appreciate, your time and participation today.