Name: Cyber Security Issues and Challenges in AI
Start: 2022-09-21T12:00:00Z
End: 2022-09-21T12:00:31.000Z
Location: BrightTALK
Rating: 4

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.

Decision intelligence promises to augment human judgment with AI-derived insight, but governance frameworks have not kept pace. When an AI system contributes to a decision, who is accountable for the outcome? How do we trace the information that informed the decision back to its sources? What documentation must exist before the decision is made, and what must be preserved afterward? These questions become urgent as AI moves from analysis into action.

This session presents a governance framework for AI-assisted decisions built on the distinction between lineage and provenance. Lineage documents the flow of data through systems: which sources informed the AI, through what transformations, with what dependencies. Provenance records the history of specific values: who created them, when they were modified, and what parameters governed their treatment. For decision governance, both are necessary. Lineage shows the decision drew on approved sources through documented pipelines; provenance ensures specific values can be traced through a custody chain.

Particular attention is given to retrieval-augmented generation, where decisions may be informed by content retrieved from knowledge bases. Governance must begin at ingestion, documenting sources before they enter the system. Access controls must filter retrieval by permissions. Citation trails must connect generated outputs to retrieved passages and original sources. The session examines what governance can automate and what it cannot.

Key Takeaways:
- Decision governance requires both lineage (system flows) and provenance (value histories).
- RAG systems require governance from ingestion through retrieval to output.
- Accountability cannot be automated; governance must preserve human responsibility.
- Citation trails enable defensible AI decisions.

Governing the Decision: Accountability in AI-Assisted Intelligence

AI governance presents an architectural challenge that centralized approaches cannot solve. Monolithic review processes create friction that development teams bypass; purely decentralized models produce inconsistency that regulators will not accept. The session presents a federated architecture for AI governance: central functions set policies and standards while domain functions retain authority to adapt implementation within defined thresholds for risk, financial impact, and infrastructure cost.

This federated model rests on five components: policies and standards that define acceptable use; governance structures that distribute authority appropriately; risk assessment processes that scale from self-service questionnaires to expert review; technical infrastructure including model registries and automated policy enforcement; and monitoring systems that track drift, accuracy, and fairness over time. Each component must be designed for the distinctive nature of AI: algorithms that learn and change, rather than fixed logic that can be reviewed once and approved indefinitely.

The session traces a maturity progression from experimentation through restricted deployment to production scale, with governance controls calibrated to each stage. Participants will examine how permission models, audit schedules, and oversight intensity can be tiered to match risk levels, avoiding both the paralysis of excessive control and the exposure of insufficient oversight.

Key Takeaways:
- Federated governance balances consistency with domain-appropriate flexibility.
- Five components (policies, structure, risk assessment, infrastructure, monitoring) form a complete architecture.
- Tiered permission and audit models match oversight intensity to risk level.
- Maturity progression from experimentation to production requires different controls at each stage.

Architecting AI Governance: Federated Models That Scale

In the world of business analytics, technical accuracy does not equate business impact. Without the trust of leadership, even the most sophisticated analytics models fail to translate data into action. Data managers must recognize the critical gap is not in the data, but in the narrative they present. 

In this session, Google’s Jiaxi Zhu will introduce a four-part, narrative-driven analytics framework for bridging the divide between analytics and real-world business outcomes. 

Through in-depth examples, he will demonstrate how to:
- Design analytics models with action in mind.
- Frame outputs in a decision-ready context. 
- Shape complex insights into coherent, compelling stories that drive action.

From Insights to Impact: Building Narrative-Enabled Analytics to Drive Executive Decision-Making

In a world where AI, automation, and digital systems are advancing faster than most organizations can keep pace, the real differentiator isn’t the technology itself, it’s the culture behind it. Innovation cannot thrive in environments where teams are overwhelmed, siloed, or unclear about how to adapt. In this session, Theo Harvey reveals how leaders can build a foundation for continuous innovation, enabling teams to navigate uncertainty with confidence and creativity.

