Women in AI experts will provide a unique perspective on why it is important to start with the data for algorithms to be ethical, be it for new transformations or for enterprise transitions towards the Generative AI era. Data governance along with regulatory audits are not sufficient anymore, and the multifaceted responsibilities towards an accessible and inclusive future for all humans requires a comprehensive data strategy. The panelists will share industry perspectives from finance and sustainability that emphasize privacy, security, synthetic data, and maturity scores to scale ethical models.

While everyone's talking about AI, far fewer have deployed it successfully or turned technology into business outcomes. This panel changes that, bringing together Generative AI experts for a deep dive into the practical application of generative AI. From building new customer offerings to refreshing internal processes, the panellists will reflect on the importance of data quality, data security, the responsible use of data as well as change management when it comes to embedding generative AI into the business strategy.

They see your every move! You know it’s true: Your data is everywhere, the fingerprints of your behavior and movements captured by all manner of apps and devices. Does privacy still exist? The short answer is no, but striving toward it will help mitigate the many risks of inappropriate data access, and that’s really what “privacy” is all about these days! Check out this Fireside Chat with Data Warrior Kent Graziano and DM Radio Host Eric Kavanagh to learn more! They’ll talk about practical methods for designing and managing data governance programs, security protocols and most importantly: responsible behavior!

At Airbnb Payments, integrating AI with blockchain can transform our services by enhancing transaction efficiency, security, and personalization. AI analyzes blockchain data, identifying potential financial anomalies and reconciling transactions immediately. 

It also optimizes blockchain operations, increasing transaction speed and reducing AWS usage.

Perhaps most importantly, AI also supports predictive analysis in for our users, creating more personalized experiences using blockchain's secure data. AI's analysis of individual preferences on blockchain enables personalized payments advice, such as how to price a listing or what payment methods to utilize. 

Overall, this integration leads to more efficient, secure, and tailored financial services, revolutionizing the industry with data-driven decision-making.

The majority of AI risk discussion has been about how to safeguard privacy and address algorithmic bias. Less discussed – but equally important – is how to manage AI cybersecurity risk in today’s regulatory environment, where technology far outpaces regulation.

At present, laws and regulations provide for only general security requirements (i.e., they don’t necessarily account for the unique cybersecurity risks posed by AI). Given the slow pace at which new laws and regulations are adopted, it is likely that regulators will stretch existing requirements to cover all things algorithmic. 

Companies, therefore, need to be ready to defend their AI-related security practices now. They can start doing that by adopting an AI risk management approach that encompasses not only physical and cyber security measures but also the procedural and personnel aspects of such measures.

In this session, Proskauer's Michelle Ovanesian and Marc Palmer offer a view from the frontlines of data security, privacy, and the law in the rapidly expanding field of artificial intelligence.

Data is an asset, and the best organizations know what they own. But identifying, defining, and classifying your critical data is only the first step in compliance; knowing what you own doesn't mean it's safe.

In this session, Imperva's Terry Ray looks at data security, and how to incorporate infosec attitudes into compliance and governance. Attendees will learn how to distinguish regulation from security, and leave with practical tools and questions they can use to complete their secure compliance journey.

This presentation covers the critical transition from traditional analytics to the strategic integration of AI into business processes, from the compelling reasons for adoption to the practical implementation steps. The 'What' segment introduces the foundational elements enabling the AI revolution and the business processes ripe for disruption. Addressing the 'Why', we highlight the responsible adoption of AI with accurate, reliable data as the backbone of any AI-driven initiative. This section underscores the need for solid data benchmarks and testing to measure AI's effectiveness, to ensure that AI implementations lead to tangible, positive outcomes, and to alert leaders to issues early. The 'How' section provides actionable insights on implementing AI to scale data-driven decisions effectively. With real-life examples, this section covers best practices for data management, technology integration, and strategies for fostering a culture that embraces data and AI. Attendees will learn about scaling AI-driven analytics, including considerations for data security, privacy, and ethical AI use.

When data is the most valuable asset of your company, protecting it is a non-negotiable. While Information Security professionals are focused on Bad Actors, we have data operations and data governance professionals focused on Bad Data… Are they one and the same? What’s similar and what’s different between the worlds of data integrity and data security?

