AWS for Software Companies Podcast

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------Benjamin Flast, Director, Product Management at MongoDB discusses vector search capabilities, integration with AWS Bedrock, and its transformative role in enabling scalable, efficient, and AI-powered solutions.Topics Include:Introduction to MongoDB's vector search and AWS BedrockCore concepts of vectors and embeddings explainedHigh-dimensional space and vector similarity overviewEmbedding model use in vector creationImportance of distance functions in vector relationsVector search uses k-nearest neighbor algorithmEuclidean, Cosine, and Dot Product similarity functionsApplications for different similarity functions discussedLarge language models and vector search explainedIntroduction to retrieval-augmented generation (RAG)Combining external data with LLMs in RAGMongoDB's document model for flexible data storageMongoDB Atlas platform capabilities overviewUnified interface for MongoDB document modelApproximate nearest neighbor search for efficiencyVector indexing in MongoDB for fast queryingSearch nodes for scalable vector search processingMongoDB AI integrations with third-party librariesSemantic caching for efficient response retrievalMongoDB's private link support on AWS BedrockFuture potential of vector search and RAG applicationsExample use case: Metaphor Data's data catalogExample use case: Okta's conversational interfaceExample use case: Delivery Hero product recommendationsFinal takeaways on MongoDB Atlas vector searchParticipants:Benjamin Flast - Director, Product Management, MongoDBSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------David Gildea of Druva shares their approach to building cost-effective, fast generative AI applications, focusing on cybersecurity, data protection, and the innovative use of LLMs for simplified, natural language threat detection.Topics Include:Introduction by Dave Gildea, VP of Product at Druva.Focus on building generative AI applications.Emphasis on cost and speed optimization.Mention of Amazon's Matt Wood keynote.AI experience with kids using "Party Rock."Prediction: GenAI as future workplace standard.Overview of Druva's data security platform.Three key Druva components: protection, response, and compliance.Druva's autonomous, rapid, and guaranteed recovery.Benefits of Druva’s 100% SaaS platform.Handling 7 billion backups annually.Managing 450 petabytes across 20 global regions.Druva’s high NPS score of 89.Introduction to Dru Investigate AI platform.Generative AI for cybersecurity and threat analysis.Support for backup and security admins.Simplified cybersecurity threat detection.AI-based natural language query interpretation.Historical analogy with Charles Babbage’s steam engine."Fail upwards" model for LLM optimization.Using small models first, escalating to larger ones.API security and customer data protection.Amazon Bedrock and security guardrails.Testing LLMs with Amazon’s new prompt evaluation tool.Speculation on $100 billion future model costs.Session wrap upParticipants:·        David Gildea - VP Product Generative AI, GM of CloudRanger, DruvaSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------Urmila Kukreja of Smartsheet and Nick Simha of AWS discuss leveraging Amazon Q’s Retrieval-Augmented Generation (RAG) solution to enhance productivity by enabling employees to quickly access relevant information within secure, integrated workflows like Slack, improving efficiency across the organization.Topics Include:Introduction by Nick Simha, AWS.Overview of Amazon Q’s role in data analytics and Gen AI.Gen AI’s impact on productivity, ~30% improvement backed by Gartner study findings.General productivity improvement seen across various departments.Amazon Q’s developer code generation tool – rapid developmentGen AI and LLMs’ challenges: security, privacy, and data relevance.Foundation models lack specific organizational knowledge by default.Empowering Gen AI to grant system access can cause issuesPrivacy concern: Sensitive data, like credit card info, can be central in data breachesCompliance is critical for organizational reputation and data integrity.Data integration techniques: prompt engineering, RAG, fine-tuning, custom training.RAG (Retrieval Augmented Generation) balances cost and accuracy effectively.Implementing RAG requires complex, resource-heavy integration steps.Amazon Q simplifies RAG integration with "RAG as a service."Amazon Q’s Gen AI stack overview, including Bedrock and model flexibility.Amazon Q connects to 40+ applications, including Salesforce and ServiceNow.Amazon Q respects existing security rules and data privacy constraints.Plugin functionality enables backend actions directly from Amazon Q.All configurations and permissions can be managed by administrators.Urmila Kukreja from Smartsheet explains real-world Q implementation.Smartsheet’s Ask Us Engineering Slack channel: origin of Q integration.Q integration in Slack simplifies data access and user workflow."Ask Me" Slack bot lets employees query databases instantly.Adoption across departments is high due to integrated workflow.Future plans include adding data sources and personalized response features.Session wrap upParticipants:Urmila Kukreja – Director of Product Management, SmartsheetNick Simha - Solutions Architecture Leader - Data, Analytics, GenAI and Emerging ISVs, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------Harold Rivas – Chief Information Security Officer at Trellix, discusses the role of generative AI in cybersecurity, focusing on Trellix's adoption of AI for threat detection and model governance, while emphasizing the importance of privacy, responsible innovation, and cross-functional collaboration.Topics Include:Introduction to generative AI and its impact on cybersecurityHarold’s background in financial services and cybersecurity rolesTrellix’s focus on product feedback through the Customer Zero ProgramOverview of machine learning's role in anomaly detection at TrellixDevelopment of guided investigations to assist security operations teamsGenerative AI's growing importance in cybersecurity at TrellixLaunch of Trellix WISE at the RSA Conference in 2024Addressing the overload of security alerts with AI modelsIntegration of various AI models like Mistral and AnthropicReducing anomalies and workload for security operations teamsImportance of privacy in generative AI adoption and data governanceChallenges with GDPR and CPRA regulations in AI implementationFocus on privacy frameworks like the NIST Privacy FrameworkNeed for multi-stakeholder involvement in AI governanceDiscussion on model governance inspired by financial services practicesImportance of inventorying and testing AI models for securityBenefits of an AI Center of Excellence (AICOE) within organizationsModel governance in generative AI