AWS for Software Companies Podcast

Founder and CTO Alex Rice discusses how HackerOne uses generative AI to automate security workflows and prioritizing accuracy over efficiency to achieve end-to-end outcomes.Topics Include:HackerOne uses ethical hackers and AI to find vulnerabilities before criminalsWhite hat hackers stress test systems to identify security weaknesses proactivelyGenerative AI plays a huge role in HackerOne's security operationsSecurity teams struggle with constant toil of finding and fixing vulnerabilitiesAI helps minimize toil through natural language interfaces and automationBoth good and bad actors have access to generative AI toolsSuccess requires measuring individual task inputs and outputs, not just aggregatesBreaking down workflows into granular tasks reveals measurable AI improvementsHackerOne deployed "Hive," their AI security agent to reduce customer toilInitial focus was on tasks where AI clearly outperformed humansStarted with low-hanging fruit before tackling more complex strategic workflowsAccuracy is the primary success metric, not just efficiency or speedSecurity requires precision; wrong fixes create bigger problems than inefficiencyCustomer acceptance and reduced time to remediation are north star metricsHumans remain the source of truth for validation and feedback loopsBreak down human jobs into granular AI tasks using systems thinkingBuild specific agents for individual tasks rather than entire job rolesKeep humans accountable for end-to-end outcomes to maintain customer trustAWS Bedrock chosen for security, confidentiality, and data separation requirementsMoving from efficiency improvements to entirely new AI-enabled capabilitiesParticipants:Alex Rice – Founder & CTO/CISO, HackerOneFurther Links:HackerOne WebsiteHackerOne on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Spencer Herrick, Principal AI Product Manager of Asana and Oliver Myers of AWS demonstrate how their integration allows Asana's AI workflows to access enterprise data from Amazon Q Business, enabling seamless cross-application automation and insights.Topics Include:Oliver Myers leads Amazon Q Business go-to-market, Spencer Herrick manages Asana AI products.Session focuses on end user productivity challenges with generative AI technology implementations.End users face technology overload with doubled workplace application usage over five years.Data silos prevent getting maximum value from generative AI across fragmented enterprise systems.Workers spend 53% of time on "work about work" instead of strategic contributions.Ideal experience needs single pane of glass with cross-application insights and actions.Amazon Q Business launched as managed service with 40+ enterprise data connectors.Connectors maintain end-user permissions from source systems for enterprise security compliance.QIndex feature enables ISVs to access Q Business data via API calls.End users get answers enriched with multiple data sources without switching applications.Asana's work graph connects all tasks, projects, and portfolios to company goals.Phase 1 AI focused on narrow solutions like smart status updates.Phase 2 aimed for AI teammate capabilities requiring extensive contextual knowledge.AI Studio launched as no-code workflow automation builder within Asana platform.Q integration allows AI Studio to access cross-application context beyond Asana boundaries.SmartChat enhanced with Q can answer "what should I work on today?" holistically.Users returning from PTO can quickly understand goal risks across data sources.AI Studio workflows automate feature request processing across Asana, Drive, Slack, email.Partnership eliminates silos while maintaining enterprise security and permission controls.Integration creates connected ecosystem enabling true cross-application AI automation and insights.Participants:Spencer Herrick - Principal AI Product Manager, AsanaOliver Myers - Worldwide Head of Business Development, Amazon Web ServicesFurther Links:Asana.comAsana on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Chief Product Officer Dan Brown explains how Celonis creates digital twins of business processes to power AI agents that automate operational improvements.Topics Include:Dan Brown introduces Celonis as the thought leader in process mining for over a decade.Celonis serves largest global companies across all industries seeking operational improvements.Companies have process diagrams but actual operations differ significantly from documentation.Celonis creates digital twins of business processes by analyzing system data flows.Process intelligence reveals how work actually happens versus how companies think it happens.Platform enables process normalization, improvement assessment, and automated corrective actions.Celonis vision: making processes work better for people, companies, and the planet.Process intelligence provides visibility into current operations and improvement strategies.Great AI requires great data, but most companies only have static views.Process intelligence graph shows real-time flow of orders, invoices, and opportunities.Agentic AI requires four capabilities: sensing, planning, executing, and governing operations.Process intelligence enables real-time