Arctic Wolf's Dean Teffer reveals how they transformed security operations by processing one trillion daily alerts with AI, and shares hard-won lessons from operationalizing AI in production SOC environments

Topics Include:

• Arctic Wolf processes one trillion security alerts daily across 10,000 global customers
• Security operations remained stubbornly human-mediated due to constantly evolving threats and infrastructure complexity
• Dean explains why platformizing data creates a virtuous cycle enabling AI automation
• Traditional ML models couldn't handle SOC's situational complexity, leading to LLM adoption
• Arctic Wolf's unique advantage: direct access to 1000+ SOC analysts for continuous feedback
• AWS partnership began with governance concerns about data privacy and model training
• "Centaur Chess" approach: AI-human teams consistently outperform either alone in cybersecurity
• Three-generation AI evolution: from personal use to prompt engineering to expert-tuned models
• Three-day AWS hackathon achieved breakthroughs that would've taken months independently
• SOC analysts actively shaped AI responses through iterative feedback during live operations
• Observability proved critical: tracking performance, quality metrics, and response times for continuous improvement
• Measurable impact achieved: automated alert orientation dramatically increased analyst efficiency and response quality

Participants:

• Dean Teffer - VP of AI/ML, Arctic Wolf
• Aswin Vasudevan - Senior ISV Solution Architect, Amazon Web Services

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

Learn how DataStax transformed customer feedback into a hybrid search solution that powers Fortune 500 companies through their partnership with AWS.

Topics Include:

• AWS and DataStax discuss how quality data powers AI workloads and applications.
• DataStax built on Apache Cassandra powers Starbucks, Netflix, and Uber at scale.
• Their TIL app collects outside-in customer feedback to drive product development decisions.
• Hybrid search and BM25 kept trending in customer requests for several months.
• Customers wanted to go beyond pure vector search, not specifically BM25 itself.
• Research showed hybrid search improves accuracy up to 40% over single methods.
• ML-based re-rankers substantially outperform score-based ones despite added latency and cost.
• DataStax repositioned their product as a knowledge layer above the data layer.
• Developer-first design prioritizes simple interfaces and eliminates manual data modeling headaches.
• Hybrid search API uses simple dollar-sign parameters and integrates with Langflow automatically.
• AWS PrivateLink ensures security while Graviton processors boost efficiency and tenant density.
• Graviton reduced total platform operating costs by 20-30% with higher throughput.

Participants:

• Alejandro Cantarero – Field CTO, AI, DataStax
• Ruskin Dantra - Senior ISV Solution Architect, AWS, Amazon Web Services

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

Qlik's Field CTO for Generative AI Ryan Welsh reveals why 95% of enterprise AI projects fail and shares the three proven strategies the successful 5% use to deliver real business value from their AI investments.

Topics Include:

• Qlik's Field CTO reveals why 95% of AI projects fail despite massive investments
• MIT research shows shocking failure rates, but 5% are achieving real business value
• First major pitfall: Bad data foundations doom even the most sophisticated AI models
• Second problem: Companies use generative AI when predictive models would work better
• Third issue: Unnecessary complexity - AI projects disconnected from business outcomes
• Success secret #1: Ground AI in trusted enterprise data and user context
• Some LLMs struggle at specific tasks like claims processing despite passing medical exams
• Success secret #2: Let AI learn from users while keeping data governance intact
• Success secret #3: Embed AI directly into existing workflows like Salesforce
• Agentic AI shifts from reactive Q&A to proactive systems that execute across platforms
• Case study: Lintek reduced churn 10% and saved millions using these principles
• Your AI choices today will lock in your trajectory for years to come

Participants:

• Ryan Welsh – Field CTO – Generative AI, Qlik

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

Rapid7's Vice President of Data and AI Laura Ellis shares how they built an AI-first cybersecurity platform by investing in AI platform AND data infrastructure simultaneously.

Topics Include:

• Rapid7 processes massive cybersecurity data across exposure management, threat detection, and managed SOC.
• 84% of security analysts want to quit due to data overload burnout.
• Challenge: investing in AI platform AND data infrastructure simultaneously, not sequentially.
• Built security data lake with AWS, unified IDs, and standardized schemas across products.
• Used traditional machine learning for 10 years before generative AI emerged.
• Generative AI raised questions about business impact; agentic AI enables full automation.
• Chose AWS for scale, model marketplace flexibility, and true partnership on capacity.
• Co-development incubator with SOC team proved critical: equal responsibility, full-time collaboration.
• Launched alert triage automation, SOC assistant chatbot, and incident report generation tools.
• Built AI platform with guardrails after pen testers generated cookie recipes costing money.
• One agentic feature initially cost-estimated at $140 million before optimization and guidance.
• Future: more AI features, granular customer configuration, and bring-your-own-model capabilities.

Participants:

• Laura Ellis – Vice President, Data & AI, Software Engineering, Rapid7

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

Industry leaders from Coder, Scale AI, and Suger reveal why 95% of AI pilots fail—and share the frameworks that actually work to get agents into production.