for regulatory and business outcomesThe impact of AI on labor, jobs, and decision-making processesAddressing cyber risk and threat modeling in AI environmentsThe double-edged sword of AI in offensive and defensive cybersecurityMITRE Atlas framework's role in AI-driven cybersecurity strategiesPotential negative consequences. Auto dealership hacked – Chevy Tahoe sold for $1Importance of vulnerability management and developer trainingEvolution of AI security tools and responsible use of generative AICollaboration, governance, and agility in AI adoption across organizationsQ&A 1: Outcomes and responsibilities an generative AI COE should have?Q&A 2: Model governance and financial implicationsQ&A 3: CISO response to model development, compliance and learning with customer dataQ&A 4: Thoughts and suggestions for rating systems for modelsQ&A 5: Selecting and evaluating modelsQ&A 6: Advice and experience for model deployment and technical controlsQ&A 7: Human reviewing AI responses to ensure accuracyQ&A 8: Will AI help avoid major outages in the future?Q&A 9: How to test and see maturity of models?Session wrap upParticipants:·        Harold Rivas – CISO at TrellixSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------Executive leaders from Arctic Wolf, Docker and Illumio share insights on fostering a strong security culture, balancing innovation with security, and addressing challenges in data protection and AI model development.Topics Include:Overview of security culture in different company teamsImportance of guidelines and secure IT infrastructure for AI modelsChallenges of accessing customer data while maintaining securityNeed for anonymization in early AI model developmentDocker's open-source ecosystem and security integrationDogfooding own products to ensure product reliability and trustworthinessIllumio’s high customer trust and responsibility for strong security practicesBalancing security awareness with development speed at IllumioGamifying security training to increase awarenessInterlocking with customers to enhance security understanding for developersEmbedding security into the development process from the startIllumio's approach to security in agile, cloud-native developmentAdapting customer success strategies for evolving security needsRise of non-developers using AI in enterprisesEducating business leaders on security best practicesScaling customer enablement and education through community engagementChallenges of placing security responsibilities in the developer workflowArctic Wolf’s AI strategy for secure developmentUse of anonymized data in secure AI model trainingGenerative AI’s potential to augment human creativity and efficiencyPanelists' views on private AI and segmented model developmentMeasuring security culture progress with gamification and development metricsAddressing human factors in cybersecurity and social engineering threatsEmphasizing resiliency and containment in preventing widespread cyberattacks.Participants:Dean Teffer – Vice President of Artificial Intelligence, Arctic WolfDixie Dunn – VP of Customer Success, DockerMario Espinoza – Chief Product Officer, IllumioBrian Shadpour – General Manager, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------Hear the generative AI journeys of Cohere, Epiq, and Forcura, including their market assessments, use case prioritization, responses to ethical and security considerations, while discussing generative AI's impact on healthcare, legal industries, and business applications.Topics Include:Panel Introductions by David CristiniWhere is Focura at in their AI journeySummary of Epiq’s AI journey to dateCohere’s AI journey to dateWhere did each company begin and assessing the market opportunitiesPrioritizing of use cases for EpiqFocura’s quick focus and results with generative AISimplifying healthcare and improving patient experience with generative AIHow do experiments and proof of concepts develop into production?Indicators that Cohere uses to identify customers ready to move fastUsecases that allows Forcura customers to move forwardGuidance on engaging the Executive Team – getting Executive alignmentHow are legal and healthcare customers responding to AI solutions and challengesChanges of priority from customer advisory panelsEvolving questions and concerns of functionality and dataSome customers reporting AI evaluation is slowing them downUsecases that are easier to start off with to gain trust and tractionDealing with AI concerns of ethics, security and privacy – managing objectionsUnderstanding ethics concerns – privacy can often be about where data residesCustomers often want “traceability”Accuracy and reducing hallucinations – AI comes with risk, business have to decide on business riskFuture facing – what are we excited about?Generative AI is excellent at translation services – ROI is excellentBusiness applications and social impact of generative AIParticipants:MaryAnn Wofford – VP of Sales, CoherePaul O'Hagan - Senior Director Product Management – AI Platform, EpiqAnnie Mueller Erstling – COO, ForcuraDavid Cristini - Director, ISV Sales North America, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV-------J.B. Brown, VP of Engineering at Smartsheet, shares how integrating Amazon Q with Smartsheet's flexible work management platform has streamlined productivity and enhanced employee support through AI-driven automation.Topics Include:Introduction by J.B. Brown, VP of Engineering at Smartsheet.Story about improving productivityContext about Smartsheet as an enterprise-scale work management platform.Examples of Smartsheet use in healthcare, TV streaming, and small businesses.Focus on not changing how companies work, offering flexibility.Integration with popular enterprise tech stack tools like Okta and Slack.Automations in Smartsheet for notifications and data synchronization.Smartsheet’s customer base includes large enterprises and small businesses.Overview of Smartsheet’s scale: 15 million users and $1 billion revenue.Smartsheet’s employee support system, including 270+ "Ask Us" Slack channels.Mention of AWS and the introduction of Amazon Q Business.Building a Smartsheet Q Business app for streamlined employee support.Setting up an Amazon Q Business app with proprietary data sources.Implementation of Slack integration for Smartsheet employee support.Example of AI summarizing Slack threads for improved efficiency.Demo of Amazon Q Business outperforming human experts in knowledge retrieval.Emphasizing the value of reducing response time and decision-making delays.Future development plans: Smartsheet-Amazon Q connector.Using AI to interrogate and manage Smartsheet project data.Invitation to AI-minded Smartsheet customers to test the new connector.Participants:J.B. Brown - VP of Engineering at SmartsheetSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

