Empowering AI Governance and Regulation Through Future Skills | AI in Action 2023

Hi everyone. So very excited to be here at AA in action to deliver a talk and present. I am hoping that you're having a great time so far.

I'm really excited to present, uh, on the topic, which is my personal favorite about empowering AI governance and regulation through future skills. So, without further ado, let's dive deeper into the, uh, section. So brief introduction about myself as I'm the currently the head of corporate development at Practical DevSecOps, which is a global community and international certification body dedicated in advancing the modern security skills across DevSecOps and security domains.

I'm also the DevOps ambassador and, uh, more importantly, continuous learner and DevSecOps enthusiast by heart. So really looking forward to, uh, this session to share more about the AI governance and how future skills are really necessary to prepare the professionals, not just for the present era present, but also for the times ahead. So, I've broken down the agenda accordingly, so that a brief introduction about AI governance, and then also giving a broad overview from the industry perspective, from the economics perspective, uh, couple of challenges around there, and then break it down into different set of skills which are required, uh, to be enhanced or upskill reskilled from the professionals, from the organizational perspective, uh, backed by some of the industry research so that you get some good, uh, uh, takeaways after this session.

And hopefully get an idea about how the industry and the overall economies are moving more towards, uh, the skills first economy, uh, as part of the new, uh, gen AI or the artificial intelligence era. Okay, so now talking about the AI governance and regulation, I think one of the most important thing specifically now is to define about what does that mean? So, uh, more necessarily and definition, which kind of encapsulate about, uh, what is AI governance in essence.

So, AI governance is the ability to direct, manage and monitor the AI activities across organization. And this practice includes processes that trace and documents data models and associated metadata in pipelines for audits. So this specific, uh, I think statement kind of defines the overall crux about AI governance, but more importantly, uh, it is, uh, required to increase transparency into the model's behavior throughout the lifecycle.

So the data which, which is like really the key factor when we talk about AI or training the large language models, it is the most important factor within the development and also the potential risk, uh, which is associated with that. So, hence, if we, uh, look at the AI governance and how safe AI driven future or responsible AI future is encapsulated. So it's a wide spectrum of capabilities because it is combining traditional governance construct like GRC, governance, risk and compliance accountability, et cetera, with differential ones such as, uh, ethics review, bias testing and surveillance monitoring, which is really necessary and will become more necessary as we progress ahead towards more advancement, uh, towards AI as well as gen ai.

So now when we look at the need for effective AI regulations and governance, so all the companies, uh, when we talk about every company is a software company, and every software company has to be an AI company, which we'll also discuss a bit later. So that means now the companies are, uh, are, are in not just increased pressure, but also as part of their own policies to include, uh, AI governance frameworks and all these seven major developers in the us Amazon anthropic, Google Inflection Meta, uh, Microsoft OpenAI has already agreed in a meeting with, uh, president Biden in July, 2023, this year itself to commit standards and implement guardrails as part of this governance framework. So you can imagine the, uh, seriousness and the need for effective air regulation and governance, which is required not just from the industry perspective, but also from the economies perspective, from the government perspective as well.

And that is why if we also narrowed down the significance of it, why, and also attaching it with some of the key timelines which has been associated with the evolvement of AI regulation and governance. So back in April, 2019, European Union Ethics guidelines for trustworthy AI was, uh, device, and I also like a Deloitte's, uh, trustworthy AI framework who will recommend if you have not seen it already, which kind of provides more holistic, uh, perspective around AI governance as well. And then in May, 2019, uh, uh, organization of economic Corporations and development AI principles were also announced.

And then two years later, uh, UNESCO's recommendations on the ethics of, uh, artificial intelligence was released. And then, uh, within this year, a lot of development has taken place, starting with the nic's AI Risk Management framework. And then at the G seven meter, uh, a process on generative AI was also announced.