Drawing from his two decades as a senior engineering leader at Cisco and his current work as CEO & Co-Founder of SynsorMed—a Google-backed digital health company—Theo shares practical frameworks for cultivating accountability, adaptability, and human-centered innovation. Attendees will learn how to shift from reactive problem-solving to proactive idea generation, fostering a culture where creativity is an everyday practice rather than a special event.

Whether your organization is scaling AI adoption, modernizing information systems, or leading large-scale transformation, this session provides actionable strategies for aligning people, processes, and technology. The result: teams that are more engaged, more empowered, and more equipped to turn complexity into opportunity.

Attendees will learn:
- How to build a culture where innovation is habitual, not hierarchical.
- The connection between accountability, adaptability, and creativity.
- Frameworks for driving progress amid uncertainty and rapid technological change.
- How to inspire teams to turn ideas into implementation with speed and clarity.

Innovation Starts With You: Building a Culture of Creativity in the Age of AI

Healthcare organizations are generating more data than ever before, yet meaningful insights remain out of reach due to siloed systems and fragmented infrastructures. In this session, Anita Nayak, Founder and CEO of ClinDCast, explores how interoperability is transforming healthcare analytics from reactive reporting to real-time intelligence. She’ll reveal how breaking down data silos across EHRs, labs, and payer systems can dramatically enhance decision-making, predictive accuracy, and patient outcomes.

Through real-world examples, Anita will share how ClinDCast has helped healthcare networks unify disparate data sources using HL7 FHIR, API orchestration, and master data mapping frameworks. Attendees will gain a practical understanding of how integrated data models reduce manual reporting, strengthen clinical insights, and enable proactive care delivery.

Participants will leave with actionable strategies to:
- Build interoperable data architectures that scale analytics.
- Overcome technical and organizational barriers to data integration.
- Champion a culture of data ethics, integrity, and shared accountability.

Join this session to learn how interoperability is not just a technical goal—but a strategic pathway to better care, smarter operations, and a truly connected healthcare ecosystem.

Bridging Data Silos: How Interoperability is Shaping the Future of Healthcare Analytics

By 2026, most organizations aren’t struggling with a lack of data — they’re struggling with turning that data into something leaders can actually use. The gap between “collecting data” and “making decisions with it” has never been wider. This session will help you to close that gap.

Tune into this webinar from CEO and industry thought leader Eugina Jordan to learn:
- What’s changing in 2026: the rise of governed AI access, unified metadata layers, predictive correlation engines, and analytics workflows that run continuously instead of once a quarter.
- A clear, step-by-step architecture for building analytics that scale — from data ingestion and quality controls to semantic layers and real-time operational dashboards.
- How to govern AI and analytics together, so insight generation doesn’t outpace your security or compliance posture. RBAC/ABAC integration, auditability, data lineage requirements, and context-aware access policies. 
- How to enable self-service analytics while protecting quality and preventing shadow AI or shadow dashboards.
- How to prove ROI, with simple ways to tie analytics outcomes to operational efficiency, revenue lift, or risk reduction.
- Case studies showing how companies reduced insight lag, increased forecast accuracy, and improved decision velocity by fixing just a few critical parts of their data pipeline.

If you want a session that gives you frameworks you can implement immediately, this is it. You’ll leave with a checklist, a reference architecture, and actionable steps to build a data organization that finally produces insights leaders trust — and use.

2026 Data Insights Playbook: How to Build It, Govern It, and Prove ROI

Organizations are investing more in data than ever, yet many still struggle to translate those assets into clear, actionable insights. In this session, industry thought leader Farid Sheikhi will explore practical strategies to eliminate data chaos, strengthen governance, and build an insights-driven enterprise equipped for 2026 and beyond.

Attendees will learn:
- How to eliminate data chaos by strengthening governance, definitions, and data visibility across the enterprise.
- A practical blueprint for becoming an insights-driven organization, including the maturity stages that matter in 2026.
- How to operationalize analytics and GenAI so insights move beyond dashboards and drive real decision-making.
- Actionable strategies to convert data investments into measurable business impact, including culture, process, and technology enablers.

