AWS for Software Companies Podcast

Box's Chief Product Officer Diego Dugatkin discusses how the enterprise content management platform is leveraging AI through partnerships with AWS Bedrock and continuing to innovate for their customers.Topics Include:Introduction of Diego Dugatkin as Box's Chief Product OfficerBox provides cloud content management for enterprise customersFocus on Intelligent Content ManagementBox serves 115,000 customers including 70% of Fortune 500Company manages approximately one exabyte of enterprise dataBox expanding product portfolio to offer more customer valuePartnership with AWS Bedrock for AI implementation announcedCollaboration with Anthropic for LLM technology integrationBox offers neutral approach letting customers choose preferred LLMsCommon misconceptions about generative AI capabilities and limitationsGenerative AI helps accelerate contract analysis and classification processesBox Hubs enables content curation and multi-document queriesSuccess measured through hub creation and query accuracy metricsLong-term AWS partnership continues expanding with new technologiesAmazon is major Box customer while Box uses AWSAPI integration important for third-party developer implementationsAI development exceeding speed expectations in efficiency improvementsChallenges remain in defining AI agent roles and capabilitiesContent strategy crucial for deploying intelligent content managementCompanies must prepare for AI agents in workplaceFlexibility in tech stack recommended over single-vendor approachNext 12-24 months will see accelerated industry changesBox maintains innovative culture through intrapreneurship approachCompany regularly hosts internal and external hackathonsFocus on maintaining integrated platform while acquiring companiesPartnership between Box and AWS continues growing strongerParticipants:Diego Dugatkin – Chief Product Officer, BoxSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Through case studies of Graviton implementation and GPU integration, Justin Fitzhugh, Snowflake’s VP of Engineering, demonstrates how cloud-native architecture combined with strategic partnerships can drive technical innovation and build business value.Topics Include:Cloud engineering and AWS partnershipTraditional databases had fixed hardware ratios for compute/storageSnowflake built cloud-native with separated storage and computeCompany has never owned physical infrastructureApplications must be cloud-optimized to leverage elastic scalingSnowflake uses credit system for customer billingCredits loosely based on compute resources providedCompany maintains cloud-agnostic approach across providersInitially aimed for identical pricing across cloud providersNow allows price variation while maintaining consistent experienceConsumption-based revenue model ties to actual usagePerformance improvements can actually decrease revenueCompany tracked ARM's move to data centersInitially skeptical of Graviton performance claimsPorting to ARM required complete pipeline reconstructionDiscovered floating point rounding differences between architecturesAmazon partnership crucial for library optimizationGraviton migration took two years instead of oneAchieved 25% performance gain with 20% cost reductionTeam requested thousands of GPUs within two monthsGPU infrastructure was new territory for SnowflakeNeeded flexible pricing for uncertain future needsSigned three to five-year contracts with flexibilityTeam pivoted from building to fine-tuning modelsPartnership allowed adaptation to business changesEmphasizes importance of leveraging provider expertiseRecommends early engagement with cloud providersBuild relationships before infrastructure needs ariseMaintain personal connections with provider executivesParticipants:Justin Fitzhugh – VP of Engineering, SnowflakeSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

