Justin DiPietro, Co-Founder & Chief Strategy Officer of Glia, shares how they are leveraging AI to enhance the customer experience in the highly regulated world of financial institutions.

Topics Include:

• Glia provides voice, digital, and AI services for customer-facing and internal operations
• Built on "channel-less architecture" unlike traditional contact centers that added channels sequentially
• One interaction can move seamlessly between channels (voice, chat, SMS, social)
• AI applies across all channels simultaneously rather than per individual channel
• 700 customers, primarily banks and credit unions, 370 employees, headquartered in New York
• Targets 3,500 banks and credit unions across the United States market
• Focuses exclusively on financial services and other regulated industries
• AI for regulated industries requires different approach than non-regulated businesses
• Traditional contact centers had trade-off between cost and quality of service
• AI enables higher quality while simultaneously decreasing costs for contact centers
• Number one reason people call banks: "What's my balance?" (20% of calls)
• Financial services require 100% accuracy, not 99.999% due to trust requirements
• Uses AWS exclusively for security, reliability, and future-oriented technology access
• Real-time system requires triple-hot redundancy; seconds matter for live calls
• Works with Bedrock team; customers certify Bedrock rather than individual features
• Showed examples of competitors' AI giving illegal million-dollar loans at 0%
• "Responsible AI" separates probabilistic understanding from deterministic responses to customers
• Uses three model types: client models, network models, and protective models
• Traditional NLP had 50% accuracy; their LLM approach achieves 100% understanding
• Policy is "use Nova unless" they can't, primarily for speed benefits

Participants:

• Justin DiPietro – Co-Founder & Chief Strategy Officer, Glia

Further Links:

• Glia Website
• Glia AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Ed Bailey, Field CISO at Cribl, shares how Cribl and AWS are helping customers rethink their data strategy by making it easier to modernize, reduce complexity, and unlock long-term flexibility.

Topics Include:

• Ed Bailey introduces topic: bridging gap between security data requirements and budget
• Companies face mismatch: 10TB data needs vs 5TB licensing budget constraints
• Data volumes growing exponentially while budgets remain relatively flat year-over-year
• IT security data differs from BI: enormous volume, variety, complexity
• Many companies discover 600+ data sources during SIEM migration projects
• 50% of SIEM data remains un-accessed within 90 days of ingestion
• Complex data collection architectures break frequently and require excessive maintenance
• Teams spend 80% time collecting data, only 20% analyzing for value
• Data collection and storage are costs; analytics and insights provide business value
• Poor data quality creates operational chaos requiring dozens of browser tabs
• SOC analysts struggle with context switching across multiple disconnected systems
• Traditional vendor approach: "give us all data, we'll solve problems" is outdated
• Data modernization requires sharing information widely across organizational business units
• Data maturity model progression: patchwork → efficiency → optimization → innovation
• Data tiering strategy: route expensive SIEM data vs cheaper data lake storage
• SIEM costs ~$1/GB while data lakes cost ~$0.15-0.20/GB for storage
• Compliance retention data should go to object storage at penny fractions
• Decouple data retention from vendor tools to enable migration flexibility
• Cribl platform offers integrated solutions: Stream, Search, Lake, Edge components
• Customer success: Siemens reduced 5TB to 500GB while maintaining security effectiveness

Participants:

• Edward Bailey – Field CISO, Cribl

Further Links:

• Cribl Website
• Cribl on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Sam Johnson, Chief Customer Officer of Jamf, discusses the implementation of AI built on Amazon Bedrock that is a gamechanger in helping Jamf’s 76,000+ customers scale their device management operations.

Topics Include:

• Sam Johnson introduces himself as Chief Customer Officer from Jamf company
• Jamf's 23-year mission: help organizations succeed with Apple device management
• Company manages 33+ million devices for 76,000+ customers worldwide from Minneapolis
• Jamf has used AI since 2018 for security threat detection
• Released first customer-facing generative AI Assistant just last year in 2024
• Presentation covers why, how they built it, use cases, and future plans
• Jamf serves horizontal market from small business to Fortune 500 companies
• Challenge: balance powerful platform capabilities with ease of use and adoption
• AI could help get best of both worlds - power and simplicity
• AI also increases security posture and scales user capabilities significantly
• Customers already using ChatGPT/Claude but wanted AI embedded in product
• Built into product to reduce "doorway effect" of switching digital environments
• Created small cross-functional team to survey land and build initial trail
• Rest of engineering organization came behind to build the production highway
• Team needed governance layer with input from security, legal, other departments
• Evaluated multiple providers but ultimately chose Amazon Bedrock for three reasons
• AWS team support, large community, and integration with existing infrastructure
• Uses Lambda, DynamoDB, CloudWatch to support the Bedrock AI implementation
• AI development required longer training/validation phase than typical product features
• Released "AI Assistant" with three skills: Reference, Explain, and Search capabilities

