Learn how Amazon Bedrock enabled Prophix to build enterprise-grade AI agents that transform secure CFO workflows, while delivering real-time financial intelligence through advanced agentic architecture.

Topics Include:

• AWS and Prophix leaders discuss building autonomous AI agents for financial management
• AI agents defined: autonomous systems that reason, plan, and execute tasks independently
• Evolution from simple chatbots to collaborative multi-agent systems solving complex problems
• Core agent components: cognitive planning module, memory systems, and external tool integrations
• Prophix: 30-year financial software company serving 3,500 CFO offices globally across industries
• Prophix One Intelligence: platform-level AI service powering predictions, analysis, and automation
• Customer concerns addressed: data privacy, role-based security, accuracy, and cost control
• Rejected "models in the sky" approach for AWS Bedrock's managed, controllable infrastructure
• Agentic architecture: LLMs generate API parameters instead of processing massive datasets
• Real-time data access, automatic security inheritance, and A/B testing capabilities achieved
• Live demo: automated budgeting workflows, natural language queries, and autonomous task execution
• AWS introduces AgentCore platform to simplify agent development for enterprise customers

Participants:

• Anurag Yagnik – Chief Technology Officer, Prophix
• Deborshi Choudhury – Sr Solutions Architect – ISV, Amazon Web Services

Further Links:

• Prophix: Website | LinkedIn 

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

Dean Teffer of Arctic Wolf reveals how they process 8 trillion weekly security observations to find "a needle in a stack of needles," and breaks down real-world GenAI lessons learned.

Topics Include:

• Dean Teffer, VP of AI at Arctic Wolf, discusses company's GenAI journey
• Arctic Wolf: decade-old security operations company serving mid-market customers globally
• Operates massive security operation center, now launching AI-powered products
• AI agent recently identified Black Basta ransomware attack, enabling rapid containment
• Dean's 15+ years in cybersecurity: traditional ML focused on detection
• GenAI breakthrough allows natural language interaction with security models
• Arctic Wolf processes 8 trillion weekly observations, correlating suspicious activities
• Challenge: finding specific threats in "stack of needles," not haystack
• Success measured by making human analysts faster, more consistent, scalable
• Evolved from treating GenAI like traditional ML to integrated workflows
• Key misconception: GenAI isn't magic, needs proper data and reasoning
• Advice: start with existing challenges, build flexible systems for adaptation
• GenAI excels at summarizing information and supporting complex decisions
• Future vision: AI handles routine threats, humans focus on creativity
• Democratizing machine learning capabilities to broader range of subject experts

Participants:

• Dean Teffer – Vice President of AI, Arctic Wolf

Further Links:

• Arctic Wolf: Website | LinkedIn | 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/

CyberArk's technology leader discusses their strategy for securing against AI threats, protecting agentic AI systems, and their vision for the future in an increasingly AI-driven cybersecurity landscape.

Topics Include:

• CyberArk celebrates recent exciting news while discussing their incredible cybersecurity journey
• Founded in 1999, CyberArk pioneered privilege access management and expanded into comprehensive identity security
• Company executed textbook SaaS transformation from perpetual licensing to subscription-based cloud model
• Leadership set clear customer expectations, framing SaaS shift as faster innovation delivery
• Addressed customer concerns about cost predictability, security compliance, and data residency requirements
• Technical team implemented lift-and-shift architecture with AWS RDS and multi-tenant improvements
• Corporate initiative tracked weekly metrics and milestones throughout full development lifecycle process
• Customer Success evolved from transactional support to strategic partnership embedded in security journeys
• AWS partnership fundamental to cloud journey with 25+ integrations and Marketplace collaboration
• AI strategy focuses on three pillars: using AI, securing against AI threats
• Future 12-24 months: continue securing all identities while expanding AI capabilities and solutions
• AWS partnership expanding in 2025 leveraging machine identity leadership and GenAI advances

Participants:

• Peretz Regev – Chief Product & Technology Officer, CyberArk
• Boaz Ziniman – Principal Developer Advocate - EMEA, Amazon Web Services

Further Links:

·        CyberArk: Website – LinkedIn – 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/

Three leading ISV executives from Coveo, DTEX Systems and Honeycomb, reveal how companies with proprietary datasets are gaining unbeatable competitive advantages in the AI era and share real-world strategies how you have similar outcomes.