From Data Chaos to Clarity: Building an Insights-Driven Enterprise in 2026

As AI capabilities expand, a common assumption takes hold: that analytics will gradually be absorbed into automated systems, with human analysts playing a diminishing role. This session challenges that assumption. AI excels at pattern detection, summarization, and prediction at scale; but analytics provides something AI cannot automate. Verification against business context, interpretation for stakeholders, accountability for decisions, and iteration based on feedback remain irreducibly human contributions. Analytics is not being replaced; it is being repositioned as the last mile of insight.

The session examines how the relationship between AI and analytics is shifting. AI handles volume and velocity; analytics supplies judgment and meaning. Organizations that understand this complementarity can design workflows where AI accelerates the routine while analysts focus on the consequential. Those who misunderstand it risk automating the wrong things: optimizing speed while losing the interpretive depth that makes data valuable.

Drawing on research into how AI affects analysts of varying skill levels, the session also addresses a differential effect: experienced analysts often find AI amplifies their creativity and productivity, while less experienced staff may feel threatened or displaced. This has implications for hiring, training, and team design that analytics leaders cannot afford to ignore.

Key Takeaways:
- AI and analytics serve complementary functions; substitution narratives misread the opportunity.
- Verification, interpretation, accountability, and iteration remain human responsibilities
- Experienced analysts benefit disproportionately from AI augmentation; organizations must design for this differential.
- The strategic value of analytics increases, not decreases, as AI capabilities expand.

The Last Mile: Why Analytics Matters More in an AI-Driven World

The customer journey doesn't live in one department. It stretches across marketing, sales, finance, and support — and along the way, manual processes, disconnected tools, and slow handoffs create gaps that frustrate customers and weaken business performance. From invoices that go unpaid, to limited visibility into cash flow, to rising churn, enterprises are feeling the impact of these disconnects in tangible ways that affect the bottom line.

This session cuts through the GenAI hype to focus on practical, safe, and measurable implementation across end-to-end customer process flows. You'll discover methodologies for identifying which problems are worth solving first, how to integrate manual and digital steps into one intelligent workflow, and where human oversight remains essential to prevent costly mistakes. We'll also dig into real-world strategies for managing GenAI's inaccuracies — ensuring invoices are correct, forecasts are reliable, and customer interactions build trust instead of risk.

Join Eugina Jordan, CEO and Co-Founder, YOUnifiedAI, as she reveals proven approaches to GenAI implementation that deliver measurable business outcomes across critical enterprise functions.

- Identify high-ROI opportunities for GenAI implementation across three critical workflows: invoicing, cash flow management, and customer retention
- Implement effective human-in-the-loop protocols that mitigate GenAI hallucinations while maximizing automation benefits
- Design cross-functional workflows that eliminate departmental silos and create seamless customer experiences
- Develop practical metrics to measure GenAI's impact on revenue acceleration, operational efficiency, and customer satisfaction
- Apply a strategic framework for scaling successful GenAI implementations across your organization

Transforming the Customer Journey with GenAI Across the Enterprise

Support teams are at a critical inflection point, with GenAI reshaping customer expectations overnight. While automation promises efficiency, it often creates impersonal experiences that damage loyalty. The most successful organizations are discovering that GenAI's true power lies not in replacing human interactions, but in strategically enhancing them at every touchpoint.

Generative AI offers transformative potential for support experiences, but only when implemented with a human-centered approach. This session defines GenAI specifically within the support context and explores practical applications that benefit both end-users and support agents. You'll discover how intelligent automation can scale operations while simultaneously improving satisfaction metrics across your support ecosystem.

Join Phyllis Drucker (Influencer, EZ2BGR8 Service Management) to explore proven frameworks for implementing GenAI solutions that put people first while driving measurable business outcomes.