In this AWS panel discussion, Naveen Rao, VP of AI of Databricks and Vijay Karunamurthy, Field CTO of Scale AI share practical insights on implementing generative AI in enterprises, leveraging private data effectively, and building reliable production systems.Topics Include:Sherry Marcus introduces panel discussion on generative AI adoptionScale AI helps make AI models more reliableDatabricks focuses on customizing AI with company dataCompanies often stressed about where to start with AIBoard-level pressure driving many enterprise AI initiativesStart by defining specific goals and success metricsBuild evaluations first before implementing AI solutionsAvoid rushing into demos without proper planningEnterprise data vastly exceeds public training data volumeCustomer support histories valuable for AI trainingModels learning to anticipate customer follow-up questionsProduction concerns: cost, latency, and accuracy trade-offsGood telemetry crucial for diagnosing AI application issuesSpeed matters more for prose, accuracy for legal documentsCost becomes important once systems begin scaling upOrganizations struggle with poor quality existing dataPrivacy crucial when leveraging internal business dataRole-based access control essential for regulated industriesAI can help locate relevant data across legacy systemsModels need organizational awareness to find data effectivelyPrivate data behind firewalls most valuable for AICustomization gives competitive advantage over generic modelsCurrent AI models primarily do flexible data recallNext few years: focus on deriving business valueFuture developments in causal inference expected post-5 yearsComplex multi-agent systems becoming more importantScale AI developing "humanity's last exam" evaluation metricDiscussion of responsibility and liability in AI decisionsCompanies must stand behind their AI system outputsExisting compliance frameworks can be adapted for AIParticipants:Naveen Rao – VP of AI, DatabricksVijay Karunamurthy – Field CTO, Scale AISherry Marcus Ph.D. - Director, Applied Science, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Suresh Vasudevan, CEO of Sysdig, discusses the evolving challenges of cloud security incident response and the need for new approaches to mitigate organizational risk.Topics Include:Cybersecurity regulations mandate incident response reporting.Challenges of cloud breach detection and response.Complex cloud attack patterns: reconnaissance, lateral movement, exploit.Rapid exploitation - minutes vs. days for on-prem.Importance of runtime, identity, and control plane monitoring.Limitations of EDR and SIEM tools for cloud.Coordinated incident response across security, DevOps, executives.Criticality of pre-defined incident response plans.Increased CISO personal liability risk and mitigation.Documenting security team's diligence to demonstrate due care.Establishing strong partnerships with legal and audit teams.Covering defensive steps in internal communications.Sysdig's cloud-native security approach and Falco project.Balancing prevention, detection, and response capabilities.Integrating security tooling with customer workflows and SOCs.Providing 24/7 monitoring and rapid response services.Correlating workload, identity, and control plane activities.Detecting unusual reconnaissance and lateral movement behaviors.Daisy-chaining events to identify potential compromise chains.Tracking historical identity activity patterns for anomaly detection.Aligning security with business impact assessment and reporting.Adapting SOC team skills for cloud-native environments.Resource and disruption cost concerns for cloud agents.Importance of "do no harm" philosophy for response.Enhancing existing security data sources with cloud context.Challenges of post-incident forensics vs. real-time response.Bridging security, DevOps, and executive domains.Establishing pre-approved incident response stakeholder roles.Maintaining documentation to demonstrate proper investigation.Evolving CISO role and personal liability considerations.Proactive management of cyber risk at board level.Developing strong general counsel and audit relationships.Transparency in internal communications to avoid discovery risks.Security teams as business partners, not just technicians.Sysdig's cloud security expertise and open-source contributions.Participants:·        Suresh Vasudevan – CEO, SysdigSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

From hard-coded credentials to boardroom buy-in, join four tech security leaders from Clumio, Mongo DB, Symphony and AWS, as they unpack how building the right security culture can be your organization's strongest defense against cyber threats.Topics Include:Security culture is crucial for managing organizational cyber riskGood culture enables quick decision-making without constant expert consultationMany security incidents occur from well-meaning people getting dupedPanel includes leaders from AWS, Symphony, MongoDB, and ClumioMeasuring security culture requires both quantitative and qualitative metricsBoard-level engagement indicates organizational security culture maturitySelf-reporting of security incidents shows positive cultural developmentSecurity committees' participation helps measure cultural engagementHard-coded credentials remain persistent problem across organizationsInternal audits and risk committees strengthen security governancePublic security incidents change board conversations about prioritiesLeadership vulnerability and transparency help build trustBeing pragmatic beats emotional responses in security leadershipSecurity programs should align with business revenue goalsCustomer security requirements drive program improvementsExcessive security questionnaires drain resources from actual securitySecurity culture started as exclusionary, evolved toward collaborationFinancial institutions often create unnecessary compliance burdenEarly security involvement in product development prevents delaysSecurity teams must match development team speedTrust between security and development teams enables efficiencySmall security teams can support large enterprise requirementsVendor partnerships help scale security capabilitiesProcess changes work better than adding security toolsSecurity leaders need deep business knowledgeTechnical depth and breadth remain essential skillsEvangelism capability critical for security leadership successInfluencing without authority key for security effectivenessCrisis moments create opportunities for security improvementSocializing between security and development teams builds trustDEF CON attendance helps developers understand security perspectiveBug bounty programs provide continuous security feedbackRegular informal meetings between teams improve collaborationBuilding personal relationships improves security outcomesModern security leadership requires balance of IQ and EQParticipants:Jacob Berry – Head of Information Security, ClumioGeorge Gerchow – Interim CISO, Head of Trust, Mongo DBBrad Levy – Chief Executive Officer, SymphonyBrendan Staveley – Global Sales Leader, Security Services, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