Topics Include:

• Panel introduces three ISV leaders discussing data platform transformation for AI
• DTEX focuses on insider threats, Coveo on enterprise search, Honeycomb on observability
• Companies with proprietary datasets gain strongest competitive advantage in AI transformation
• Data gravity concept: LLMs learning from unique datasets create defensible business positions
• Coveo maintains unified enterprise index with real-time content and access rights sync
• Honeycomb enables subsecond queries for analyzing logs, traces, and metrics at scale
• Multi-tenant architectures balance shared infrastructure benefits with single-tenant data separation
• Coveo deployed 140,000 times last year using mostly multi-tenant, some single-tenant components
• DTEX scaled from thousands to hundreds of thousands endpoints after architectural transformation
• Capital One partnership taught DTEX how to break monolithic architecture into services
• Apache Iceberg and open table formats enable interoperability without data duplication
• Honeycomb built custom format following similar patterns with hot/cold storage tiers
• Business data catalogs become critical for AI agents understanding dataset context
• MCP servers allow AI systems to leverage structured cybersecurity datasets effectively
• DTEX used Cursor with their data to identify North Korean threat actors
• Real-time AI data needs balanced with costs using right models for jobs
• Caching strategies and precise context reduce expensive LLM inference calls unnecessarily
• Search remains essential for enterprise AI to prevent hallucination and access information
• ROI measurement focuses on cost reduction, analyst efficiency, and measurable business outcomes
• Key takeaway: invest in data structure early, context is king, AI is just software

Participants:

• Sebastien Paquet - Vice President of AI Strategy, Coveo
• Rajan Koo - CTO, DTEX Systems
• Patrick King - Head of Data, Honeycomb.io
• KP Bhat - Sr Solutions Architecture Leader- Analytics & AI, Amazon Web Services

Further Links:

• Coveo: Website – LinkedIn – AWS Marketplace
• DTEX Systems: Website – LinkedIn – AWS Marketplace
• Honeycomb.io: Website – LinkedIn – 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/

Okta's CTO Bhawna Singh discusses AI adoption, innovation and the four critical identity patterns needed to build the trust that accelerates AI implementation.

Topics Include:

• AI innovation races ahead while adoption lags due to trust and security concerns
• Research shows 82% plan AI deployment but 61% of customers demand trust first
• AI coding tools dramatically reduce development time, accelerating software delivery cycles
• AI interaction evolved from ChatGPT conversations to autonomous headless agents working independently
• Future envisions millions of agents making decisions and communicating without human oversight
• Complex data relationships emerge as agents access multiple dynamic sources simultaneously
• Trust fundamentally starts with identity - the foundation for all AI security
• Four critical identity patterns needed: authentication, API security, user confirmation, and authorization
• Authentication ensures legitimate agents while token vaults enable secure agent-to-agent communication
• Asynchronous user approval prevents rogue decisions like the recent database deletion incident
• Industry standards like MCP protocol establish minimum security guardrails for interoperability
• Trust accelerates AI adoption through security, accountability, and collaborative standard-building efforts

Participants:

• Bhawna Singh – CTO, Customer Identity, Okta

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

A panel discussion with AI industry leaders revealing how enterprises are scaling AI today, with predictions on coming breakthroughs for AI and the impact on Fortune 500 companies and beyond.

