Name: Security Issues and Challenges in Machine Learning
Start: 2022-09-20T12:00:00Z
End: 2022-09-20T12:00:30.000Z
Location: BrightTALK
Rating: 4.5

Data is the foundation of any organization and therefore, it is paramount that it is managed and maintained as a valuable resource. 

Subscribe to this channel to learn best practices and emerging trends in a variety of topics including data governance, analysis, quality management, warehousing, business intelligence, ERP, CRM, big data and more.

Customer retention has never been more important - or challenging. With rising acquisition costs, keeping the customers you already have is paramount, but they're constantly bombarded with competing offers. The key to standing out and fostering loyalty lies in delivering personalized experiences that make them feel valued and understood. Are you truly leveraging your customer data to create these stickier relationships?

The surge of customer data coupled with fragmented digital touchpoints presents a complex challenge. Many companies are overwhelmed - struggling to translate raw data into actionable insights and personalised experiences that resonate with individual customers. Traditional marketing tactics fall short, leading to missed opportunities to build lasting relationships, drive loyalty, and achieve sustainable growth. This is precisely why embracing a connected customer strategy has never been more critical.

Join Anna Samkova, Co-Founder & Strategic Advisor at Albany Advisory, for this session to explore how to unlock exponential growth by turning customer data into personalized experiences with the C.L.I.E.N.T. Growth Framework.

Key Takeaways:

- Implement the C.L.I.E.N.T. Growth Framework to transform raw customer data into a powerful engine for personalization
- Discover proven strategies to activate data in ways that feel personal, purposeful, and commercial
- Gain actionable insights into what truly meaningful personalization looks like in 2025 and beyond – and how to deliver it effectively
- Identify and avoid common personalization pitfalls that can damage customer trust and undermine your ROI
- Understand how to build a connected customer strategy that drives customer lifetime value, loyalty, and sustainable revenue growth

The C.L.I.E.N.T. Growth Framework: Your Roadmap to Personalized CX Success

Architectural innovation has become essential for organizations seeking to balance decentralization with control, or struggle for real-time insights while maintaining robust data governance and compliance.

This session presented by industry thought leader Donald Farmer covers practical strategies for reducing vendor lock-in, optimizing cloud and on-premises workloads, and ensuring data quality at scale, all while leveraging AI to automate and enhance governance.

Attendees will discover how forward-thinking enterprises are reimagining their data architectures to maximize agility, trust, and business impact.

Architectural Innovation for Insight and Governance at Scale

The data and analytics landscape is undergoing a profound transformation. Emerging trends in Gen AI, open table formats, collaborative analytics, and next-generation data platforms are converging to reshape the future of data architecture. In this webinar, we'll explore how:
- Gen AI is revolutionizing data roles and workflows. 
- Open table and open file formats are breaking down data silos, enabling seamless collaboration, and democratizing data access.
- Next-generation data platforms, including data lakehouses on data meshes, are emerging as powerful paradigms for managing distributed, decentralized data ecosystems

We'll delve into the architectural principles, design patterns, and implementation strategies that define these next-gen data platforms. Participants will gain insights into:
- Creating more flexible, scalable, and domain-oriented data infrastructures.
- Adapting to rapidly changing business requirements.
- Harnessing the power of Gen AI, open standards, and collaborative analytics.

Join us as we explore the pivotal trends and technologies transforming data architecture. Discover how to design and implement a unified data architecture that drives business innovation, scalability, and reliability.

Unifying Data Architecture for the Future: Gen AI, Open Standards, Collaborative Analytics, and Next-Gen Data Platforms

As organizations scale, managing data across diverse platforms and environments becomes increasingly complex. A modern data architecture must be flexible, scalable, and integration-ready to support real-time decision-making, AI-driven analytics, and regulatory compliance. In this session, we will explore:
1. The core principles of a resilient data architecture. 
2. Strategies for seamless multi-cloud and hybrid integration.
3. How to balance performance, security, and cost efficiency.
4. Best practices for future-proofing your data ecosystem.

Attendees will gain actionable insights to enhance data agility, optimize integration workflows, and maximize the ROI of their data infrastructure.