AWS's Shahid Mohammed and Darktrace's Michael Beck discuss how generative AI innovations are transforming cybersecurity by both enhancing defences and introducing new, sophisticated threat management strategies.Topics Include:Shahid Mohammed introduces himself as a lead solution architect at AWS.Mike Beck is Global Chief Information Security Officer at Darktrace.Darktrace specializes in AI-driven cybersecurity solutions for digital environments.Darktrace secures multiple digital data pots: email, network, cloud, SaaS, and endpoint.The conversation focuses on innovation in cybersecurity through AI.Mike emphasizes the benefits of Gen AI despite its security risks.Gen AI enables more complex, targeted attacks against organizations.Attackers use Gen AI to tailor attacks through phishing and deepfakes.Gen AI increases phishing complexity by eliminating common detection cues.Data privacy risks arise when large models process sensitive business data.Businesses must be mindful of AI’s impact on data sovereignty and security.Shahid compares the cybersecurity space to an arms race due to Gen AI.Mike stresses the importance of choosing the right AI for each task.Darktrace uses unsupervised machine learning and Gen AI together for defense.AI is essential for scaling cybersecurity efforts given today's threat complexity.Darktrace relies on AWS cloud for compute power, scaling, and innovation.AWS infrastructure helps accelerate Darktrace's R&D and operations securely.Security leaders should implement Gen AI policies and training.Mike advises technical controls and monitoring for safe Gen AI use.Gen AI is here to stay, but businesses must handle its security implications carefully.Participants:Michael Beck – Global CISO - DarktraceShahid Mohammed – Solution Architect Manager – Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Daryl Martis of Salesforce and Rashna Chadha of AWS share how Amazon Bedrock integrates with Salesforce to enhance AI applications, highlighting the partnership's strategic benefits, AI model customization, and secure deployment options.Topics Include:Introduction to Amazon Bedrock and Salesforce partnership.Overview of Amazon Bedrock as an API-based generative AI service.The strategic collaboration between AWS and Salesforce.Salesforce Data Cloud and its integration with Amazon Bedrock.Overview of Salesforce Einstein and its use of Amazon SageMaker.Recent AI launches between Salesforce and AWS, including Slack AI and MuleSoft.Use cases of AI services like Amazon Textract within Salesforce.Bringing Your Own Large Language Model (BYO LLM) with Bedrock.Foundation models offered by Amazon Bedrock (Anthropic, Cohere, Llama).Overview of security, privacy, and compliance in Bedrock AI services.Salesforce Data Cloud’s unified customer data and real-time AI capabilities.Bedrock’s support for custom AI model evaluation and metrics.Consumption models in Bedrock: on-demand vs. provision throughput.Bedrock’s agent capabilities for real-world applications like scheduling.Demo of using Amazon Bedrock models within Salesforce.Participants:Daryl Martis – Director of Product Management, Einstein AI - SalesforceRashna Chadha – AI/ML Specialist – Principal Solution Architect – Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Ashish Arora, Head of Engineering & Machine Learning, Product Analytics at Autodesk, shares how personalized AI-powered experiences and real-time data can drive product adoption, featuring insights into Autodesk’s transformation journey, leveraging machine learning, and delivering actionable recommendations to millions of users.Topics Include:Real-time data description and examplesLeveraging AWS for real time and generative AI servicesAutodesk’s journey leveraging AWS to transform architecture and deliver personalized insightsOverview of Autodesk and product portfolioUtilizing data gathered for customersPersonalized data-driven insights for customers on Autocad and other productsDescriptive insights: providing usage data for customersPrescriptive insights: Making recommendations to customers based on their workflowsPredictive insights: Using ML to recommend products and featuresAutodesk processes 100+ billion events across all products, delivered 350+ million insights to customers, served to 3.5 million customersExample walkthrough – RachaelArchitecture of Autodesk data and insight processLeveraging LLMs – Sagemaker and BedrockBringing it altogetherSession wrap upParticipants:Ashish Arora – Head of Engineering & ML, Product Analytics - AutodeskBrian Slater – Principal Solutions Architect – Amazon Web Services