Key Takeaways:

- Find the right balance between scaling support operations and maintaining customer satisfaction
- Implement GenAI-powered chatbots that deliver near-human experiences through contextual understanding
- Create customized, tailored responses that increase satisfaction and engagement 
- Establish measurement systems that quantify GenAI's impact and inform future initiatives
Develop a practical roadmap for implementing GenAI in support functions, regardless of your starting point

Human-Centered GenAI Support: Winning Customer Loyalty in the Automation Era

Despite massive investment and executive enthusiasm, most organizations implementing GenAI remain stuck in perpetual pilot purgatory. Behind the headlines and hype, a select group of companies has quietly crossed the chasm from experimentation to enterprise-wide transformation—achieving what their competitors merely discuss in boardrooms. Their secret isn't better technology, but rather a systematic approach to implementation that balances ambition with pragmatism.

GenAI's true power lies in enterprise-wide applications—reimagining customer experiences, supercharging product innovation cycles and transforming operational efficiency across every business function. The question isn't whether to implement GenAI, but how quickly you can transition from experimentation to organization-wide transformation while balancing innovation with responsible governance and ethical considerations.

Join Pankul Chitrav (Application Release Engineer, TD Bank) to discover how your organization can build an actionable GenAI roadmap that delivers immediate business impact while positioning you for sustainable competitive advantage.

Key Takeaways:

- Explore how leading organizations are applying GenAI to create tangible business value across functions
- Learn specific strategies to expedite product development, content creation, and decision-making through GenAI implementation
- Master techniques for streamlining operations while delivering hyper-personalized services at scale
- Gain actionable insights on governance frameworks, ethical considerations, and sustainable GenAI adoption strategies

Unleashing Generative AI: Transform Business Strategy from Concept to Competitive Edge

With AI tools exploding, organizations today are bombarded with new technical tutorials and prompt engineering tactics. But they often become obsolete within weeks. While competitors chase fleeting GenAI techniques, the true competitive advantage lies not in mastering specific tools, but in cultivating something far more fundamental. This presentation reveals the missing catalyst that transforms GenAI from a specialized technology into an organization-wide force multiplier.

The challenge isn't in teaching your teams how to use GenAI—they're already adopting it in their personal lives. It's in creating an environment where innovation flourishes organically across all levels of your organization. Brian Kirsch demonstrates why empowering employees to apply their unique job expertise to GenAI implementation delivers more sustainable results than top-down technical approaches. Learn how to establish the right framework that enables continuous innovation without requiring constant retraining as the technology evolves.

Join Brian Kirsch, IT Instructor and Industry Expert, to discover the one evergreen principle that drives successful GenAI adoption across organizations of all sizes.

Key Takeaways:

- Understand why traditional GenAI implementation approaches fail to deliver lasting competitive advantage
- Learn to empower employees at all levels to become drivers of AI innovation without technical expertise
- Discover a sustainable framework for GenAI adoption that remains effective despite rapid technological changes
- Gain practical strategies for fostering an environment where AI-driven innovation flourishes organically
- See real-world examples of how simple ideas can create profound change across departments

Unlocking the Secret to Sustained Innovation with GenAI

Generative AI is rapidly transforming collaboration by enabling capabilities such as summarization, workflow automation, translation and more. Generative AI offers the potential to fundamentally transform how teams and individuals collaborate and create, eliminating mundane activities and fostering innovation.

In this session, we’ll explore how Generative AI is changing the workplace collaboration landscape, including availability of tools within collaboration applications, third-party tools, and studios enabling custom AI tool creation. We’ll look at organizational strategies and share real-world examples of approaches that achieve measurable success. We’ll also look at security, compliance, and governance concerns to minimize risk.

Key Takeaways:

- Learn how to identify and implement the right Generative AI tools for your team's specific needs, whether that means using built-in features in existing applications, integrating third-party solutions, or building custom tools for unique workflows.
- Understand how to create a foundational strategy for introducing Generative AI into your organization, including understanding which collaborative tasks can be automated to free up time for strategic work.
- Gain knowledge to evaluate and address security, compliance, and governance risks associated with Generative AI, enabling you to implement these tools responsibly and minimize potential liabilities.
- Discover how to measure the success of Generative AI initiatives by measuring before and after improvements resulting from implementation.