detection of conformance problems and deviations.AWS partnership leverages Bedrock for agentic AI and infrastructure for data processing.Data ingestion, organization, and enrichment are core to process intelligence value.AI agents now handle process deviations with increasing autonomy and sophistication.Heavy equipment manufacturer uses agents to coordinate with third-party vendors automatically.Agents text and email vendors to confirm delivery dates, reducing manual work.Implementation challenges include data quality, conservative adoption, and governance concerns.Companies should start with achievable use cases and expand gradually across domains.Future involves enterprise-wide process visibility powering automated applications and continuous improvement.Participants:Dan Brown – Chief Product Officer, CelonisFurther Links:Celonis WebsiteCelonis on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

AWS partners Automation Anywhere, Qlik, and Vectra.ai discuss revolutionizing customer experience through generative AI, sharing real-world implementations in automation, analytics, and cybersecurity applications. Topics Include:AWS Technology Partnerships panel on agentic AI implementationThree AWS partners share real-world AI deployment experiencesAutomation Anywhere automates end-to-end business processes with agentsVectra.ai uses autonomous agents for cybersecurity threat detectionQlik applies generative AI across their data platform portfolioCustomer service automation handles L1 support requests efficientlyUtility company processes 144,000 complaints annually using agentsRegulatory compliance improved through faster complaint resolution workflowsCybersecurity agents reduce threat detection time by 50-60%Triage, correlation, and prioritization handled by autonomous agentsSignal fatigue reduced through intelligent alert filtering systemsNatural language queries enable faster business decision makingSales AI agent provides competitive information during callsAWS Marketplace reduced 7,000 weekly tickets to zero2023 was proof-of-concept year, 2024 focuses production deploymentAWS Bedrock integration seamless with existing data repositoriesModel optionality crucial for different use case requirementsAgility most important capability in generative AI journeyCode abandonment becomes acceptable due to rapid innovationMaximum team size of 10 people maintains development agilityTargeted solutions outperform broad platform capabilities in adoptionImplementation expertise becomes bottleneck for customer scaling effortsNatural language interaction patterns completely shifted user behaviorKeywords searches replaced by conversational query approachesResponsible AI committees review decisions and establish principlesSecurity considerations balance speed with responsible deployment practicesBad actors adopt generative AI faster than defendersExplainability requirements slow feature rollout but ensure auditabilityMulti-modal deployments use different models for specific casesFuture trends include AI-powered business process outsourcingParticipants:Peter White – SVP, Emerging Products, Automation AnywhereRyan Welsh – Field CTO - Generative AI, QlikJohn Skinner – Vice President Corporate/Business Development, Vectra.aiChris Grusz – Managing Director for Technology Partnerships, AWSFurther Links:Automation Anywhere in AWS MarketplaceQlik in AWS MarketplaceVectra.ai in AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Zak Krider, Trellix's Director of Strategy and AI, shares how Trellix has successfully integrated generative AI into their security operations and democratized access to AI models across the organization.Topics Include:Trellix formed from McAfee Enterprise and FireEye mergerProvides full security stack visibility in single platformServes SMBs to Fortune 500 and government customersUsed machine learning for two decades with 30 modelsRecently pivoted to generative AI with Wwise platformAI finds critical events among thousands daily alertsIncorporates threat hunting knowledge into AI prompt structuresAWS Bedrock central to AI strategy for model flexibilityFormed small tiger team to investigate generative AIAnthropic Claude provided breakthrough "aha moments" for capabilitiesAdopted "fail fast, learn fast" innovation culture approachEnabled employee access to models through Bedrock APIConducted innovation jam sessions with VC-style pitchesAI decoded Base64 without prompting, identified benign activityJunior analysts elevated to level two with AICommon misconception: models train on customer data falselyEarly challenge: providing too much data overwhelmed modelsSmaller models hallucinated more with plausible-sounding responsesDesign partner programs help prioritize product developmentDemocratize AI access beyond just technical teamsTest multiple models for specific use casesLarge models work better than small ones initiallyPrompt engineering crucial for effective model communicationModel Context Protocol will gain traction next yearBackend data security remains largely unsolved challengeFederal customers require on-premises, air-gapped AI solutionsParticipants:Zak Krider – Director of AI and Innovation, TrellixFurther Links:Website: https://www.trellix.comTrellix on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