AWS executive Giancarlo Casella explains how organizations can navigate global privacy regulations and achieve compliant international expansion using AWS's privacy reference architecture.Topics Include:Welcome to executive forum on security and Gen AIIntroduction of Giancarlo Casella from AWS Security Assurance ServicesAWS helps organizations with compliance and audit readinessGlobal expansion requires understanding local privacy lawsGermany and France interpret GDPR differentlyGermany has Federal Data Protection Act (BDSG)France focuses on consumer privacy through CENILRisk of non-compliance includes fines and reputation damagePrivacy laws existed in only 10 countries in 2000EU Privacy Directive of 1990 was prominentBy 2010, forty countries had privacy lawsHIPAA and GLBA introduced in United StatesNow over 150 countries have privacy regulations75% of world population under privacy laws soonRegulations are vague and open to interpretationGDPR example: encryption requirements lack specificityNeed right stakeholders for privacy complianceLegal team must lead privacy interpretationEngineering implements technical privacy aspectsRisk and compliance teams coordinate evidence gatheringData Protection Officer oversees entire programCIO, CTO, CISO alignment creates strong foundationSecurity transforms from bureaucratic to revenue enablerAWS develops cloud-specific privacy reference architectureIndustry standards provide guidance frameworksAWS privacy reference architecture focuses on cloud specificsData minimization and individual autonomy are keyCase study: Middle Eastern AI company expands to CanadaCompany used CCTV at gas stationsCreated privacy baseline and roadmapData flow documentation essential for complianceContinuous compliance strategy helps enable successAligning stakeholders across different organizational linesFuture of US federal privacy regulation discussedDiscussion of responsible AI usage requirementsParticipants:Giancarlo Casella - Head of Business Development and Growth Strategies, AWS Security Assurance ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Haggai Polak – Chief Product Officer, Securonix and a veteran cybersecurity expert examines how artificial intelligence, quantum computing, and resource constraints are fundamentally transforming the threat landscape for security leadersTopics Include:AI transformation of cybersecurity landscape from past tactical focusCISO accountability and regulatory pressures increasing significantlyAttack surface expanding beyond traditional network boundariesQuantum computing threatens current cryptographic protectionsDefenders remain understaffed and outmatched against sophisticated threatsSecuronix leads SIEM/SOAR space with 1000+ global customersWorld Economic Forum identifies misinformation/disinformation as major crisisAI benefits attackers more than defenders currentlySmall/medium enterprises falling below cyber poverty lineAI enables faster, more sophisticated malware developmentDeepfakes caused $25M loss in Hong Kong CFO impersonationDigital tsunami: broadband, IoT, cloud everywhere expanding attack surface50+ democracies face election security challenges in 2024Cloud intrusions increased 75% between 2022-2023Quantum-resistant cryptography transition needed within 10 yearsSEC regulations require specific cybersecurity incident disclosure guidelines4 million unfilled cybersecurity positions globallyCybercrime-as-a-Service growing, estimated $1.6B annual revenue81% of organizations faced ransomware attacks in 2023Insider threats increasing with remote work adoption30,000+ vulnerabilities published last year, half critical/highMean time to exploit now 44 daysSecuronix Eon leverages AI to increase analyst efficiencyDark web selling corporate credentials for $10,000Balance needed between protection and detection/response investmentsParticipants:Haggai Polak – Chief Product Officer, SecuronixSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Dr. Yanbing Li, Chief Product Officer at Datadog, outlines how the company has integrated AI and automation into its incident response framework, helping customers manage both traditional security challenges and emerging AI-specific risks.Topics Include:Introduced talk about incident response and CISO liabilityDatadog founded 14 years ago for cloud-based developmentPlatform unifies observability and security for cloud applicationsCurrent environment has too many fragmented security productsSEC requires material incident reporting within four daysDatadog's incident response automates Slack room creationResponse team includes Legal, Security, Engineering, and ProductSystem tracks non-material incidents to identify concerning patternsReal-time telemetry data drives incident management automationOn-call capabilities manage escalation workflowsDatadog uses own products internally for incident responseCompany focuses on reducing time to incident detectionAI brings new risks: hallucination, data leaks, design exploitationBits.ai launched as LLM-based incident management co-pilotTool synthesizes events and generates incident summariesBits.ai suggests code remediation and creates synthetic testsSecurity built into AI products from initial designPrompt injection prevented through structured validation approachSensitive data anonymized before LLM processingEngineering and security teams collaborate closely on AILLM observability becoming critical for production deploymentsCustomers need monitoring for hallucinations and token usageDatadog extends infrastructure monitoring into security naturallyCompany maintains strong partnership with AWSQ&A covered Bits.ai proactive capabilities and enterprise differentiationParticipants:Yanbing Li – Chief Product Officer - DatadogSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Sanjay Kalra of Zscaler and Randy DeFauw of Amazon Web Services explore the hidden dangers of generative AI security—from invisible text manipulation and deep fakes to data poisoning and dark AI models—while offering practical strategies for protecting your enterprise in this era of generative AI.Topics Include:AI security threats grouped into data, malicious use, trust/safetyData security critical for SaaS-based AI servicesModel training data vulnerable to poisoning and manipulationGenAI lacks traditional data deletion capabilitiesAccess controls difficult once data becomes model embeddingsPrompt injection attacks becoming widespread, with libraries available onlineDeepfake scams increasing in sophistication and frequencyAI enhancing phishing attacks with better written contentDark AI models emerging specifically for malicious purposesModel hallucinations being exploited for security attacksAI accelerating analysis of stolen dataShadow AI usage by employees poses security risksExisting vendor AI integration creating unexpected security challengesFine-grained access controls essential for AI applicationsPII protection critical in both inputs and outputsComprehensive prompt and response logging necessaryInvisible text manipulation emerging in resumes and RFPsModel fine-tuning can compromise built-in security guardrailsMulti-language inputs create new security considerationsCompetition-sensitive content requires careful AI managementAI firewalls needed for input/output monitoringRegular security testing required for AI modelsAI compliance standards emerging globallyMulti-modal AI creating new security challengesBrowser isolation helping control AI application usageParticipants:Sanjay Kalra – Product Management at ZscalerRandy DeFauw – Senior Principal Solutions 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/