Leveraging Generative AI to Improve Collaboration

Storytelling is one of humanity's oldest tools—yet in the corporate world, its power remains largely untapped. Every day, brilliant business ideas die silently, buried beneath technical specifications and corporate jargon. The result? Missed opportunities, disengaged stakeholders, and strategies that never reach their potential. What if you could break this cycle and transform how your ideas are received and implemented?

With GenAI, business leaders can go from dense and technical business language to persuasive narratives that connect and convert. By leveraging AI-powered storytelling techniques, operations leaders, IT professionals and transformation executives can overcome the traditional disconnect between technical teams and business decision-makers, creating alignment that accelerates adoption and drives measurable outcomes. For professionals working in enterprise processes, AI operations, and digital transformation, this capability isn't just nice-to-have—it's essential for ensuring your initiatives receive the right support and resources.

In this session, Inigo Antolin (Head of Data Management and Governance at The British Council) reveals how to harness GenAI to transform your business communication from forgettable presentations into strategic narratives that drive action and results.

Key Takeaways

- How the storytelling gap is undermining enterprise process improvements and technology adoption in data-driven organizations
- Practical frameworks for using GenAI to translate complex technical concepts into compelling business narratives
- Techniques to customize your storytelling approach for different stakeholders across the enterprise
- Ready-to-implement GenAI tools and prompts that enhance communication effectiveness without requiring creative expertise
- Measurable business outcomes achieved by organizations that have mastered GenAI-powered storytelling in process transformation initiatives

GenAI Storytelling: Turning Technical Complexity into Business Impact

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

Sustainability has become a defining element of modern customer experience (CX), shaping how companies build trust, deliver value and foster long-term relationships. Today’s stakeholders expect more than responsiveness and services; they want transparency, environmental accountability and data-driven insights.  

CHEP's transformation from traditional platform provider to data-driven sustainability partner illustrates how organizations can turn environmental commitments into competitive advantages. By embedding circular economy principles into customer touchpoints, companies create dual value: reducing environmental impact while enhancing customer relationships through transparency, efficiency and shared purpose. This approach transforms sustainability from a compliance exercise into a powerful engine for customer loyalty and operational excellence.

Hear from CHEP leaders on how sustainability is reshaping customer experience expectations across industries.

Key Takeaways:

- Insights for embedding sustainability metrics and circular innovation across customer touchpoints 
- How transparent environmental reporting strengthens customer relationships and reduces friction points
- Techniques for leveraging digital systems to enable circular business models and quantify sustainability impact
- Methods for integrating self-service platforms with predictive analytics to deliver more sustainable, responsive service

Accelerate Loyalty Through Circular Innovation in Today's Sustainability Market

Customer conversations contain strategic revelations that most executives never hear, yet these unfiltered moments often predict market shifts before formal data ever could. Although 84% of CX leaders advocate for bringing these insights to the executive table, many organizations still treat customer interactions as operational noise rather than the strategic signal they actually represent. 

Although 48% of companies now employ interaction analytics, many struggle to effectively distribute these insights beyond the contact center. Additional advantages come fromsystematically analyzing customer interactions to inform product development, marketing strategies, and executive decisions; creating a unified approach to customer experience that drives measurable business outcomes and strengthens customer loyalty across the entire journey. 

Join Robin Gareiss, CEO of Metrigy, as she shares research-backed strategies that transform conversation data into business value. 

Key Takeaways: 

- Discover which conversation metrics deliver the most substantial business intelligence and how to capture them effectively 
- Learn practical frameworks for distributing interaction insights across departments to create organization-wide customer-centricity 
- Understand how to quantify the ROI of conversation intelligence through improved business metrics and enhanced customer satisfaction 
- Gain actionable implementation strategies that work regardless of your current analytics maturity

Conversation Intelligence: The Hidden Gems that Impact all Business Decisions

Your customer just spent 15 minutes navigating your support portal only to end up exactly where they didn't want to be: on hold with your call center. This all-too-common scenario reveals the main flaw in how organizations approach digital self-service: designing for scale rather than CX success. 