Future-Proofing Data Architecture: Strategies for Scalable and Seamless Integration

Learn what it really takes to make substantive AI progress. Tune into this talk to learn the seven tenets of enterprise transformation in the AI era which underscore both the current data situation of many companies and how to leverage this technology for the best results. 

1. Despite the long-standing hype, AI currently only includes natural language processing, not understanding. 
2. To make the best use of the AI, enterprises must build a modern knowledge foundation, i.e., data that’s logically connected, contextualized and extensible with the help of shared, interlinked, symbiotic graph data models. 
3. Organizations can’t be data mature if they’re not in data sharing mode whenever possible or pushing the data sharing envelope. Holding onto data doesn’t make sense if you’re not sharing with the partners who need what that data can provide. 
4. Transformation doesn’t matter if you’re not providing enterprise-wide and even boundary-crossing transparency in the process.
5. It doesn’t make sense to buy “AI products” without having the ability first to feed the AI the best, high quality, relevant, disambiguated data relevant to your business. 
6. Most of the AI software providers out there aren’t focused on helping you solve your data problems.
7. Most enterprises have huge, unresolved data problems. Therefore, most enterprises won’t benefit all that much from AI until they move up the data lifecycle management learning curve.

This talk will explore what effective transformation looks like in the age of AI and provide best practice guidance from organizations in leading industries.

About the Speaker
Alan Morrison is an independent consultant and freelance writer on data tech and enterprise transformation. He has a contributor to Data Science Central and TechTarget with over 35 years of experience as an analyst, researcher, writer, editor and technology trends forecaster, including 20 years in emerging tech R&D at PwC.

7 Tenets of Data and Enterprise Transformation in the AI Era

Digitalization and artificial intelligence (AI) are revolutionizing the process industry by capturing and leveraging tacit knowledge -- insights and expertise that are often unspoken and deeply embedded in the workforce. Through advanced data analytics, machine learning, and AI-driven decision support systems, manufacturers can now extract and codify this implicit knowledge. 

What You Will Discover in this Session:
1. Capturing Tacit Knowledge: Learn how AI can capture and codify tacit knowledge from experienced workers using AI tools, ensuring valuable expertise is retained and utilized.
2. Optimizing Operations: Discover how AI can optimize various aspects of operations, such as predictive maintenance, process optimization, and real-time monitoring, leading to increased efficiency and reduced disruptions and downtime.
3. Case Studies and Best Practices: Study real-world examples and best practices from leading companies that have successfully implemented AI and digitalization strategies.

This workshop, presented by CEO Andreas Eschbach will equip you with the knowledge to leverage digitalization and AI for improving efficiency and competitiveness in the process industry.

Unlocking Human Knowledge: Harnessing AI and Digitalization in the Process Industry

Managing data at scale presents challenges in accuracy, consistency, and accessibility. AI is reshaping this landscape by automating data quality monitoring and enabling natural language interactions for faster insights.

This session explores two AI-driven advancements in data management. AI-powered data quality and anomaly detection addresses inconsistencies by identifying duplicate records, detecting anomalies, and enforcing validation in real time. Natural language querying removes complexity from data access, allowing users to ask questions in plain language and receive structured responses without technical expertise.

Tune into this session to discover how implementing these innovations can reduce manual intervention, improve reliability, and make data-driven decision-making more efficient.

AI-Driven Data Management: Enhancing Accuracy and Accessibility

Web application environments are more complex than ever. Many span multiple clouds, often include containers, are connected via APIs, and delivered via CDNs. While exploits against known vulnerabilities remain common, advanced campaigns use a variety of tactics to compromise web applications. An abundance of tools claiming to address these challenges has left many security teams unclear as to where to focus their attention. On top of this, security teams are worried about how to address increased usage of genAI in development, and attacks that leverage AI. Join John Grady and Melinda Marks as they discuss recent research on the application protection market and modern application security to help security teams solve the challenges they face.

Trends in Application Security and Increasing AI Adoption

Generative AI (GenAI) could contribute $2.6 trillion to $4.4 trillion annually to the global economy according to consulting firm McKinsey, but achieving business goals requires using GenAI in a trustworthy and secure manner. As enterprise business units pour resources to drive innovation, security teams need to avoid compliance violations and data loss. Security for GenAI is a many-layered cake requiring security teams to consider everything from the (sensitive) data informing the model to prompt responses that might result in (sensitive) data exfiltration. Join Todd Thiemann as he discusses recent research on data resilience and data loss prevention in the context of GenAI.