This illuminating conversation with CyberArk's SVP of Finance, Nili Serr-Reuven, reveals how the 25-year-old cybersecurity leader successfully transformed from a traditional software company to a SaaS business model in just five quarters - far faster than the industry standard of 2-2.5 years - while maintaining strong margins and customer trust throughout the transition.Topics Include:Introduction to SaaS transformation challenges and opportunities.Tomaz Perc introduces Nili Serr Reuven from CyberArk.Overview of CyberArk's 25-year history and milestones.Transition from a perpetual model to SaaS.CyberArk's accelerated transformation in just five quarters.Challenges of shifting from product-centric to customer-centric.Importance of market research and peer consultations.Key role of cross-functional collaboration in success.Explanation of "swallowing the fish" in SaaS.Managing short-term revenue drops during SaaS transformation.CyberArk's 70% SaaS revenue share post-transformation.Impact of global economic challenges on business strategy.CyberArk's robust demand for identity security solutions.Strategic leadership's role in transformation execution.CyberArk's disciplined financial planning during uncertainty.Establishing KPIs like ARR and customer satisfaction.Managing rising cloud costs with FinOps practices.CyberArk's approach to pricing and packaging SaaS solutions.Leveraging acquisitions to speed up SaaS capabilities.Impact of transformation on CyberArk's finance department.Evolution of finance roles to support SaaS growth.Communication with investors during transformative periods.The importance of cultural shifts in transformation success.Continuous learning, transparency, and collaboration as cornerstones.Advice for future SaaS leaders: plan, communicate, adapt.Participants:Nili Serr Reuven – SVP Corporate Finance, CyberArkTomaz Perc – SaaS Business Lead, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Soumya Banerjee, Associate Partner at McKinsey and Company, shares a comprehensive data-driven exploration of how generative AI is transforming the cybersecurity landscape, revealing emerging threats, organizational challenges, and strategic opportunities for security professionals.Topics Include:AI's transformative potential in cybersecuritySurvey of 500 cybersecurity professionalsGenerative AI's impact on security landscapeRising sophistication of phishing attacksThreat actors leveraging generative AIDeepfake technologies circumventing biometric controlsCybersecurity companies' valuation and growthPlatform versus point solution debatesExpanding cybersecurity attack surfacesCloud security emerging as top priorityAI use cases in threat detectionGenerative AI risks for organizationsSecuring AI investments and budgetsData protection and sensitive information challengesRegulatory scrutiny of AI technologiesTalent gaps in cybersecurity sectorEvolving cyber insurance risk modelsIdentity and access management trendsAPI and machine identity securityLLM prompt and data protectionEnterprise strategies for AI adoptionEmerging technologies for cybersecurity defensePartnerships between cybersecurity vendorsDisclosure risks in generative AIFuture of cybersecurity technology landscapeParticipants:·        Soumya Banerjee – Associate Partner at McKinsey and CompanySee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Brendan Ittelson, Chief Ecosystem Officer of Zoom and Fedrico Torreti of AWS share how Zoom and AWS are leveraging generative AI to revolutionize application development, enhance cross-app personalization, and streamline user experiences with intelligent communication tools.Topics Include:Introduction of speakers and session overview.Generative AI's disruptive impact across industries.Reimagining customer experiences with generative AI.Driving productivity through AI-powered applications.Challenges faced by application developers with AI integration.Importance of AI as a collaborator, not replacement.Cross-functional workplace complexity with multiple apps.Reducing task redundancy via generative AI automation.Case study: AI accelerating creative project briefings.Business outcomes achieved through thoughtful AI implementation.McKinsey and Gartner projections on generative AI's potential.Top use cases: R&D, customer operations, sales, marketing.Bridging data silos for richer user experiences.Security and compliance challenges in AI implementations.Zoom's federated model for adaptable AI architecture.Meeting summaries powered by Zoom AI Companion.Expanding generative AI into chat, whiteboards, voicemails.Vision for AI amplifying, simplifying, and delegating tasks.Integrating external data for personalized user experiences.Open platform approach for seamless data exchange.AI Companion empowering users with actionable insights.Role of AWS in enabling AI-first solutions.Addressing notification overload with smarter AI design.Enhancing end-to-end workflows with unified AI tools.Encouragement for developers to embrace thoughtful AI adoption.Participants:Brendan Ittelson - Chief Ecosystem Officer, ZoomFedrico Torreti - Head of Product, AppFabric, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Jonathan Shulman, Senior Partner at McKinsey & Company, highlights the transformative potential of AI in the software industry and the evolutions needed to capture emerging market opportunities.Topics Include:AI's transformative potential in the software industry.Why AI is a massive business opportunity.Software industry evolution: Mainframe to Cloud SaaS eras.Potential entrance into a new AI-driven era.AI spend forecast: $15B to $200B by 2026.Most AI spend repurposed from existing IT budgets.Legacy software spend likely shifting towards AI.Importance of targeting specific, high-impact AI use cases.Key areas disrupted: sales, marketing, software engineering.AI's adoption rates vary by industry and function.Four waves of AI: predictive to agent-based.Most companies are still in early AI stages.Prioritize building agentic, end-to-end AI solutions.Winning companies invest disproportionately in AI innovation.Position offerings to tap into AI-specific budgets.Deliver complete workflows, not isolated point solutions.Generative AI accelerates development and iteration cycles.Scaling AI pilots remains a major industry challenge.Tool fragmentation undermines productivity and innovation.Change management critical for successful AI integration.Rethinking team roles and processes for AI deployment.Consumption-based pricing models gaining industry traction.Shift from perpetual to subscription to consumption models.Balancing value-driven and cost-efficient consumption pricing.AI market poised to redefine IT and business landscapes.Participants:Jonathan Shulman – Senior Partner, McKinsey and CompanySee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