Unfortunately, customer portals are often designed primarily to eliminate contact via more costly resources. When designed solely to scale support, they fail to meet the CX mark. The best portals humanize the digital experience by understanding the customer's journey and anticipating needs before frustration sets in.

This session examines the critical need to balance operational efficiency with customer-centered design principles that transform portals from necessary evils into preferred channels.

Join Phllis Drucker (Influencer, EZ2BGR8 Service Management) to explore how to achieve great CX results through innovative portal design that puts customers first while still delivering operational benefits.

Key Takeaways:

- Identify common pitfalls that typical portals fall into and how to avoid them
- Apply customer journey mapping techniques to create intuitive, emotionally intelligent digital experiences
- Implement a practical roadmap for designing and innovating a new portal experience
- Measure the true ROI of your portal beyond call deflection metrics
- Balance automation with human touchpoints to create meaningful digital interactions

Humanizing Digital Portals: Transform Self-Service in the Age of CX Expectations

There’s no questioning the massive impact advanced analytics in AI and ML have had on our lives, and from a business perspective, these technologies have made available an unprecedented wealth of information for decision-making.  

This information has evolved our ability to understand what can happen in the future. What hasn’t evolved is our approach to making decisions.  Business decisions are more complex than ever, yet a critical element of intelligent decision-making is overlooked – mathematical optimization (MO) 

This talk will address several key topics on incorporating MO with AI and ML, including: 

Defining MO 
Describing how MO and ML can work together 
MO case studies and success stories 
Facts about MO (e.g. scalability, guaranteed optimality) 
Learning MO and getting started

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

AI/ML-driven data applications are often considered as the final data application -- the target of all data preparation and metadata management activities performed upfront. However, AI/ML can also effectively aid the data aggregation and categorization for search, analytics, and discovery. Data tagging and metadata management can be automated and thereby, enhance the overall data experience for all entities involved in the data process – business or technical. In this session, we'll cover certain examples of AI/ML in data engineering.

About the Speaker
Dr. Pragyansmita Nayak is the Chief Data Scientist at Hitachi Vantara Federal (HVF). She explores the "Art to the Science" engaging with several challenging Data Engineering and Data Science projects in the US Federal Government domain. She has over 22+ years of experience in software development and data science. She holds a Ph.D. in Computational Sciences and Informatics from GMU (Fairfax, VA. She has presented as part of various BrightTalk and other similar technical events in the past. She lives in DC Metro with her husband and enjoys cooking, travel, and photography. For more information on Pragyan's professional experience, please visit her LinkedIn profile at https://www.linkedin.com/in/pragyansmita and Twitter profile at https://twitter.com/SorishaPragyan.

AI and Machine Learning in Data Engineering

Enterprises increasingly deploy machine learning models to analyze corporate data that informs vital business issues. Identifying and gathering high quality data to develop these models is hugely challenging, however, as some information is protected by regulatory or proprietary reasons. Quality data collection is also expensive and time-consuming, leaving huge gaps in AI and machine learning model development. The development of synthetic data and explainable AI is helping fill these gaps by helping train machine learning models or develop new algorithms with insights that are readily available for data scientists.

In this fireside chat with machine learning scientist Vincent Granville, he'll discuss:
o Synthetic data design techniques, and how to identify business processes where most useful;
o How to test the quality of synthetic data;
o The benefits, and potential detriments, of explainable AI;
o Common modern enterprise data issues, such as managing unbalanced, inconsistent, small, outdated, and unstructured data;
o Ways to address data leakage, as well as small, wide and unobserved data;
o How synthetic data, explainable AI and other modern technologies are used to overcome these issues.

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

Tune into this webinar to hear University of Washington's Deveeshree Nayak discuss the role of AI in cyber security and the how AI can be applied to address present cyber attacks.

Key Takeaways:
- How AI can be used in cybersecurity;
- Does cyber security have components of AI?
- Examples of cybersecurity in AI.