Topics Include:

• Three technical leaders discuss production challenges: security, interoperability, and scaling agentic systems
• Panelists represent Enkrypt (security), Anyscale (infrastructure), and CrewAI (agent orchestration platforms)
• Industry moving from flashy demos to dependable agents with real business outcomes
• Breakthrough examples include 70-page IRS form processing and multimodal workflow automation
• Multimodal data integration becoming crucial - incorporating video, audio, screenshots into decisions
• Less than 10% of future applications expected to be text-only
• Companies shifting from experimenting with individual models to deploying agent networks
• Need for governance frameworks as enterprises scale to hundreds of agents
• Growing software stack complexity requires specialized infrastructure between applications and GPUs
• Security teams need centralized visibility across fragmented agent deployments across enterprises
• Existing industry regulations apply to AI services - no special AI laws needed
• Interoperability standards debate: MCP gaining adoption while A2A seems premature solution
• MCP shows higher API reliability than OpenAI tool calling for implementations
• Multimodal systems more vulnerable to attacks but value proposition too high ignore
• Fortune 500 company automated price operations approval process using 630 brands data
• 87% of enterprise customers deploy agents in private VPCs or on-premises infrastructure
• Specialized AI systems needed to oversee other agents at machine speed scales
• Cost optimization through model specialization rather than always using most powerful models
• Future learning may happen through context/prompting rather than traditional weight fine-tuning
• Predictions include AI meeting moderators and agents working autonomously for hours

Participants:

• Robert Nishihara - Co-founder, Anyscale
• João Moura - CEO, CrewAI
• Sahil Agarwal - Co-Founder & CEO, Enkrypt AI
• Jillian D’Arcy - Sr. ISV Sales Leader, Amazon Web Services

Further Links:

• Anyscale – Website  | LinkedIn  | AWS Marketplace
• CrewAI - Website  | LinkedIn  | AWS Marketplace
• Enkrypt AI - Website  | LinkedIn  | 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/

AI executives from Archer, Demandbase and Highspot and AWS reveal how they're tackling AI's biggest challenges—from securing data, managing regulatory changes and keeping humans in the loop.

Topics Include:

• Three AI leaders introduce their companies: Archer, Demandbase and Highspot's approaches to enterprise AI
• Demandbase's data strategy: Customer data stays isolated, shared data requires consent, public sources fuel training
• Geographic complexity: AI compliance varies dramatically between Germany, US, Canada, and California regulations
• HighSpot tackles sales bias: Granular questions replace generic assessments for more accurate rep evaluations
• SBI framework applied to AI: Specific behavioral observations create better, more actionable sales coaching
• AI transparency through citations: Timestamped evidence lets managers verify AI feedback and catch hallucinations
• Archer handles 20-30K monthly regulations: AI helps enterprises manage overwhelming compliance requirements at scale
• Two compliance types explained: Operational (common across companies) versus business-specific regulatory requirements
• EU AI Act adoption: US companies embracing European framework for responsible AI governance
• Human oversight becomes mandatory: Expert-in-the-loop reviews ensure AI decisions remain correctable and auditable
• The bigger AI risk: Companies face greater danger from AI inaction than AI adoption
• Agentic AI security challenges: Data layers must enforce permissions before AI access, not after
• AI agents need identity management: Same access controls apply whether human clicks or AI acts
• Human oversight in high stakes: Chief compliance officers demand transparency and correction capabilities
• Future challenge identified: 80% of enterprise data behind firewalls remains invisible to AI models

Participants:

• Kayvan Alikhani - Global Head of Engineering- Emerging Solutions, Archer Integrated Risk Management
• Umberto Milletti - Chief R&D Officer, Demandbase
• Oliver Sharp - Co-Founder & Chief AI Officer, Highspot
• Brian Shadpour - General Manager, Security, Amazon Web Services

Further Links:

• Archer Integrated Risk Management: Website – LinkedIn – AWS Marketplace
• Demandbase: Website – LinkedIn – AWS Marketplace
• Highspot: Website – LinkedIn – 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/

In a fascinating discussion, Rob McGrorty, Product Leader of Agents at Amazon AGI Lab, reveals how rapidly AI agents are evolving with corporate adoption exploding as companies race to deploy production agents and the challenges and advantages they’re experiencing.