AWS's Miguel Álava and BoardWave's Phill Robinson explore how European software companies can overcome market fragmentation, leverage cloud and generative AI, and adopt global strategies to scale competitively.Topics Include:Introduction of Miguel Álava and Phill RobinsonOverview of BoardWave's mission and communityChallenges for European software companies in scaling globallyComparison of US and European software market dynamicsCloud adoption benefits for European software scalabilityModern licensing models to centralize operations in EuropeImpact of fragmented European markets on growthCOVID-19's effect on sales strategies and efficiencySelling enterprise software across Europe via cloud toolsRole of centralized marketing in global competitivenessAdvantages of targeting the US before EMEA expansionBoardWave's "Voyager" model for scaling internationallyImportance of solving universal versus local market problemsUsing cloud infrastructure to penetrate diverse marketsGenerative AI's role in product innovation and scalingGenerative AI's transformational impact on global software industryEuropean software companies' opportunity to lead in AIBuilding a collaborative European ecosystem for innovationLessons from Silicon Valley's collaborative success modelBoardWave's mentoring programs for European software CEOsAWS's support for cloud adoption and business scalingThe evolving role of Chief Product Officers (CPOs)AI's potential to enhance cross-market product functionalityStrategic next steps for scaling European software businessesVision for Europe as a software superpower by 2034Participants:Phill Robinson – Founder, BoardwaveMiguel Alava – EMEA ISV General 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/

