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Benjamin Flast, Director, Product Management at MongoDB discusses vector search capabilities, integration with AWS Bedrock, and its transformative role in enabling scalable, efficient, and AI-powered solutions.

Topics Include:

• Introduction to MongoDB's vector search and AWS Bedrock
• Core concepts of vectors and embeddings explained
• High-dimensional space and vector similarity overview
• Embedding model use in vector creation
• Importance of distance functions in vector relations
• Vector search uses k-nearest neighbor algorithm
• Euclidean, Cosine, and Dot Product similarity functions
• Applications for different similarity functions discussed
• Large language models and vector search explained
• Introduction to retrieval-augmented generation (RAG)
• Combining external data with LLMs in RAG
• MongoDB's document model for flexible data storage
• MongoDB Atlas platform capabilities overview
• Unified interface for MongoDB document model
• Approximate nearest neighbor search for efficiency
• Vector indexing in MongoDB for fast querying
• Search nodes for scalable vector search processing
• MongoDB AI integrations with third-party libraries
• Semantic caching for efficient response retrieval
• MongoDB's private link support on AWS Bedrock
• Future potential of vector search and RAG applications
• Example use case: Metaphor Data's data catalog
• Example use case: Okta's conversational interface
• Example use case: Delivery Hero product recommendations
• Final takeaways on MongoDB Atlas vector search

Participants:

• Benjamin Flast - Director, Product Management, MongoDB

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Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV

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David Gildea of Druva shares their approach to building cost-effective, fast generative AI applications, focusing on cybersecurity, data protection, and the innovative use of LLMs for simplified, natural language threat detection.

Topics Include:

• Introduction by Dave Gildea, VP of Product at Druva.
• Focus on building generative AI applications.
• Emphasis on cost and speed optimization.
• Mention of Amazon's Matt Wood keynote.
• AI experience with kids using "Party Rock."
• Prediction: GenAI as future workplace standard.
• Overview of Druva's data security platform.
• Three key Druva components: protection, response, and compliance.
• Druva's autonomous, rapid, and guaranteed recovery.
• Benefits of Druva’s 100% SaaS platform.
• Handling 7 billion backups annually.
• Managing 450 petabytes across 20 global regions.
• Druva’s high NPS score of 89.
• Introduction to Dru Investigate AI platform.
• Generative AI for cybersecurity and threat analysis.
• Support for backup and security admins.
• Simplified cybersecurity threat detection.
• AI-based natural language query interpretation.
• Historical analogy with Charles Babbage’s steam engine.
• "Fail upwards" model for LLM optimization.
• Using small models first, escalating to larger ones.
• API security and customer data protection.
• Amazon Bedrock and security guardrails.
• Testing LLMs with Amazon’s new prompt evaluation tool.
• Speculation on $100 billion future model costs.
• Session wrap up

Participants:

· David Gildea - VP Product Generative AI, GM of CloudRanger, Druva

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Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV

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Urmila Kukreja of Smartsheet and Nick Simha of AWS discuss leveraging Amazon Q’s Retrieval-Augmented Generation (RAG) solution to enhance productivity by enabling employees to quickly access relevant information within secure, integrated workflows like Slack, improving efficiency across the organization.

Topics Include:

• Introduction by Nick Simha, AWS.
• Overview of Amazon Q’s role in data analytics and Gen AI.
• Gen AI’s impact on productivity, ~30% improvement backed by Gartner study findings.
• General productivity improvement seen across various departments.
• Amazon Q’s developer code generation tool – rapid development
• Gen AI and LLMs’ challenges: security, privacy, and data relevance.
• Foundation models lack specific organizational knowledge by default.
• Empowering Gen AI to grant system access can cause issues
• Privacy concern: Sensitive data, like credit card info, can be central in data breaches
• Compliance is critical for organizational reputation and data integrity.
• Data integration techniques: prompt engineering, RAG, fine-tuning, custom training.
• RAG (Retrieval Augmented Generation) balances cost and accuracy effectively.
• Implementing RAG requires complex, resource-heavy integration steps.
• Amazon Q simplifies RAG integration with "RAG as a service."
• Amazon Q’s Gen AI stack overview, including Bedrock and model flexibility.
• Amazon Q connects to 40+ applications, including Salesforce and ServiceNow.
• Amazon Q respects existing security rules and data privacy constraints.
• Plugin functionality enables backend actions directly from Amazon Q.
• All configurations and permissions can be managed by administrators.
• Urmila Kukreja from Smartsheet explains real-world Q implementation.
• Smartsheet’s Ask Us Engineering Slack channel: origin of Q integration.
• Q integration in Slack simplifies data access and user workflow.
• "Ask Me" Slack bot lets employees query databases instantly.
• Adoption across departments is high due to integrated workflow.
• Future plans include adding data sources and personalized response features.
• Session wrap up

Participants:

• Urmila Kukreja – Director of Product Management, Smartsheet
• Nick Simha - Solutions Architecture Leader - Data, Analytics, GenAI and Emerging ISVs, AWS

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Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV

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Harold Rivas – Chief Information Security Officer at Trellix, discusses the role of generative AI in cybersecurity, focusing on Trellix's adoption of AI for threat detection and model governance, while emphasizing the importance of privacy, responsible innovation, and cross-functional collaboration.