Data Security & GenAI Infrastructure: Facilitating Innovation While Avoiding Compromises

The introduction of AI has created an imperative for the transformation of virtually every business system. This means every aspect of apps and infrastructure are in transition, leaving security pros to react to a massively changing attack surface. How will security teams respond? Where are security leaders focusing first? How will security strategies need to change to respond to this critical transformation? Join Dave Gruber, Principal Analyst, Enterprise Strategy Group as he shares a fresh industry perspective and data from his recent research on this topic.

AI-led Security Operations Strategies: Are You Keeping Up?

Cybersecurity has traditionally focused on identifying, analyzing, and mitigating vulnerabilities. However, as attackers evolve, including utilizing AI, the world of vulnerability management must undergo a massive transformation. This presentation will explore how AI-powered systems can reshape each facet of cyber risk and vulnerability management, improving both defensive capabilities and hacker tactics. This webinar will teach you the innovations driving this new approach, the emerging threats resulting from AI-enhanced attacks, and the future of risk management in an AI-driven world.

The Future of Security Risk - Security Vulnerabilities in the AI Era

As AI technologies rapidly transform enterprise operations, we're witnessing an unprecedented shift in workforce dynamics. Gen Z and Alpha generations are entering a workplace where AI isn't just a tool but a fundamental business driver, requiring a complex blend of technical prowess, adaptability, and ethical decision-making capabilities. While these digital natives possess an intuitive understanding of technology, the challenge lies in channeling this affinity into meaningful business value and innovation.

The stakes are higher than ever for CIOs and technology leaders. Unlike previous technological transitions, AI's impact on workforce development demands a complete reimagining of talent strategies. Organizations must move beyond traditional technical training to foster a unique combination of AI literacy, human-centric skills, and ethical judgment. This requires a transformative approach to talent development that bridges the gap between native digital fluency and the strategic capabilities needed to drive AI-powered innovation and value creation.

In this session, Giles Lindsay (Chief Information Technology Officer, Lif3away Inc.) provides a practical roadmap for CIOs to build and nurture a future-ready workforce that can thrive in an AI-augmented business landscape.

Key Takeaways
- Transform digital natives into strategic AI leaders, combining technical proficiency with business acumen and ethical decision-making capabilities.
- Create effective learning frameworks that balance AI literacy with distinctly human skills like creativity, emotional intelligence, and critical thinking.
- Implement practical approaches for continuous skill development and adaptation, ensuring your workforce remains relevant and innovative in a rapidly evolving AI landscape.
- Integrate ethical considerations and bias awareness into AI talent development, building teams that can responsibly drive AI adoption while maintaining transparency and fairness.

From AI Users to AI Leaders: Cultivating Next-Gen Digital Talent

As AI investments surge across large enterprises everywhere, CIOs are facing a new spin on familiar challenges. The rise of "FOMO AI" (Fear of Missing Out on AI) is driving a renewed focus on issues like business engagement, board influence, team development, and change management. 

Despite these challenges, the wave of technological advancement also presents new career opportunities for CIOs at both the executive management and board levels. CEOs are adding new technology officer positions to their executive committees, and boards are eager for technology representation. 

In this session, Martha Heller, CEO of tech executive search firm Heller, discusses the future of the CIO career and shares unique insights on how to leverage AI to achieve both business and career success.  

Topics for discussion include: 
- How CIOs are educating their boards about AI value
- New technology executive roles on the executive committee
- Evolving career paths for CIOs.
- Levering AI leadership needs for Board Director positions.
- And more.

Meet the Speaker
Martha Heller is CEO of Heller, an executive search firm specializing in technology leadership roles. She has placed hundreds of senior technology executives throughout her career. Martha has authored two books on IT leadership - The CIO Paradox: Battling the Contradictions of IT Leadership and Be the Business: CIOs in the New Era of IT – and is a contributor to CIO.com. Her e-newsletter, The Heller Report, has become a must-read for the industry.