Topics Include:

• Panel features leaders from Coder, Scale AI, and Suger discussing agentic AI.
• MIT report reveals 95% of AI pilots fail to reach production.
• Challenges are rarely technical—they're organizational, mindset, and people-driven instead.
• Companies lack documented tribal knowledge needed to train agents effectively.
• Many organizations attempt AI where deterministic, rules-based automation would work better.
• "Freestyle agents" concept: Some problems shouldn't be solved by agents at all.
• Regulated industries struggle when asking agents to handle highly differentiated, complex tasks.
• Common mistakes: building one universal agent or separate agents for every use case.
• Post-billing workflows and business-critical operations aren't ready for AI's black box.
• VCs pressure companies to define "AI-native"—but nobody has clear answers yet.
• Scale AI uses five maturity levels; Coder uses three tiers for adoption.
• Success metrics span operational readiness, business impact, and technology performance indicators.
• Production requires data governance, context, A/B testing, and robust fallback mechanisms.
• Even Anthropic uses agents conservatively: research tasks and log triage, no write-access.
• Path to 50% success requires agile frameworks, people change, and proper AI talent.

Participants:

• Ben Potter - VP of Product, Coder
• Raviteja Yelamanchili - Head of Solutions Engineering, Scale AI
• Jon Yoo - CEO, Suger
• Adam Ross - US, Partner Sales Sr. Leader, Amazon Web Services

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

Coralogix CEO Ariel Assaraf reveals how their observability lake lets companies own their data, reduce costs, and use AI agents to transform monitoring into actionable business intelligence.

Topics Include:

• Coralogix solves observability scaling issues: tool disparity, sprawling costs, limited control.
• Streama parses data pre-ingestion; DataPrime queries directly on customer's own S3 buckets.
• AI will generate massive unstructured data, making observability challenges exponentially worse.
• CTOs should ask: Can observability data drive business decisions beyond just monitoring?
• Observability lake lets you own data in open format versus vendor lock-in.
• OLLI designed as research engine, not another natural language database interface.
• Ask business questions like "What's customer experience today?" instead of technical queries.
• Trading platform unified tools, reduced resolution time 6x, now uses for business intelligence.
• Future: Multiple AI personas, automated investigations, hypothesis-driven alerts without human prompting.
• AWS partnership enables S3 innovation, Bedrock models, and strong co-sell growth motion.
• Data sovereignty solved: customers control their S3, remove access anytime, own encryption.
• Business data experience will match consumer AI tools within two years fundamentally.

Participants:

• Ariel Assaraf – Chief Executive Officer, Coralogix
• Boaz Ziniman – Principal Developer Advocate - EMEA, Amazon Web Services

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

ISV leaders from Automation Anywhere, DataVisor, and Sumo Logic share battle-tested strategies for deploying AI agents at scale, including pricing models, proof of concepts and ROI.

Topics Include:

• Panel brings together ISV leaders from automation, fraud detection, and security operations.
• Companies rethinking entire business processes rather than automating incremental portions with agents.
• Start with immutable data before tackling real-time changing data in production.
• Intent for change must come from board, CEO, and customers simultaneously.
• Challenge: proving agent value beyond CSAT when internal teams block deployment.
• Sumo Logic measures Mean Time to Resolution, aiming to cut hours to zero.
• DataVisor cuts fraud alert resolution from one hour down to twenty minutes.
• Customers demand reliability as workflows shift from deterministic to probabilistic agent decisions.
• Automation Anywhere spent three years making every platform component fully agent-ready.
• Focus on business outcomes, not chasing every new model release each week.
• Human oversight still critical—agents are task-oriented and prone to hallucinations and drift.
• Humans validate agent findings, then let agents scale actions across hundreds instances.
• Pricing experiments range from platform-plus-consumption to outcome-based to decision-event models.
• Token pricing doesn't work due to varied data modalities and complexity.
• Next two quarters: more POCs moving to production with productive agents deployed.
• Future prediction: enterprise apps becoming systems of knowledge powered by MCP protocol.

Participants:

• Jay Bala - Senior Vice President of Product, Automation Anywhere
• Kedar Toraskar – VP Product Partnerships, DataVisor
• Bill Peterson - Senior Director, Product Marketing, Sumo Logic
• Jillian D'Arcy - ISV Senior Leader, Amazon Web Services

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

Learn how Coveo automated LLM migration like a "mind transplant," building frameworks to optimize prompts and maintain quality across model changes.

Topics Include:

• AWS and Coveo discuss their Gen-AI innovation using Amazon Bedrock and Nova.
• Coveo faced multi-cloud complexity, data residency requirements, and rising AI costs.
• Coveo indexes enterprise content across hundreds of sources while maintaining security permissions.
• The platform powers search, generative answers, and AI agents across commerce and support.
• CRGA is Coveo's fully managed RAG solution deployed in days, not months.
• Customers see 20-30% case reduction; SAP Concur saves €8 million annually.
• Original architecture used GPT on Azure; migration targeted Nova Lite on Bedrock.
• Infrastructure setup involved guardrails and load testing for 70 billion monthly tokens.
• Migrating LLMs is like a "mind transplant"—prompts must be completely re-optimized.
• Coveo built automated evaluation framework testing 20+ behaviors with each system change.
• Nova Lite improved answer accuracy, reduced hallucinations, and matched GPT-4o Mini performance.
• Migration simplified governance, enabled regional compliance, reduced latency, and lowered costs.

Participants:

• Sebastien Paquet – Vice President, AI Strategy, Coveo
• Yanick Houngbedji – Solutions Architect Canada ISV, Amazon Web Services

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