The Power of AI Cybersecurity

Why do customers churn? Why is revenue down? What are my top channels to acquire new customers? Why is my business losing more orders in rural areas?

The answer is in your data.

As we move forward in modernizing the traditional data stack, companies are collecting more and more data.  Getting through all the data is a growing challenge.  Operationalized ML powered analytics can make analysts more efficient and eliminate rote data science work. Data teams need the ability to rapidly process cloud-scale data, separating signal from noise with pre-built and operationalized AI/ML tools.

Join this webinar to learn the basics of how to:

-Rapidly sift through data (in seconds)
-Determine key drivers of metric change
-Leverage artificial intelligence and machine learning in your data stack

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

For over 40 plus years, organizations around the world have used the language of SAS to build assets such as revenue forecast models, customer retention models, analyze risk at an enterprise leveland perform compound discovery.

As analytical technology has continued to evolve and develop, organizations are increasingly seeking to include modern programing languages such as Python and R into their model building and to understand how they can gain value from and utilize the speed and agility of The Cloud.

Today, organizations have choices with respect to the language of SAS deployment models.Join us for this 30-minute webinar where we will be discussing:

•What a transition away from legacy systems looks like and the benefits it can offer in terms of resource elasticity and scaling up the numbers of models produced compared to traditional models.
•How you can easily include modern programming languages into your models for swift deployment, removing frustrating devops blocks.
•How to leverage the decades of investment in legacy systems whilst incorporating the best of what modern analytics systems can offer and continue utilize the resources ideally suited for your organizations modeling building process.

Making the Transition from Legacy Systems to Modern Analytics Platforms

Sometimes people manage machine learning projects as if they were software development projects. Sometimes they use approaches that have evolved and "just work for them." And sometimes they don't manage them at all! But the community "in the know" has come up with approaches and techniques that work to remove risk from ML projects and create high quality outcomes without burning out valuable teams in the process. 

I've written this all up in the book Managing Machine Learning Projects: From Design to Deployment (Manning, 2022). In this talk, I'll give an overview of the methodology and the motivations behind it. Attendees will learn:
- How to get a project started; 
- What the team needs to focus on up front;
- Where the risks come from; 
- How to handle the model development process; 
- Why evaluation is fundamental to success; 
- How and why to integrate ethics into the process;
- What needs to be considered when integrating ML into applications.

Managing Machine Learning Projects

Data is widely considered the most valuable asset in the world; that data is no longer just the exhaust of the business, but more and more is becoming the business. If data is the new oil in driving the economic growth of the 21st century, then how effective is your organization at leveraging data and analytics to derive and drive new sources of business and operational value? 

Data and analytics are unique economic assets -- assets not only never wear out, but actually can appreciate, not depreciate, in value the more valuable they are used. Give that, tune into this webinar session to discover how your organization can embrace a systematic approach to build out your data and analytic economic assets.

The Economics of Data and Analytics

Intelligence should make sense across the general, the specific and the in-between. Instead, we've got general rule-based thinking in one set of silos, and narrow probabilistic thinking in another.

A knowledge graph foundation for analytics and collaboration provides the means for solving two problems:

1) How to eliminate siloing, and
2) How to ensure quality, contextualized data at scale.

With the knowledge graph approach, we're not just looking for your keys under the lamppost because that's where the light is. Instead, the light is in more places. In other words, there's less observational bias and more explicit context.

The result is a shared advanced analytics environment we can have confidence in. 
 
This talk will discuss practical examples of knowledge graph foundations and how they're used today in analytics for ecommerce, healthcare, and scientific research. It will also lay out a roadmap to artificial general intelligence through more math, more data storytelling, and more actionable insight in the graph.

Bio
Alan Morrison is an independent consultant and freelance writer on data tech and enterprise transformation. He is a contributor to Data Science Central 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.