Participants:

• Sam Johnson – Chief Customer Officer, Jamf

Further Links:

• Jamf.com
• Jamf on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Mark Stevens, SVP, Channels and Alliances, discusses how SecurityScorecard's strategic partnership with AWS enables them to scale their security solutions through cloud infrastructure, marketplace integration, and co-sell programs

Topics Include:

• SecurityScorecard founded 10 years ago to understand third-party vendor security posture
• Company has grown to 3,000 enterprise customers and 200+ partners globally
• Evolved from ratings to "supply chain detection and response" over last year
• Supply chain threats have doubled, creating extended attack surfaces for companies
• Many organizations don't know their vendor count or vulnerabilities within supply chains
• SecurityScorecard provides visibility into attack surfaces and management tools for control
• Generative AI is central to their ecosystem, leveraging AWS Bedrock extensively
• They scan the entire internet every two days at massive scale
• Have scored 12 million companies with security scorecards to date
• All workloads run on AWS cloud infrastructure as their primary platform
• AWS partnership provides necessary scale for managing hundreds of thousands of vendors
• Case study: Identified vendor misconfigurations that could shut down 1,000 locations
• Own massive 10-year data lake with tens of millions of companies
• New managed service combines AI automation with human analysts for support
• Large organizations cannot fully automate supply chain security management yet
• Quality threat intelligence data now valuable to SOC teams, not just risk
• Third-party risk management and SOC teams are slowly converging for better security
• AWS marketplace integration provides frictionless customer experience and larger deals
• Co-sell programs with AWS enterprise sales teams create effective flywheel motion
• Future expansion includes identity management, response actions, and internal signal management

Participants:

• Mark Stevens – SVP, Channels and Alliances, SecurityScorecard

Further Links:

• SecurityScorecard.io
• SecurityScorecard AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Chief Product Development Officer Mitchell Johnson discusses how Sonatype protects enterprise developers from malicious open source components while keeping them productive through AI.

Topics Include:

• Sonatype provides software supply chain solutions for enterprises using open source components
• They serve large enterprises, government agencies, and critical infrastructure providers globally
• Main challenge: keeping developers productive while maintaining secure software supply chains
• Cybercrime and supply chain attacks are massive, growing industries threatening developers
• AI adoption is happening faster than expected, profoundly changing development workflows
• Bad actors evolved from waiting for vulnerabilities to creating malicious components
• Malicious open source components specifically target developer and DevOps toolchains
• Sonatype's security research team uses AI/ML to analyze every open source component
• They can predict and block malicious components before entering customer environments
• AWS partnership helps Sonatype meet customers where they want to do business
• Partnership focuses on go-to-market alignment, not just technical integration
• AWS sales teams should be treated as extensions of your own sales organization
• Understanding AWS sales structure and incentives is crucial for successful partnerships
• AI development is following same pattern as open source adoption twenty years ago
• "Shadow AI" parallels the earlier "shadow IT" trend with open source software
• AI speeds up code generation but security review processes haven't kept pace
• Developers need a "Hippocratic Oath" - taking responsibility for AI-generated code output
• Within 24 months, professionals not skilled in AI will struggle to stay relevant
• Sonatype's culture encourages curiosity, experimentation, and accepts failure as part of innovation
• Their core mission: help developers focus on innovation, not security chores

Participants:

• Mitchell Johnson – Chief Product Development Officer, Sonatype

Further Links:

• Sonatype Website
• Sonatype on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