NVIDIA’s Global Head of Partnerships & Cloud for Startups, Jen Hoskins, details their collaboration with AWS to support over 25,000 startups through their Inception program.Topics Include:AI transformation happening across all industries and verticalsNVIDIA evolved from GPU company to full-stack AI solutionsAccelerated computing requires complete stack re-engineering from chip upTraditional CPU scaling has reached its fundamental performance limitsNVIDIA-AWS partnership spans over 13 years of co-developmentDGX Cloud integrates seamlessly with AWS SageMaker and BedrockOver 26 NVIDIA solutions available in AWS MarketplaceNVIDIA AI Enterprise accelerates data science and deployment pipelinesNIM microservices streamline AI model development like Docker containersCodeway gaming startup saved 48% on compute costs using NVIDIAEternal improved marketing ROI by 30X with generative AIQuoto achieved 10X content length and 3X throughput improvementNOATech biotech scaled cancer research with small team efficientlyNVIDIA Inception program supports over 25,000 startups globallyProgram covers 100+ countries across all verticals and stagesStartups get AWS credits up to $100,000 through ActivateDeveloper program offers free access to hundreds of SDKsThree program pillars: Innovate, Build, and Grow stagesVC Alliance connects startups with over 1,000 investorsVenture Capital Connect directly links startups to funding opportunitiesParticipants:Jen Hoskins – Startups, Global Head of Cloud, Partnerships & Go to Market, NVIDIAFurther Links:Website: https://www.nvidia.comNVIDIA Inception ProgramNVIDIA on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Matt “Kix” Kixmoeller, Chief Marketing Officer of Glean, shares how Glean partners with AWS to deploy secure, scalable AI solutions that help companies move from basic productivity tools to transformative business intelligence.Topics Include:Introduction to GleanGlean targets Global 2000 companies for AI transformationEnterprise AI needs company context: data, people, processesBottom-up approach: deploy to all employees firstFocus on business results, not just productivity gainsGlean Assistant provides daily AI tool for employeesGlean Agents platform enables natural language agent buildingOpen APIs export context to enterprise systemsStarted as enterprise search, evolved to knowledge graphsKnowledge graphs map content, people, projects, and processesIndividual knowledge graphs created for each personGlean WorkAI platform includes search, protect, agentsGlean Protect ensures data security and AI governancePlatform integrates with existing enterprise tools nativelyMCP enables connection to various AI systemsStrong growth: $100M ARR, $700M+ funding raisedAWS partnership provides models, security, and deploymentParticipants:Matt “Kix” Kixmoeller – Chief Marketing Officer, GleanFurther Links:Website: https://www.glean.com/Glean on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Kui Jia, Sumo Logic's Vice President of Engineering and Head of AI, shares how their AWS-powered AI agents transform chaotic security investigations into streamlined workflows.Topics Include:Kui Jia leads AI Engineering at Sumo LogicSREs and SOC analysts work under chaotic, high-pressure conditionsTeams constantly switch between different vendor tools and platformsInvestigation requires quick hypothesis formation and complex query writingSumo Logic processes petabytes of data daily across enterprisesCompany serves 2,000+ enterprise customers for 15 yearsPlatform focuses on observability and cybersecurity use casesInvestigation journey: discover, diagnose, decide, act, learn phasesData flows from ingestion through analytics to human insightsTraditional workflow relies heavily on tribal domain knowledgeSenior engineers create queries that juniors struggle to understandWar room situations demand immediate answers, not learning curvesContext switching between tools wastes time and creates frictionMultiple AI generations deployed: ML anomaly detection to GenAIAgentic AI enables reasoning, planning, tools, and evaluation capabilitiesMo Copilot launched at AWS re:Invent as AI agent suiteNatural language converts high-level questions into Sumo queriesSystem provides intelligent autocomplete and multi-turn conversationsInsight agents summarize logs and security signals automaticallyKnowledge integration combines foundation models with proprietary metadataAI generates playbooks and remediation scripts for automated actionsThree-tier architecture: Infrastructure, AI Tooling, and Application layersBuilt on AWS Bedrock with Nova models for performanceFocus on reusable infrastructure and AI tooling componentsData differentiation more important than AI model selectionGolden datasets and contextualized metadata are development challengesGuardrails and evaluation frameworks critical for enterprise deploymentAI observability enables debugging and performance monitoringEnterprise agents achievable within one year development timelineFuture vision: multiple AI agents collaborating with human investigatorsParticipants:Kui Jia – Vice President of AI Engineering, Head of AI, Sumo LogicFurther Links:Website: https://www.sumologic.com/Sumo Logic in the AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Wilson Patton, Solutions Architect for Trellix, demonstrates how their four-pillar Gen-AI framework transforms incident alerts into actionable intelligence.Topics Include:Wilson Patton: Trellix Solutions Architect, 20 years government experienceWitnessed evolution from basic firewalls to zero trust architecturesTrellix combines McAfee and FireEye heritage and capabilitiesAI integration isn't new - machine learning embedded for yearsPartnership with AWS Bedrock accelerates Gen-AI development capabilities2014: Developed Impossible Travel Analytic for anomaly detection2016: Launched Guided Investigations framework for SOC analysts2023: Introduced AI Guided Investigations with contextual understanding64% of public sector exploring AI adoption activelyOnly 21% have requisite data ready for trainingGen-AI won't magically clean up messy, siloed data74% of executives doubt AI information accuracy currentlyMonday morning alert queue: 76 high, 318 medium alertsAdversaries steal credentials 90 days before major incidentsCritical breadcrumbs hidden in low-priority informational alerts1000+ data-driven investigative questions developed over eight yearsSkilled analysts take too long reading all answersAutomate analysis, distill thousands down to ten critical alertsFour foundational pillars for effective, trustworthy Gen-AI implementationCybersecurity expertise essential - Gen-AI is just a toolFrameworks ensure reliability and consistent prompting for productionMultiple LLM models tested through AWS Bedrock platformQuality diverse datasets required for accurate question answeringGood prompts combine evidence, context, and comprehensive informationTesting shows order of magnitude price differences between modelsNova Micro provides cost-effective results for many scenariosPrompt engineering superior to fine-tuning for avoiding biasAgentic AI performs multi-step investigations with live dataStrategic model choice based on specific requirements and costsTransparent audit trails mandatory for government compliance requirementsParticipants:Wilson Patton – Solutions Architect, TrellixFurther Links:Website: https://www.trellix.comTrellix in the AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Jeff Chow, Chief Product and Technology Officer at Miro, explores how harnessing AI — in addition to reshaping teams and workflows — accelerates the product development lifecycle. He also shares insight into how Miro is embracing new technology and ways of working to transform its Innovation Workspace.Topics Include:Platform & PartnershipMiro serves 250,000+ customers with 90+ million knowledge workers using their Innovation WorkspacePlatform supports discovery, definition, and delivery phases of innovation processReal-time multiplayer canvas enables team co-creation across multiple formats, including seamless transitions between structured and unstructured work.Three-tier AWS partnership: infrastructure backbone, AI services (Bedrock/Q), and joint customer solutionsInnovation Challenges & FrictionProduct development lifecycle bottlenecks: separate tools per function create process delays and collaborative frictionPain points include stalled product kickoffs, lengthy design ideation cycles, and process delays from engineering architecture discussions.Leadership struggles with project visibility and strategic alignment across initiativesAI TransformationAI fundamentally shifts workflows with universal knowledge access at fingertipsCraft democratization blurs traditional role boundaries (PMs prototyping, developers designing)Agentic workflows and agents collapse traditional development stack layersAI shortcuts enable one-button synthesis of workshops into product briefsProduct development lifecycle compression from 20 steps to 5 key phasesBedrock and Q services create significant business accelerationOrganizational DesignCommon organizational rhythms and rituals create shared working languageDriving maximum impact by aligning on big initiatives vs. distributed prioritiesCollaborating across all functions — product, engineering, design — and at all organizational levelsBottom-up innovation requiring clear problem communication throughout organizationInclusive environments welcoming ideas from junior and introverted team membersWorking backwards planning and PR FAQs adopted from Amazon methodologiesCreating the next big thing with MiroLarge enterprises use Miro for strategic planning, OKR planning, capacity planning, roadmappingVisual proof-of-concepts and live demos make abstract concepts tangibleSame-day product brief delivery improves team collaboration and ownershipVoice of customer integration: automated synthesis of feedback into feature developmentMiro uses Miro internally to build next-generation featuresEnhanced employee engagement alongside improved business outcomesCustomers consistently achieve 2-3x time-to-market improvementsParticipants:Jeff Chow – Chief Product and Technology Officer, MiroJohan Broman – EMEA ISV Head of Solutions Architecture, AWSFurther Links:Website: https://miro.com/page/product-leaders/Miro in the AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/