Graph Foundations for Advanced Analytics and Collaboration

When we think of AI we tend to think of predictive models and supervised learning. But AI can be applied to the exploration and understanding of our data right from the start, leading to better business decisions. Virtualitics Co-head of AI, Aakash Indurkhya, sits down with author and data scientist Tobias Zwingmann to talk about how hypothesis-driven exploration is leaving valuable insight on the table, and setting you up for underwhelming AI. 

Join us as we discuss how to use AI to explore data—we like to call it Intelligent Exploration—and how it can impact your business.

You will learn:
How to discover high-impact insight that you’re overlooking today
How to determine where you should direct your AI resources by uncovering good use cases and assessing the feasibility of projects
How to remove bias and limits from your exploration to find impactful opportunities

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

From the rapid rise of ecommerce, to labor shortages, to geopolitical  and macro economic tensions - CPG & Retail supply chains are being impacted in a myriad of ways. Join us as we share how companies have solved key inventory planning challenges by leveraging AI driven demand forecasting to bring agile, smarter decision making to daily supply chain operations. 

Hear how o9 integrated Google Cloud’s Vertex AI Forecast into their Digital Brain solution to solve forecast accuracy problems and ultimately reduce risk, improving financial performance. The Vertex AI Forecast offering, when integrated into the o9 Digital Brain’s open architecture framework, providing a more comprehensive supply chain solution.The end result helps ensure that demand will be met without running the risk of having excess and obsolete inventory.

Supply chain planning with AI

The AI market in its current state is rapidly transforming, but not fully transformed. Organizations are shifting from AI Experimentation to AI at Scale, but very few organizations have been able to do so successfully to become completely AI-fueled. 

These AI-fueled organizations have been able to move AI out of a few use cases in an R&D setting and into a company philosophy, scaling AI across the entire organization. They have discovered how AI and Machine Learning (ML) can be used, developed roadmaps, defined strategies around Machine Learning Operations (MLOPs), and implemented ethics governance to realize evergreen business value. 

Join Deloitte Consulting's Rohit Tandon (ReadyAI Practice Leader) and Jatin Dave (ReadyAI Senior Manager), to learn how organizations can accelerate their AI journey and achieve scale with speed.

Delivering Business Value Through Scaled AI

Theoretically, AI in BI should allow all users within an organization to discern, easy to understand actionable insights that help in attaining business outcomes. Often, lack of definition, standards, defined metrics, alignment to business outcomes, paucity of skilled resources and budgets, poor implementation and hesitancy to adopt AI are among some of the top reasons for failure of AI in projects that showed tremendous promise but did not yield good business outcomes. This discussion explores some critical questions every business must ask before adopting AI and a step-by-step approach for implementing AI.

Some key areas of exploration during this session: 
• Why is AI all around us and what is AI in BI?
• What are the benefits of AI in BI?
• What does it mean to infuse AI in BI? 
• How does one successfully implement AI in BI? 
• How do you drive AI adoption in your business?
• What are some key steps to realize the power of AI in BI?

This session breaks down some of the complexity of AI in BI, where business can gain deeper insight into their data, using intuitive exploration tools, visualizations, dashboards and other easy-to-comprehend metrics and present a more compelling case to their clients, engaging them through concise, explainable data.

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

AI in BI: How to Kickstart Your Adoption Journey

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

Machine Learning & Artificial Intelligence

Tune into this webinar to hear AI security issues and the best approaches to apply while using AI-powered products in daily life. 

Key Takeaways:
- Security challenges in AI;
- Best practices to use AI products;
- Best practices to implement security practices.

Cyber Security Issues and Challenges in AI

Artificial Intelligence

Machine Learning

AI tools

machine learning products

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Enterprise Applications

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Business Intelligence and Analytics

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Big Data and Data Management

As an IT professional, many of the problems you face are multifaceted, complex and don’t lend themselves to simple solutions. The information technology community features useful and free information technology resources. Join to browse thousands of videos and webinars on ITIL best practices, IT security strategy and more presented by leading CTOs, CIOs and other technology experts.

Cyber Security Issues and Challenges in AI

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Business Intelligence and Analytics