AWS for Software Companies Podcast

ActiveFence CEO Noam Schwartz discusses how his company evolved from protecting platforms against user-generated harmful content to helping companies deploy public-facing AI safely at scale.Topics Include:Noam Schwartz introduces himself as ActiveFence CEOFormer intelligence officer specializing in open source intelligenceMission: protect online experiences for everyone everywhereOnline platforms constantly hammered by various attacksAttacks include cybersecurity, abuse, hate speech, spamCompanies playing endless whack-a-mole game with violationsNeed scalable solution that works across languages/formatsDeveloped enterprise-grade technology for sophisticated companiesAmazon became customer and great partner early onGenerative AI introduction changed the game completelyLLMs non-deterministic unlike traditional programmed chatbotsSame input produces different outputs each timeAI deployed in customer support, healthcare, airlinesNew risks when models speak on company's behalfOne bad output creates legal and reputational damageCompanies need to deploy public-facing AI safelyTransition affects healthcare, finance, gaming, government sectorsBuilding on years of user-generated content expertiseNo specific ChatGPT moment triggered their AI pivotActiveFence was AI company since day oneModel companies like Amazon, Nvidia asked for helpRealized their expertise perfectly suited for AI safetyStaying on top of AI developments is impossibleFocus on customer adoption, not every new releaseMain enterprise challenge is trusting AI technologyUnrealistic expectations for 100% accuracy from AIMost companies will license existing models, not buildSecurity solutions remain independent like traditional cybersecurityParticipants:Noam Schwartz – CEO and Co-Founder, ActiveFenceOfer Oringher – Software and Technology Account Manager, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Jeff Moncrief discusses Sonrai Security's Cloud Permissions Firewall, and the best practices for using AI-powered summaries and orchestration to ensure security at all points.Topics Include:Jeff Moncrief introduces Sonrai Security and Cloud Permissions FirewallFocus on achieving least privilege access in AWS quicklyLightweight orchestration layer secures IAM from inside outEliminates need to write hundreds of individual policiesCustomers struggle with identity risk in CNAP/CSPM toolsGenerative AI adoption driving top security use casesBedrock and AI agents mentioned daily by customersProduct managers should consider underlying platform security risksAI models have control over infrastructure they run onIdentity is fundamental infrastructure enabling AWS AI modelsSonrai uses Bedrock capability inside Cloud Permissions FirewallJust-in-time access provides temporary, time-boxed AWS accessBedrock generates session summaries from audit logs automaticallyPlain English insights show what happened during sessionsSession summaries improve audit compliance and incident responseCustomer with 1000 accounts manually deployed service controlsFriday afternoon deployment caused very bad weekend disasterPolicy inheritance issues broke child accounts and OUsPlanning and orchestration essential for scaling AI securitySonrai platform built 100% cloud-native on AWSCoordinates service control policies and resource control policiesJust-in-time access relies on IAM Identity CenterParticipates in ISV Accelerate and AWS MarketplaceSecurity best practices start with identity as foundation"Hackers don't hack, they just log in" philosophyEliminate standing privileges with just-in-time access patternsRestrict AI services by user, location, and accountReview over-permissioned or inactive third-party vendor accessActionable insights through useful logging and AI summarizationFuture focus on protecting new services and permissionsParticipants:Jeff Moncrief – Field CTO & Director of Sales Engineering, Sonrai SecurityLinks:Website – Sonraisecurity.comAWS Marketplace – Sonrai SecuritySee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Akanksha Bilani of Intel shares how businesses can successfully adopt generative AI with significant performance gains while saving on costs.Topics Include:Akanksha runs go-to-market team for Amazon at IntelPersonal and business devices transformed how we communicateForrester predicts 500 billion connected devices by 20265,000 billion sensors will be smartly connected online40% of machines will communicate machine-to-machineWe're living in a world of data delugeAI and Gen AI help make data effectiveGoal is making businesses more profitable and effectiveVarious industries need Gen AI and data transformationIntel advises companies as partners with AWSThree factors determine which Gen AI use cases adoptFactor one: availability and ease of use casesHow unique and important are they for business?Does it have enough data for right analytics?Factor two: purchasing power for Gen AI adoption70% of companies target Gen AI but lack clarityLeaders must ensure capability and purchasing power existFactor three: necessary skill sets for implementationNeed access to right partnerships if lacking skillsIntel and AWS partnered for 18 years since inceptionIntel provides latest silicon customized for Amazon servicesEngineer-to-engineer collaboration on each processor generation92% of EC2 runs on Intel processorsIntel powers compute capability for EC2-based servicesIntel ensures access to skillsets making cloud aliveAWS services include Bedrock, SageMaker, DLAMIs, KinesisPerformance is the top three priorities for successNot every use case requires expensive GPU acceleratorsCPUs can power AI inference and training effectivelyEvery GPU has a CPU head node component Participants:Akanksha Bilani – Global Sales Director, IntelSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