Drawing from a wealth of experience and real-world challenges, Gorkem will shed light on the pivotal role of data quality in the forefront of information security. We’ll discuss opportunities for early detection, auto-detection, and the establishment of tiered rules to manage and remediate bad data effectively. Learn how proactive governance and observability can transform data management from a reactive stance to a formidable defense mechanism, ensuring the integrity and security of your data ecosystem. 

In today's data-driven world, organizations face the challenge of not only harnessing the power of data but also ensuring its responsible and effective use. This panel discussion will delve into the critical components of embedding data governance and data literacy into the fabric of organizational culture. Data governance forms the foundation of a robust data strategy, encompassing policies, processes, and frameworks to ensure data quality, integrity, and security. However, effective governance requires more than just frameworks; it necessitates a cultural shift where data stewardship is ingrained into every aspect of organizational operations. Moreover, data literacy is paramount in enabling individuals across an organization to effectively interpret, analyze, and derive insights from data. By cultivating a culture of data literacy, organizations empower employees to make informed decisions, driving innovation and growth. This panel will explore strategies for fostering a culture of accountability, collaboration, and trust around data practices driving sustainable success in today's dynamic business landscape.

Our efforts to obtain – and maintain – a little privacy in our lives have been a subject of concern and conversation in the United States since 1890. A famous article that year, titled “The Right to Privacy,” called out how new technology and business processes threatened what is also called “our right to be let alone.” The concerns and conversations have evolved since then to also encompass the right to privacy over how the data about our personal lives is collected and used. 

Today, collecting and using our personal data is ubiquitous. We are recorded by dashcams and surveillance cameras for safety and security. We use our biometric data to conveniently open phones and enter buildings. We provide intimate details of our lives so connected devices can help us stock refrigerators, brush our teeth, drive cars and more. This session will provide a quick overview of data privacy, with tips for governing the use of personal data in order to comply with laws and meet expectations. 

This session explores data fabric and data mesh architectures and the common need for a Data Center of Excellence, including standardized practices, resources, compliance policies and security standards. By establishing a DCoE, organizations can navigate the complexities of data compliance and drive business growth in today’s competitive landscape.

The Federal Government's approach to AI Policy, Governance, and Security is evolving to address this dynamic landscape of artificial intelligence and the explosion of Large Language Models. The goal is to develop a framework with the aim of reducing risk to people and the planet in the development, procurement and use of AI and the comprehensive strategies employed to formulate and implement policies ensuring responsible AI use. Emphasizing robust governance frameworks, organizations navigate ethical considerations, privacy concerns, and potential biases. Security protocols are paramount, safeguarding against cyber threats and ensuring the integrity of AI systems. Leveraging this multifaceted approach will help to foster innovation while prioritizing transparency, accountability, and the public interest. The Federal Government's commitment to shaping a secure and ethical AI landscape underscores its pivotal role in technological evolution.

GenAI can look deceptively easy when it comes to showing a cool demo, but can prove incredibly hard to productionalize. This session will cover the challenges behind industrializing GenAI applications in the enterprise, and the approaches engineers are taking to meet these challenges. Attendees will get to take a look under the hood to see how Data Engineering and Integration techniques can help us go from simple demos to production grade applications with consistently high quality results.  

We will explore how Retrieval Augmented Generation (RAG) workflows go from naive to advanced. Techniques discussed will cover a typical GenAI application flow with topics including multiple and hybrid models, refined data processing, data security, getting transparency in results, combining structured and unstructured data, and putting it all together to get high performance and cost effective outcomes. Attendees will leave the session with a framework to understand proposed solutions from their teams and ask the right questions to test if a solution can become industrial-grade.

This session explores the technological advancements of analytics, data science, machine learning, AI, and quantum computing. Beginning with an overview of the historical development and current state of these technologies. LexisNexis' Flavio Villanustre will explore how analytics has transformed industries; how data science extracts valuable insights, how machine learning has moved from simple rule-based systems to today's deep learning models; and how quantum computing promises to solve previously intractable problems at speeds unattainable by classical computers.

Attendees will learn about the benefits and associated risks of each technology: Increases in efficiency, productivity, and innovation juxtaposed against vulnerabilities in privacy, security, bias, and wider societal concerns such as unemployment. These competing issues underscore the need for responsible, ethical research and deployment of such tools.