So overall, across all the continents from Americas to Africa to Asia and Europe, uh, AI strategies and governance and framework has been defined and is being worked on as we speak. So there is a tremendous need and significance of air regulation and governance. And that is why if you look at some of the, uh, significant development that has taken place in some of the major economies, uh, within us, the Algorithmic Accountability Act, uh, within this year, the AI Disclosure Act, within this year, the Digital Services Oversight and Safety Act, uh, of 2022, and, uh, within Canada, artificial Intelligence Data Act within Europe, EU Artificial Intelligence Act, uh, within Asia, uh, not just in China, but in other countries as well.

We have seen a lot of regulatory developments that is taking place and which is a positive trend, uh, about EA regulation and governance. And then if we look at some of the common practices, or rather we can say best practices for AI regulation and governance framework, so according to standard and the poor global. So this is a good template which, uh, I found more relevant, aligning with the developments which are happening across organization, industries, uh, governments and economies.

So human centrism and oversight. So making it more, uh, empathy specific, uh, human oriented, uh, is really important. And that's why responsible ai, the ethical ai, I think, which is part of the second common best practices, and then ensuring transparency and explainability accountability liability, it is really important because that's where, uh, the confidence between all these stakeholders can remain intact and keep on building, uh, while ensuring the, uh, risk associated with this is minimized.

And then, of course, uh, uh, ensuring privacy and data protection while also ensuring safety, security, and reliability is, uh, really essential to develop a more optimized version of AI regulation and governance framework. So, and then again, at a global level, uh, world Economic Forum has also announced a AI governance alliance, which is dedicated of uniting industry leaders, governments, academy institutions, civil society organizations, nonprofits, to champion responsible global design and release of transparency and inclusive AI system. So this set of practices and developments, which are happening across the globe, uh, significant, uh, signifies the importance of AI governance.

And that's why I thought it is really good to provide a more holistic overview about AI governance and regulation before we dive into the future skills template, which is really necessary for the professionals and organizations to align to. So gaining the future skills in the age of gen AI is not the matter of, uh, uh, let's say as an option, but it is the matter of urgency. It is the matter of really key significance, and that's why, uh, the choice is always, uh, for the professionals, for the communities to advance as the technology is being advanced.

Because if you look at some of the innovation cycles, which has happened over the, uh, historical timelines, right? So if you break it down in, into, uh, six waves of innovations, so between first and second waves, there was a period of, uh, uh, 60 to 50 years, and then same, uh, within the second, third wave 50 years. Then it kind of reduces from a decade, like, uh, 40 years, 30 years, 25 years, which is the current sixth wave.

But with the advancement of, uh, gen AI and the current investment and developments, which we are seeing within ai, uh, the next, uh, wave is, uh, being considered to happen within the next 10 years itself. So you can imagine the kind of acceleration it is providing towards the innovation across the industry. And, uh, that's where, uh, I mean, uh, I like this, uh, graphic reading, uh, uh, interesting every time because this, this is the board meeting which is happening, and, uh, the management team is discussing about the future of ai.

And then sure enough, generative AI was announced, and we have seen the kind of drastic development which happened across the sectors. So that is why, uh, now every company will not just be a software company, but it will be an AI company in essence. And it is not, uh, the matter of, uh, something as an option, but a matter of necessity.

And that's why if you look at some of the disruption, which is caused by journey AI and, uh, AI overall within the software development cycle. So it's not just limited to, uh, one or two key domains around cloud services or virtualization, but it has been really significant across the entire software development cycle, right, from cybersecurity. So security remains as the key component, which is, uh, an umbrella of key pillar between every set of domains.

And then you can see the, uh, impact the gene is making. So this is the research from Boston Consulting Group, and then this is another research from, uh, BCG, in which they have broken it down further into the SDLC, and then you can see the maturing technology offerings and emerging technology offerings for next years, and then, uh, some of the competitors yet to emerge as part of it. So the acceleration by the Gen AI for the next three to five years has been significant in the SDLC.