Victoria Chin of Asana and Michael Horn of AWS demonstrate how Amazon Q integrates with Asana to enable AI-powered workflows while dramatically reducing manual work and improving cross-functional collaboration.Topics Include:Victoria Chin introduces herself as Asana's CPO Chief of StaffMichael Horn from AWS discusses customer feedback on generative AIAI agents limited by quality of data pulled into themAmazon Q Business created to analyze information and take actionHundreds of customers using Q Business across various industries dailyAWS hosts most business applications, ideal for AI journeyAmazon Q has most built-in, managed, secure data connectors availableQ Index creates comprehensive, accessible index of all company dataSecurity permissions automatically pulled in, no manual configuration neededSupports both structured and unstructured data from multiple sourcesVictoria returns to discuss Asana's integration with Q IndexBillions invested in integrations, but usage still lags behindTeams switch between apps 1000 times daily, missing connectionsRoot problem: no reliable way to track who/what/when/whyContent platforms store work but don't manage or coordinateAsana bridges content and communication for effective teamwork scalingAI disrupting software, but questions remain about real valueSoftware must provide structured framework to guide LLMs effectivelyAI needs data AND structure to separate signal from noiseAsana Work Graph maps how work actually gets done organizationallyWork Graph visualized as interconnected data, not rows and columnsMost strategic work is cross-functional, requiring multiple teams collaboratingTraditional integrations require manual setup and knowing when to useQ Index gives Asana access to 40+ different data connectorsUsers can ask questions, get answers with cross-application contextAI Studio enables no-code building of workflows with AI agentsProduct launch example shows intake, planning, execution, and reporting stagesAI can surface relevant documents, research, and updates automaticallyChat is tip of iceberg; real power comes from embedded workflowsIntegration evolves from feature-level to AI-powered product-level connectionsParticipants:Victoria J. Chin – Chief of Staff / Product Strategy, AI, AsanaMichael Horn – Principal Head of Business Development – Artificial Intelligence & Machine Learning, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon/isv/

AWS leaders commemorate the podcast's 100th episode while looking ahead to expanded coverage of technology partners and continued focus on generative AI, modern data strategies, agentic AI solutions and more!Topics Include:Episode 100 celebrates milestone of AWS software companies podcastWeekly podcast shares ISV stories, best practices, guidanceToday features AWS leader thoughts on ISV communityArym Diamond heads North America data and AI salesSpecialist team helps win deals, create happy customersISV customers do cutting-edge work on AWS platformISVs create force multiplier effect for entire companyBuilding community through podcast video and audio contentKristen Backeberg leads global ISV partner marketing at AWSPodcast featured 157 ISV leaders from 121 companiesReached over 30,000 listeners across 90+ countries worldwideISV partners drive cloud innovation across all industriesAWS supports growth from startups to enterprise leadersAPN network designed to help partners succeed, scaleOlawale Oladehin directs ISV solutions architecture in North AmericaPodcast shares customer insights, journeys, and innovationsAWS technology continues evolving to meet customer needsCarol Potts leads North America ISV sales at AWSPodcast started less than two years agoFirst episode titled "Data the Engine for Growth"Customer obsession drives everything AWS does for ISVsDeep collaboration focused on joint ISV success partnershipsVishal Sanghvi heads ISV marketing for North AmericaISVs face pressure delivering products at generative AI paceModern data strategy foundational for ISV product successFavorite episodes include Snowflake, Wiz, Coupang discussionsAWS offers programs for every ISV persona typeFuture episodes focus on generative AI, cybersecurity, dataAgentic AI becoming important for production phase evolutionPodcast expanding scope to include technology partnersParticipants:Kristen Backeberg – Director, Global ISV, Solutions Enterprise and Alliance Partner Marketing, Amazon Web ServicesArym Diamond – Director, US ISV Specialists, Amazon Web ServicesOlawale Oladehin – Director, ISV, Solutions Architecture, North America, Amazon Web ServicesCarol Potts – GM, ISV