Richard Borstein of RingCentral and Richard Sonnenblick of Planview discuss how AI-driven innovations enhance customer and employee experiences, and unlock organizational growth through cutting-edge tools and strategies.Topics Include:Importance of integrated communication tools for businesses.Challenges caused by disconnected communication platforms.Role of data in enhancing business operations.How RingCentral addresses communication and data integration issues.Benefits of real-time conversational intelligence in organizations.Leveraging AI to transform communication into actionable insights.Unlocking customer and employee voices through AI.How AI identifies patterns in customer interactions.Overview of RingSense for Sales AI tool.Real-world success story with RingSense for Sales.Streamlining customer interactions using AI-powered analysis.Enhancing employee productivity with AI-driven tools.AI solutions for faster, accurate information searches.Overview of RingCentral's Ring CX contact center solution.Improving customer satisfaction through AI-powered call analysis.Case study: Success with Ring CX at Worldwide Express.Features and benefits of RingCentral Events platform.Integrating event tech with existing customer workflows.Personalizing events with branding and engagement tools.PlanView’s use of AWS to drive innovation.Solving governance challenges with PlanView’s solutions.How generative AI accelerates productivity and decision-making.Making every user a power user with AI.Practical examples of generative AI in project management.Unlocking growth with next-gen AI-driven business tools.Participants:Richard Bornstein - Chief Business Development Officer, RingCentralRichard Sonnenblick Ph.D – Chief Data Scientist, PlanviewSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

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/

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/

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/

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/

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/