A panel discussion on presenting on security and data to Boards of Directors, focusing on metrics, ROI, generative AI, securing intellectual property, and strategic board engagement.Topics Include:What security metrics are most valuable for Board of DirectorsArticulating ROI of security programQuantifying benefits of security programSales enablement of the security teamDriving efficiencies within the business with generative AI and moreQuantifying business and template-based reporting to Boards with Diligent softwareLeveraging consultants and 3rd parties to leverage messages to Board of DirectorsManaging and communicating data posture and risk managementWorking with data leadership, securing intellectual propertyChallenges of labelling dataEducating boards on software architecture principles and generative AIHigh focus and techniques for securing IPEnrolling Boards with generative AI use cases and innovationCreating communities of excellenceGamifying security and generative AI to increase internal knowledgeQ&A 1: Taking action on a summarized documentQ&A 2: Mental model for evaluative SaaS partnersQ&A 3: Using the board to influence budgetary decisionsSession wrap upParticipants:Satheesh Ravala - Chief Technology Officer, DiligentJosh Blackwelder – Deputy CISO, Sentinel OneRobert Huber - Chief Security Officer, Head of Tenable Research, President Tenable PublicKaren Henlsey - Principal WW Data & Generative AI Strategist, AWS

Lacework’s VP and Head of Engineering Arash Nikkar, and Product and Security Software Engineer Teddy Reed, discuss their work with generative AI and Amazon Bedrock, focusing on threat detection, chat interface improvements, and the rapid development of AI-driven solutions.Topics Include:Introducing Lacework with Arash NikkarWorking with AWS and BedrockFocusing on threat detectionLacework ingests over a trillion events each dayComposite alerts for detecting anomaliesDeveloping and improving the chat interfaceTeddy Reed talking chat interface improvementsReviewing the architectureFast engagement with generative AI / AWS BedrockThe rapid delivery with Bedrock – took 20% of expected timeBest practice – build in time to review and verify responsesMeasuring customer’s engagement and return frequency and feedbackUnderstanding customer sentiment and topic analysisAchieving 60% completed results and feedbackTransparency of source, responseEnabling assistant to have more access to data from LLMUsing AWS’ model evaluator, raising the accuracy scoresMoving everything over to Bedrock for multiple reasonsFamiliarity of AWS tools helpful for all development and security teamsAlways challenging assumptions – is chat interface the right interface?Q&A 1: Using vector database for analysis, using results as system promptQ&A 2: Approaching cost-optimization and balancing ROIQ&A 3: Using as automated remediation for findingsQ&A 4: Data source Bedrock in leveragingSession wrap upParticipants:Arash Nikkar – VP, Head of Engineering – LaceworkTeddy Reed – Product and Security Software Engineer - Lacework