Sales Segment, North America, Amazon Web ServicesVishal Sanghvi - Head of ISV Field Marketing, North America, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Executive leaders from UneeQ and Zeta Global discuss the revolutionary impact of AI technologies that enable enhanced customer experiences and improved sales performances.Topics Include:Dave Cristini introduces panel on AI in advertising and marketing.Panel explores personalized experiences at scale with privacy focus.UneeQ creates AI-powered digital humans for brand interactions.Zeta Global uses AI to optimize customer messaging.LLMs combined with traditional ML empowers marketers to create models.Marketers can now build models without needing data scientists.AI agents integrated into systems can take action, not just respond.Agent chaining orchestrates sophisticated marketing actions automatically.AWS Bedrock provides tools to shape AI marketing future.Hyper-personalization becoming more achievable through AI automation.Ethics requires authenticity in brand AI representation.Transparency about data usage builds customer trust.Win-win approach: AI should augment teams, not just reduce costs.Integration difficulties remain a major challenge for AI implementation.AI agents have limited context windows and memory.Solution: Create specialized agents with persistent viewpoints.Companies need strong integration capabilities before implementing AI.Privacy regulations impact AI use in global marketing.Highly regulated industries require careful AI implementation strategies.Generative AI creates compliance challenges with unpredictable outputs.Digital humans eliminate judgment, revealing new customer insights.Banking clients discovered customers didn't understand financial terminology.Zeta improved onboarding with AI agents for data mapping.AI data mapping increased NPS scores and accelerated monetization.CMOs and CIOs increasingly collaborating on AI initiatives.Tension exists between marketing (quick wins) and IT (security).Strategic alignment with approved infrastructure enables scaling AI solutions.CEOs have critical role in aligning AI goals across departments.Internal AI use case: practicing sales with digital humans.Sales teams achieved 500% higher sales through AI role-playing.Participants:Danny Tomsett – Chief Executive Officer, UneeQRoman Gun – Vice President, Product, Zeta GlobalDavid Cristini – Director, ISV Sales, North America – Business Applications, AWSSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Ash Pembroke, Portfolio CTO of Caylent, discusses the critical balance of data accuracy in the era of Gen AI for the benefit of boosting innovation.Topics Include:Ash Pembroke, Portfolio CTO of Caylent, self-identifies as a "recovering data scientist."Caylent is an AWS native services company.Data quality remains an issue despite Gen AI.Contrasts legalism versus mysticism in data quality.Legalism: accurate data when applications need it.Mysticism: insights that help decision-making.Traditional data foundations approach is being challenged weekly.Gen AI developments force rethinking of solution architectures.Teams share solutions through giant Slack threads.Example: Vector databases questioned after model context protocol.Still do traditional data assessments, but stay flexible.Integration and data processing constantly get abstracted.Data strategy equals architecture strategy equals business strategy.Traditional approach: standardize data across engineering teams.New approach: allow business users to innovate.Bring valuable techniques back to the organization.Case study: North Sea wind turbine alerts.Initially seen as data quality issue, revealed new predictive failure signal.Gen AI enables local experimentation by business users.Blurring lines between enterprise enablement and software building.BrainBox AI case study: energy optimization across buildings.Architecture decisions impact ability to scale products.Work with business edges rather than looking for patterns.Gen AI can process information from these working groups.Think about data as a product, not asset.Redimensionalize dependencies across your organization.Now's a good time to attack data quality.New tools help visualize complexity across organizations.Participants:·        Ash Pembroke – Portfolio CTO, CaylentSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/ 

New Relic's Head of AI and ML Innovation, Camden Swita discusses their four-cornered AI strategy and envisions a future of "agentic orchestration" with specialized agents.Topics Include:Introduction of Camden Swita, Head of AI at New Relic.New Relic invented the observability space for monitoring applications.Started with Java workloads monitoring and APM.Evolved into full-stack observability with infrastructure and browser monitoring.Uses advanced query language (NRQL) with time series database.AI strategy focuses on AI ops for automation.First cornerstone: Intelligent detection capabilities with machine learning.Second cornerstone: Incident response with generative AI assistance.Third cornerstone: Problem management with root cause analysis.Fourth cornerstone: Knowledge management to improve future detection.Initially overwhelmed by "ocean