And then, uh, if you look at, uh, what Gartner is saying, it is, it has said recently that more than 50% of software engineering leader roles will explicitly require oversight of generative AI by 2025. So that means that the dynamic skills approach, the skills first approach becomes really important, not just for professionals, but also for organizations and economies to align with that set of technological advancement. And that's how, if you look at the hype cycle for emerging technology by Gartner in which it kind of fits into four key categories, the emergent, uh, ai, uh, devex developer experience, pervasive cloud or human centric security and privacy, uh, that's where we will be focusing in this session about building your skill stack accordingly.

And then, uh, if you look at some of the, uh, research by McKensey and company in which developer productivity has really enhanced with the evolution of generative ai. So these, these are all industry backed research in which we are providing more evidence about why gen AI or artificial intelligence is increasing the need of future skills. So, Turing bots is the, uh, term which was coined by Forrester.

And, uh, um, they have just, uh, announced within this year that the maturity of Turing bots, uh, has, uh, increased by five to 10 years, which is the concept of combining AI with DevOps and SDLC. And this is another research in which they are, uh, talking about how developers are being more productive, more happy, uh, with the evolution of generative ai. So the opportunities and the impact is really tremendous when we talk about gen AI and the alignment of future of jobs or future of skills.

So some of the, uh, fact which is being, uh, taught by World Economic Forum and their future of jobs report 2023, in which the work by machine and the work by humans is significantly, uh, will have a change in the next five years. And that is why if we talk about some top skills, uh, which the leaders believe are really essential for the future of jobs. So the future of, uh, new age skills, uh, it is broken down into analytical judgment, uh, flexibility, emotional intelligence, intellectual curiosity, bias detection and handling, and AI delegation.

So prompt engineering training, the large models and so and so forth. But more importantly, if we look at the skills FA first framework, which is again defined by, uh, world Economic Forum, uh, it's not just limited towards the technical skills, but it is also important to have a continuous learning mindset. Uh, these soft skills are equally necessary and to co-develop and co-deliver these skills-based training programs with industry learning providers and government.

And that's where we at Practical Dev, ffc ops are so passionate with our mission to provide that skills first approach, uh, towards aligning the future of jobs with the future of skills. And once we move forward to provide you a more of a template around the key future skillset aligned with the AI advancement. So I've broken it down into, uh, technical and soft skills.

So technical skills, which are really essential to build upon is coding and scripting, uh, cloud computing because, uh, uh, of all the cloud advancement, containerization and orchestration. So cloud native Kubernetes, uh, automation tools, uh, monitoring and logging and version controls. So these are some of the key technical skills which will remain not just relevant, but in demand as part of the key future skillset.

And then we, when we talk about soft skill, it's going to be more collaborative in nature. It is going to be, uh, more problem solving, continuous learning mindset about adaptability, about teamwork, about time management, about security awareness, very important, uh, based on all the risk associated with the AI advancement, uh, leadership creates customer centering approach, ethical consideration, again, a part of responsible AI or AI governance. So these are some of the technical, uh, and soft skills.

And when we talk about future skills with generative ai, so there's definitely a difference between gen AI and artificial intelligence, and when we broke it down between individuals and organizations. So for the individuals, it is all about continuous learning. Uh, it is definitely necessary to build your AI skills, uh, with the prompt engineering training, large language models, uh, and other, uh, set of areas, cross-functional skills.

Again, really important to have a t-shaped or a v-shaped, uh, skillset, uh, stay ethical and more collaborative. And when we talk about from the organization perspective, it is not similar. It has to be a bit different to balance it accordingly, both ways to have, uh, an efficient opt optimized collaboration.

So investing in AI tools, cross-training, data management, security, cultural transformations, metrics and KPIs, training and education pilot projects, monitoring and feedback are the key, uh, aspects from the organizational perspective, which requires a lot of focus to bring that balance between the individuals and the organizational template for the future skills development, uh, uh, side. So that's how, uh, uh, there is a need of developing a multi dimensional approach when we talk about future skills aligned with AI governance and AI regulation. So what does it mean by multi-dimensional approach?