Massimo Ghislandi from AWS interviews Frank Contrepois, Chief Innovation Officer of Strategic Blue and Dvir Mizrahi, Head of FinOps for WIX sharing perspectives and best practices for FinOps, implementation, challenges and future FinOps trends for software companies.Topics Include:FinOps is a new approach to managing cloud finances, bridging the gap between finance, engineering, and business stakeholdersThe implementation of FinOps can take a top-down or bottom-up approach, requiring collaboration and a common language across teamsShifting the conversation from "costs" to "investment" and "efficiency" has been an effective strategy for driving cultural changeFocusing on automation, governance, remediation, and anomaly detection can have a greater impact than just cost observabilityEducating finance teams on technical cloud concepts and vice versa is crucial for building trust and alignmentThe future of FinOps may involve expanding beyond just cloud to encompass all IT assets and aligning with a company's overall values and business objectivesPotential changes in legislation or accounting practices related to cloud could drastically impact the FinOps landscapeParticipants:Frank Contrepois – Chief Innovation Officer, Strategic BlueDvir Mizrahi – Head of Financial Engineering (FinOps), WIXMassimo Ghislandi – ISV Marketing Leader, EMEA, AWS

We feature a panel of executives from ASAPP, Glean, Smarsh, Socotra and AWS sharing essential strategies, including the role of Generative AI, trust-building, and addressing legal challenges to secure executive buy-in for transformative initiatives.Topics Include:Introductions to panelExample of ideal Executive SellerDepth of business acumen and problem solving are important skills for Executive SellersFraming a customer problem and create the argumentMaking selling to a C-Suite a team sportKeeping sellers and produce C-suite relevantRevolution and renaissance positioningTrust with the C-suite – do salesperson have a texting relationship w decision makers?How is Generative AI driving customer’s satisfaction and dissatisfaction?Data is the new oilDefining the business problem for insurance industryHelping customers reimagine business with Generative AI80% of customers report the chatbot makes them madThe data journey is the precursor to the Generative AI journeyThe top unique legal challenges with Generative AILegal now brought into early stage of sales processTurning legal concerns into an opportunityThe book “Never Split the Difference” by Christopher VossCalls to action from each panellistSession wrap-upParticipants:Daniel Rood – Senior Vice President Marketing, ASAPPAJ Tennant – Vice President Sales & Success, GleanNeva DePalma – General Council, SmarshEkine Akuiyibo – Chief Business Officer, SocotraLauren Larscheid – Sr. Sales Leader, Business Applications, AWS

Today Anthropic’s Zach Witten takes us on a deep dive into Anthropic’s cutting-edge AI models—Claude Haiku, Sonnet, and Opus—exploring their safety-first approach to generative AI and sharing essential tips for prompt engineering.Topics Include:Introductions, about Anthropic3 models: Haiku, Sonnet and OpusScaling laws for hardware, data and computeCompeting to be safest AI solutions, safety-first organizationLeader in jailbreak resistanceInterpretability features and breakthroughs for AI modelsBasics of prompt engineeringImproving prompts with ClaudeDetails matter – small changes to spelling, context will greatly improve resultsSystem prompt – role setting will improve results (i.e. “You are an expert mathematician…” for math queryBe clear and direct – use XML tags where possibleEncourage Claude to think step-by-step – answering fast comes with accuracy riskUse examples to provide additional clarity to ClaudeBonus tips for image-based prompt engineeringQ&A 1) Who wrote the meta-prompts in the cookbook?Q&A 2) Guidance for writing prompts for prompt generatorQ&A 3) Best practices for tabular and structured dataQ&A 4) Maintaining “tone” across hundreds/thousands of responsesQ&A 5) Reverse engineering a prompt