Topics Include:

• GenAI adoption outpaces all previous tech waves, growing faster than computers or internet
• Early adopters tackle complex tasks while newcomers still use basic text manipulation features
• AI models double their single-call task capabilities every seven months, exponentially increasing power
• Accelerating progress makes yesterday's magic mundane, unlocking mass creativity and customer demand
• Agents represent natural evolution: chatbots answered questions, now agents autonomously accomplish tasks
• Amazon's browser agent finds apartments, maps distances, ranks options using multiple transit modes
• Corporate adoption exploded: 33% piloting agents in 2024, 67% moving to production now
• Two main agent types today: API calling with tool use, browser automation
• Current applications mirror "RPA 2.0" - form filling, data extraction, website QA testing
• Future brings multi-agent systems, self-directing loops, and agent-to-agent negotiation scenarios
• Major challenges: data privacy, oversight protocols, error responsibility, and ecosystem sustainability
• Technical hurdles include real-time accuracy measurement, latency issues, and quality assurance frameworks

Participants:

• Rob McGrorty – Product Leader, Agents at Amazon AGI Lab

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

Gagan Singh of Elastic discuses how agentic AI systems reduce analyst burnout by automatically triaging security alerts, resulting in measurable ROI for organizations

Topics Include:

• AI breaks security silos between teams, data, and tools in SOCs
• Attackers gain system access; SOC teams have only 40 minutes to detect/contain
• Alert overload causes analyst burnout; thousands of low-value alerts overwhelm teams daily
• AI inevitable for SOCs to process data, separate false positives from real threats
• Agentic systems understand environment, reason through problems, take action without hand-holding
• Attack discovery capability reduces hundreds of alerts to 3-4 prioritized threat discoveries
• AI provides ROI metrics: processed alerts, filtered noise, hours saved for organizations
• RAG (Retrieval Augmented Generation) prevents hallucination by adding enterprise context to LLMs
• AWS integration uses SageMaker, Bedrock, Anthropic models with Elasticsearch vector database capabilities
• End-to-end LLM observability tracks costs, tokens, invocations, errors, and performance bottlenecks
• Junior analysts detect nation-state attacks; teams shift from reactive to proactive security
• Future requires balancing costs, data richness, sovereignty, model choice, human-machine collaboration

Participants:

• Gagan Singh – Vice President Product Marketing, Elastic

Additional Links:

• Elastic – LinkedIn - Website – 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/

Pete Rubio reveals how Rapid7 transformed to an AI-first platform that automates security investigations and accelerates results from hours to seconds.

Topics Include:

• Pete Rubio introduces Rapid7's journey to becoming an AI-first cybersecurity platform
• Cybersecurity teams overwhelmed by growing attack surfaces and constant alert fatigue
• Customers needed faster response times, not just more alerts coming faster
• Legacy tools created silos requiring manual triage that doesn't scale effectively
• AI must turn raw security data into real-time decisions humans can trust
• Unified data platform correlates infrastructure, applications, identity, and business context together
• Agentic AI automates investigative work, reducing analyst tasks from hours to seconds
• Rapid7 evaluated multiple vendors, choosing AWS for performance, cost, and flexibility
• Nova models delivered unmatched performance for global scaling at controlled costs
• Bedrock provided secure model deployment with governance and data privacy boundaries
• AWS partnership enabled co-development and rapid iteration beyond typical vendor relationships
• Transparent AI shows customers how models reach conclusions before automated actions
• SOC analyst expertise continuously trains models with real-time security intelligence
• Governance frameworks and guardrails implemented from day one, not retrofitted later
• Future plans include customer AI integration and bring-your-own-model capabilities

Participants:

• Pete Rubio – Senior Vice President, Platform & Engineering, Rapid7

Additional Links:

• Rapid 7 – LinkedIn - Website – 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/