So, uh, what we have defined and try to define is define five key dimensions. So it's not just limited to the technological ecosystem, uh, because at times when we see a lot of advancement toward technology, we tend to see that that is the most important aspect, but rather, I would say it is only one of the key component about aligning your multi-dimensional approach for future skills. So what are the five key dimensions?

So the human aspects. So this is about the cultural transformation, uh, psychological mindset shift, uh, dynamic learning, transformational leadership, uh, happening as work or ways of working, diversity and inclusion. All these areas falls under the human aspects.

And then the process and frameworks. So this is all about the traditional methodologies and frameworks along with, uh, the halo and human system, thinkings, all these false and the process and, uh, frameworks, dimensions, and then the functional composition, which talks about collaboration, portfolio and product management, change management, architecture management, uh, the part of the SDLC that is all false under the function composition. And then the intelligent, uh, automation, that's where the AI advancement becomes really important.

So, which we have already seen within DevOps, within DevSecOps, within AI ops, within value stream management. And then last, but not the least, is the technological ecosystem. So the infrastructures, the containers, cloud natives, APIs, serverless, DevOps, tool chain, open source secrets management, all these falls under technologically ecosystem.

So if we kind of break it down, each of these, uh, uh, dimensions, so all these aspects, which we have talked about in the previous slide, kind of gets bifurcated and segmented into all these domains. And of course, this is not something exhaustive in nature. It'll keep on expanding as the advancement in all these dimensions happens.

But more importantly, having that multidimensional skills development approach is really necessary. So when we align AI and the future skills, so the top concerns related to AI ml, so this is some of the, uh, information which we have segmented from the, uh, GitLab's AI global, uh, DevSecOps AI report in which they have, uh, talk about that AI ML will reduce the number of available jobs, uh, with the highest percentage. So that means the importance of making security as part of the, one of the general skills which will remain relevant across all the segments, whether it is from the developer side or the operations side, or from security in, in itself.

So it'll remain relevant and it'll be more segmented and will be more, become more t-shaped in nature, so that everyone will have at least a general set of security skill stack built upon, irrespective of different roles within the organization. So that's why security becomes one of the key factor, which every individual and organization must focus when we talk about AI governance, uh, AI regulation. So that is how the modern security, uh, practices and skills are really required to be elevated or built upon as part of, uh, your upskilling or re-skilling journey.

And that is how, uh, as part of, for different job roles which are available, even though, uh, within the, uh, technological domain, there is still a lot of restructuring and lots of, uh, uh, lay lay of which are happening, but security remains as, uh, uh, one of the domain which remains as a recession proof, or rather more in demand from the industry perspective. So there's a lot of opportunities, uh, to grab into, to take advantage into if you are able to build your security skill stack aligned with the multidimensional approach, uh, the individuals and organization, uh, support and balance and alignment, which is needed, which we discuss in the previous slide, which will always help you to grow and align with the technological advancement within AI and Gen AI to not just be relevant, but be in demand from the industry perspective. So that is how, uh, we at Practical DevSecOps has been really providing, uh, as a global community, a lot of learning resources career path, which breaks down into all the modern security practices from the DevSecOps to the cloud native, uh, to the threat modeling, to the API security, uh, supply chain security to ensure that the professionals and the community, uh, gain a lot of, uh, skillset, but also align with the developments around AI with the emerging learning platform powered by ai, which we provide in a part of the learning resources.

So feel free to engage with us, uh, as part of the community and engage with other professionals as part of the extended community to build your multidimensional or hybrid skills and align with the future skills roadmap as we grow more towards governance and regulations between organizations, individuals, economies and governance, uh, towards more, uh, positive ecosystem at times ahead. So with that note, uh, that concludes the session for today. And if you have any kind of queries, please feel free to reach out to me on any of the coordinates below.

And once again, it, it is a pleasure to be here, and I hope you keep on enjoying AI in action and another session as part of this virtual event. Thank you so much.