The Impact of AI on the CIO Career

Organizations across industries are adopting AI to boost efficiency, reduce costs, and enhance user experiences in IT service and support. In this webcast, Jeff Rumburg, Managing Partner at MetricNet, will present detailed case studies and benchmarking data that illustrates how AI solutions have delivered measurable improvements—including reduced resolution times, lower operating costs, and higher customer satisfaction. 

Attendees Will Learn: 
- Insights from real-world AI adoption case studies. 
- Strategies and challenges in implementing AI solutions. 
- Proven ROI, cost, quality, and cycle time benefits of AI

AI in Action: Real-World Transformations in IT Service and Support

GenAI is the shiny new object that has executives and techies salivating. Companies have launched thousands of GenAI prototypes, of which only a small fraction have made it into production. The reason? Data deadweight. 

A GenAI model is only as good as its data. Garbage in, garbage out. But given how eloquent GenAI can string together words and/or images, many unsuspecting users may not recognize errors, distortions, and downright hallucinations. 

This session will outline the data risks of GenAI and provide guidelines for how to address them. It will address data bias, data freshness, data access, and data accuracy--among other things--and describe governance techniques, architectural patterns, and tooling that can bolster data inputs and minimize the risks of GenAI to business organizations.

Data Deadweight: How to Overcome Your GenAI Handicap

This session will explore practical applications of AI and machine learning in driving efficiency, improving decision-making, and creating competitive advantages. Attendees will gain insights into real-world case studies, learn how to overcome implementation challenges, and discover strategies to integrate AI into existing business processes effectively.

Key Takeaways:
1. How to identify business processes that can benefit most from AI and machine learning.
2. Strategies to successfully implement AI while addressing common challenges.
3. Lessons learned from real-world applications to accelerate adoption in your organization.
4. Best practices for integrating AI into existing workflows to maximize value.

Transforming Business Operations with AI and Machine Learning

Advanced analytics capabilities like deep learning and NLP have unprecedented potential for business impact -- but only with the right foundation. This session from industry thought leader Donald Farmer will demonstrate how structured governance and model management practices create a "safety net" that empowers data teams to push technical boundaries. 

Through real-world examples of predictive modeling and NLP implementations, learn how leading organizations use robust model lifecycle management, ethical AI frameworks, and systematic testing approaches to accelerate rather than inhibit innovation. 

We will cover some important practical techniques for establishing governance that enables rather than constrains, helping analytics teams confidently explore cutting-edge approaches while maintaining reliability and trust.

Breaking Free Through Boundaries: How Robust Governance of Advanced Analytics Drives Innovation

Data is more than numbers; it tells stories. This webinar explores innovative techniques to humanize data by identifying underlying narratives. Discover how to interpret datasets as storylines, extract characters from data, and organize them into a plot. Perfect for data scientists, analysts, and storytellers, this webinar bridges the gap between technical analysis and creative art, empowering you to craft compelling, data-driven narratives that resonate with diverse audiences and objectives.

Beyond Data Analysis: Extracting Characters and a Plot From Data

The most critical and FORGOTTEN element in advanced analytics: ML, AI and MO

AI and Machine Learning in Data Engineering

The Business Analysis Benefits -- and Limitation -- Of AI and Synthetic Data

The Power of AI Cybersecurity

Analysts vs. Machines: AI/ML for cloud-scale data

Making the Transition from Legacy Systems to Modern Analytics Platforms

Cyber Security Issues and Challenges in AI

Managing Machine Learning Projects

The Economics of Data and Analytics

Graph Foundations for Advanced Analytics and Collaboration

How to overcome your biases and get better results from data exploration

Supply chain planning with AI

Delivering Business Value Through Scaled AI

AI in BI: How to Kickstart Your Adoption Journey with Accenture

AI in BI: How to Kickstart Your Adoption Journey

Machine Learning & Artificial Intelligence

Tune into this webinar to hear University of Washington's Deveeshree Nayak cover the security issues and challenges that may occur due to machine learning software and approaches.

Key Takeaways:
- Security challenges in the machine learning algorithm;
- Best practices to use machine learning products;
- Best practices to implement security practices in using AI/ML software.

Security Issues and Challenges in Machine Learning

Artificial Intelligence

Machine Learning

AI security

Cyber Security

security practices

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