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 criminals
• White hat hackers stress test systems to identify security weaknesses proactively
• Generative AI plays a huge role in HackerOne's security operations
• Security teams struggle with constant toil of finding and fixing vulnerabilities
• AI helps minimize toil through natural language interfaces and automation
• Both good and bad actors have access to generative AI tools
• Success requires measuring individual task inputs and outputs, not just aggregates
• Breaking down workflows into granular tasks reveals measurable AI improvements
• HackerOne deployed "Hive," their AI security agent to reduce customer toil
• Initial focus was on tasks where AI clearly outperformed humans
• Started with low-hanging fruit before tackling more complex strategic workflows
• Accuracy is the primary success metric, not just efficiency or speed
• Security requires precision; wrong fixes create bigger problems than inefficiency
• Customer acceptance and reduced time to remediation are north star metrics
• Humans remain the source of truth for validation and feedback loops
• Break down human jobs into granular AI tasks using systems thinking
• Build specific agents for individual tasks rather than entire job roles
• Keep humans accountable for end-to-end outcomes to maintain customer trust
• AWS Bedrock chosen for security, confidentiality, and data separation requirements
• Moving from efficiency improvements to entirely new AI-enabled capabilities

Participants:

• Alex Rice – Founder & CTO/CISO, HackerOne

Further Links:

• HackerOne Website
• HackerOne on AWS Marketplace

See 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, Asana
• Oliver Myers - Worldwide Head of Business Development, Amazon Web Services

Further Links:

• Asana.com
• Asana on AWS Marketplace

See 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, Celonis

Further Links:

• Celonis Website
• Celonis on AWS Marketplace

See 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 implementation
• Three AWS partners share real-world AI deployment experiences
• Automation Anywhere automates end-to-end business processes with agents
• Vectra.ai uses autonomous agents for cybersecurity threat detection
• Qlik applies generative AI across their data platform portfolio
• Customer service automation handles L1 support requests efficiently
• Utility company processes 144,000 complaints annually using agents
• Regulatory compliance improved through faster complaint resolution workflows
• Cybersecurity agents reduce threat detection time by 50-60%
• Triage, correlation, and prioritization handled by autonomous agents
• Signal fatigue reduced through intelligent alert filtering systems
• Natural language queries enable faster business decision making
• Sales AI agent provides competitive information during calls
• AWS Marketplace reduced 7,000 weekly tickets to zero
• 2023 was proof-of-concept year, 2024 focuses production deployment
• AWS Bedrock integration seamless with existing data repositories
• Model optionality crucial for different use case requirements
• Agility most important capability in generative AI journey
• Code abandonment becomes acceptable due to rapid innovation
• Maximum team size of 10 people maintains development agility
• Targeted solutions outperform broad platform capabilities in adoption
• Implementation expertise becomes bottleneck for customer scaling efforts
• Natural language interaction patterns completely shifted user behavior
• Keywords searches replaced by conversational query approaches
• Responsible AI committees review decisions and establish principles
• Security considerations balance speed with responsible deployment practices
• Bad actors adopt generative AI faster than defenders
• Explainability requirements slow feature rollout but ensure auditability
• Multi-modal deployments use different models for specific cases
• Future trends include AI-powered business process outsourcing

Participants:

• Peter White – SVP, Emerging Products, Automation Anywhere
• Ryan Welsh – Field CTO - Generative AI, Qlik
• John Skinner – Vice President Corporate/Business Development, Vectra.ai
• Chris Grusz – Managing Director for Technology Partnerships, AWS

Further Links:

• Automation Anywhere in AWS Marketplace
• Qlik in AWS Marketplace
• Vectra.ai in AWS Marketplace

See 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 merger
• Provides full security stack visibility in single platform
• Serves SMBs to Fortune 500 and government customers
• Used machine learning for two decades with 30 models
• Recently pivoted to generative AI with Wwise platform
• AI finds critical events among thousands daily alerts
• Incorporates threat hunting knowledge into AI prompt structures
• AWS Bedrock central to AI strategy for model flexibility
• Formed small tiger team to investigate generative AI
• Anthropic Claude provided breakthrough "aha moments" for capabilities
• Adopted "fail fast, learn fast" innovation culture approach
• Enabled employee access to models through Bedrock API
• Conducted innovation jam sessions with VC-style pitches
• AI decoded Base64 without prompting, identified benign activity
• Junior analysts elevated to level two with AI
• Common misconception: models train on customer data falsely
• Early challenge: providing too much data overwhelmed models
• Smaller models hallucinated more with plausible-sounding responses
• Design partner programs help prioritize product development
• Democratize AI access beyond just technical teams
• Test multiple models for specific use cases
• Large models work better than small ones initially
• Prompt engineering crucial for effective model communication
• Model Context Protocol will gain traction next year
• Backend data security remains largely unsolved challenge
• Federal customers require on-premises, air-gapped AI solutions

Participants:

• Zak Krider – Director of AI and Innovation, Trellix

Further Links:

• Website: https://www.trellix.com
• Trellix on AWS Marketplace

See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/