Today, Dr. Ratinder Paul Singh Ahuja of Pure Storage takes us through how his organization is leveraging Amazon Bedrock to enrich the digital experience for end users and increase returns for the company.Topics Include:IntroductionsGenerative AI activity within Pure StorageOwnership and expertise of team, role of generative AIRapid changes require dedicated team reporting to office of CTOWhere Pure Storage is investing – Productivity gains, Customer Experience, R&DVision starts with the needs of the business usersGenAI Ops – Content, Voctor databases for ingestion, query strategies & securityExample – Purchase Acquisition ProcessFeatures for a mobile experienceConfigurations and logs are getting more enhanced with generative AIDefining schema and improving large language model for more accurate responsesSecurity models and deploymentSummary and session wrap upQ&A 1) How did the team get trained to use the generative AI tools and systems?Q&A 2) How did the team reduce the hallucinations?Q&A 3) How did you decide on which foundation models to use?Q&A 4) The ROI of this particular use caseQ&A 5) Models working with poly-morphic evolving inputsSession conclusion

Federico Torreti and Tom Sly of AWS discuss how application developers can leverage Generative AI to enhance cross-app experiences, minimize custom integrations and increase the value-add of enhanced communication features.Topics Include:Federico Torreti intro: What is AWS’ vision for applicationsFocus of Machine Learning & AIAbility to build rapid prototypes, experimentationCambrian explosion of SaaS applicationsThe challenge of toggling of multiple applicationsBringing the world of disconnected appsData and focusing on the primary value for customerGenerating more content will raise number of alerts and potential of burnoutTom Sly intro: Being delighted by businessesCustomers expect personalization from businessesMore than 150,000 customers leverage AWS for communications strategyLeveraging messaging to increase customer delight2 examples: restaurant and medical needSession wrap upParticipants:Federico Torreti – Head of Product – Amazon Q, Amazon Web ServicesTom Sly - Director, Special Projects, Amazon Web Services

Sara Yamase, Partner at Simon-Kucher & Partners, Head of Software, Internet and Media Practice shares guidance and industry examples on generative AI pricing and GTM strategies.Topics Include:What percentage of companies are developing Gen AI features for customersProjected returns on Gen AI investmentArticulating the value is 1st step in building pricing and monetization strategyAlmost half of VC funding in last 5 quarters were for AI solutionsGetting through the hype cycle and trough of disillusionmentEducation, Marketing, Food industry examples of companies clarifying value of generative AI to customersHaving strong data sets for identifying trendsPricing tenets for generative AIIndirect monetization – more usage, faster, greater productivity, etcStandalone Generative AI software with bespoke pricingAbility to scale pricing per customer usageSubscription selling for generative AI softwareChoosing pricing model for generative AI solutionsSummaries and session wrap up

Today Nicole Eagan, Chief Strategy and AI Officer and Michael Beck, Global CISO of Darktrace AI Research centre share experience, predictions and guidance on the future of AI and Cybersecurity.Topics Include:A brief history of DarktraceApplying AI to the challenge of cyber securityChanges and evolution of cyber security threatsWhat are the key security challenges business are facing?Generative AI provided a seismic shift with threat actorsBest practices for managing and scaling security teamsGuidance for leaders for baking AI into workflowsResponsible AI best practices for building trustTrends of Generative AI on the security landscapeLeveraging Generative AI to transform the customer experiencePredictions for emerging technology trends over the next 10 yearsRecommendations and parting wisdom for security and AIKeeping a workforce trained and relevant for technology trendsInterview wrap upParticipants:Nicole Eagan – Chief Strategy Officer and AI Officer, DarktraceMichael Beck – Global CISO, DarktracePhil Le-Brun – Director, Enterprise Strategy, Amazon Web Services

A panel discussion with executives from ActionIQ, FloQast and Wiz on experiences and best practices for working and selling to customer C-suites.Topics Include:Panel introductionsKeeping products and sellers focused on C-Suite outcomesCustomer C-suite focus, identifying internal championsKeeping sales motion concise for key C-SuiteConnecting value for enterprise spaceImpact of Generative AI and inventing on behalf of customersCustomer impressions of GenAI and going beyond the hypeHelping customers reimagine / hack their businessC-suite concerns about Generative AIFlipping objections into selling points for C-SuiteBuilding confidence for security, data and GenAI for ExecutivesFinal thoughts, Session wrap-upParticipants:Justin DeBrabant – Chief Product Officer, ActionIQStefan van Duyvendijk – Accounting Operations Evangelist, FloQastRick Kickert – Senior Vice President of Revenue Growth & Strategy, WizVeronica McCarthy – Senior Sales Manager, ISV, Amazon Web Services