of possibilities" with LLMs.Needed narrow scope and guardrails for measurable progress.Natural language to NRQL translation proved immensely complex.Selecting from thousands of possible events caused accuracy issues.Shifted from "one tool" approach to many specialized tools.Created routing layer to select right tool for each job.Evaluation of NRQL is challenging even when syntactically correct.Implemented multi-stage validation with user confirmation step.AWS partnership involves fine-tuning models for NRQL translation.Using Bedrock to select appropriate models for different tasks.Initially advised prototyping on biggest, best available models.Now recommends considering specialized, targeted models from start.Agent development platforms have improved significantly since beginning.Future focus: "Agentic orchestration" with specialized agents.Envisions agents communicating through APIs without human prompts.Integration with AWS tools like Amazon Q.Industry possibly plateauing in large language model improvements.Increasing focus on inference-time compute in newer models.Context and quality prompts remain crucial despite model advances.Potential pros and cons to inference-time compute approach.Participants:Camden Swita – Head of AI & ML Innovation, Product Management, New RelicSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Jon Yoo, CEO of Suger, shares how his company automates the complex & challenging workflows of selling software through cloud marketplaces like AWS.Topics Include:Jon Yoo is co-founder/CEO of Suger.Suger automates B2B marketplace workflows.Handles listing, contracts, offers, billing for marketplaces like AWS.Co-founder previously led Confluent's marketplace enablement product.Confluent had 40-50% revenue through cloud marketplaces.Required 10-20 engineers working solely on marketplace integration.Engineers prefer core product work over marketplace integration.Product/engineering leaders struggle with marketplace deployment requirements.Marketplace customers adopt without marketing, creating unexpected management needs.Version control is challenging for marketplace-deployed products.License management through marketplace creates engineering challenges.Suger helps sell, resell, co-sell through AWS Marketplace.Marketplace integration isn't one-time; requires ongoing maintenance.Business users constantly request marketplace automation features.Suger works with Snowflake, Intel, and AI startups.Data security concerns drive self-hosted AI deployments.AI products increasingly deploy via AMI/container solutions.AI products use usage-based pricing, not seat-based.Usage-based pricing creates complex billing challenges.AI products are tested at unprecedented rates.Two deployment options: vendor cloud or customer cloud.SaaS requires reporting usage to marketplace APIs.Customer-hosted deployment simplifies some billing aspects.Marketplaces need integration with ERP systems.Version control particularly challenging for AI products.Companies need automated updates for marketplace-deployed products.License management includes scaling up/down and expiration handling.Suger aims to integrate with GitHub for automatic updates.Participants:·        Jon Yoo – CEO and Co-founder, SugerSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/ 

SentinelOne's Ric Smith shares how Purple AI, built on Amazon Bedrock, helps security teams handle increasing threat volumes while facing budget constraints and talent shortages.Topics Include:Introduction of Ric Smith, President of Product Technology and OperationsSentinelOne overview: cybersecurity company focused on endpoint and data securityCustomer range: small businesses to Fortune 10 companiesProducts protect endpoints, cloud environments, and provide enterprise observabilityRic oversees 65% of company operationsPurple AI launched on AWS BedrockPurple AI helps security teams become more efficient and productiveSecurity teams face budget constraints and talent shortagesPurple AI helps teams manage increasing alert volumesTop security challenge: increased malware variants through AIAI enables more convincing spear-phishing attemptsIdentity breaches through social engineering are increasingVoice deepfakes used to bypass security protocolsFuture threats: autonomous AI agents conducting orchestrated attacksSentinelOne helps with productivity and advanced detection capabilitiesSentinelOne primarily deployed on AWS infrastructureUsing SageMaker and Bedrock for AI capabilitiesBest practice: find partners for AI training and deploymentCustomer insight: Purple AI made teams more confident and creativeAI frees security teams from constant anxietySentinelOne's hyper-automation handles cascading remediation tasksMultiple operational modes: fully automated or human-in-the-loopAgent-to-agent interactions expected within 24 monthsCommon misconception: generative AI is infallibleAI helps with "blank slate problem" providing starting frameworksAI content still requires human personalization and reviewAWS partnership provides cost efficiency and governance benefitsParticipants:·        Ric Smith – President – Product, Technology and Operations, SentinelOneSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/