Topics Include:

• Introduction to generative AI and its impact on cybersecurity
• Harold’s background in financial services and cybersecurity roles
• Trellix’s focus on product feedback through the Customer Zero Program
• Overview of machine learning's role in anomaly detection at Trellix
• Development of guided investigations to assist security operations teams
• Generative AI's growing importance in cybersecurity at Trellix
• Launch of Trellix WISE at the RSA Conference in 2024
• Addressing the overload of security alerts with AI models
• Integration of various AI models like Mistral and Anthropic
• Reducing anomalies and workload for security operations teams
• Importance of privacy in generative AI adoption and data governance
• Challenges with GDPR and CPRA regulations in AI implementation
• Focus on privacy frameworks like the NIST Privacy Framework
• Need for multi-stakeholder involvement in AI governance
• Discussion on model governance inspired by financial services practices
• Importance of inventorying and testing AI models for security
• Benefits of an AI Center of Excellence (AICOE) within organizations
• Model governance in generative AI for regulatory and business outcomes
• The impact of AI on labor, jobs, and decision-making processes
• Addressing cyber risk and threat modeling in AI environments
• The double-edged sword of AI in offensive and defensive cybersecurity
• MITRE Atlas framework's role in AI-driven cybersecurity strategies
• Potential negative consequences. Auto dealership hacked – Chevy Tahoe sold for $1
• Importance of vulnerability management and developer training
• Evolution of AI security tools and responsible use of generative AI
• Collaboration, governance, and agility in AI adoption across organizations
• Q&A 1: Outcomes and responsibilities an generative AI COE should have?
• Q&A 2: Model governance and financial implications
• Q&A 3: CISO response to model development, compliance and learning with customer data
• Q&A 4: Thoughts and suggestions for rating systems for models
• Q&A 5: Selecting and evaluating models
• Q&A 6: Advice and experience for model deployment and technical controls
• Q&A 7: Human reviewing AI responses to ensure accuracy
• Q&A 8: Will AI help avoid major outages in the future?
• Q&A 9: How to test and see maturity of models?
• Session wrap up

Participants:

· Harold Rivas – CISO at Trellix

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Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV

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Executive leaders from Arctic Wolf, Docker and Illumio share insights on fostering a strong security culture, balancing innovation with security, and addressing challenges in data protection and AI model development.

Topics Include:

• Overview of security culture in different company teams
• Importance of guidelines and secure IT infrastructure for AI models
• Challenges of accessing customer data while maintaining security
• Need for anonymization in early AI model development
• Docker's open-source ecosystem and security integration
• Dogfooding own products to ensure product reliability and trustworthiness
• Illumio’s high customer trust and responsibility for strong security practices
• Balancing security awareness with development speed at Illumio
• Gamifying security training to increase awareness
• Interlocking with customers to enhance security understanding for developers
• Embedding security into the development process from the start
• Illumio's approach to security in agile, cloud-native development
• Adapting customer success strategies for evolving security needs
• Rise of non-developers using AI in enterprises
• Educating business leaders on security best practices
• Scaling customer enablement and education through community engagement
• Challenges of placing security responsibilities in the developer workflow
• Arctic Wolf’s AI strategy for secure development
• Use of anonymized data in secure AI model training
• Generative AI’s potential to augment human creativity and efficiency
• Panelists' views on private AI and segmented model development
• Measuring security culture progress with gamification and development metrics
• Addressing human factors in cybersecurity and social engineering threats
• Emphasizing resiliency and containment in preventing widespread cyberattacks.
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Participants:

• Dean Teffer – Vice President of Artificial Intelligence, Arctic Wolf
• Dixie Dunn – VP of Customer Success, Docker
• Mario Espinoza – Chief Product Officer, Illumio
• Brian Shadpour – General Manager, AWS

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Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV

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Hear the generative AI journeys of Cohere, Epiq, and Forcura, including their market assessments, use case prioritization, responses to ethical and security considerations, while discussing generative AI's impact on healthcare, legal industries, and business applications.

Topics Include:

• Panel Introductions by David Cristini
• Where is Focura at in their AI journey
• Summary of Epiq’s AI journey to date
• Cohere’s AI journey to date
• Where did each company begin and assessing the market opportunities
• Prioritizing of use cases for Epiq
• Focura’s quick focus and results with generative AI
• Simplifying healthcare and improving patient experience with generative AI
• How do experiments and proof of concepts develop into production?
• Indicators that Cohere uses to identify customers ready to move fast
• Usecases that allows Forcura customers to move forward
• Guidance on engaging the Executive Team – getting Executive alignment
• How are legal and healthcare customers responding to AI solutions and challenges
• Changes of priority from customer advisory panels
• Evolving questions and concerns of functionality and data
• Some customers reporting AI evaluation is slowing them down
• Usecases that are easier to start off with to gain trust and traction
• Dealing with AI concerns of ethics, security and privacy – managing objections
• Understanding ethics concerns – privacy can often be about where data resides
• Customers often want “traceability”
• Accuracy and reducing hallucinations – AI comes with risk, business have to decide on business risk
• Future facing – what are we excited about?
• Generative AI is excellent at translation services – ROI is excellent
• Business applications and social impact of generative AI

Participants:

• MaryAnn Wofford – VP of Sales, Cohere
• Paul O'Hagan - Senior Director Product Management – AI Platform, Epiq
• Annie Mueller Erstling – COO, Forcura
• David Cristini - Director, ISV Sales North America, AWS

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

Register here for AWS re:Invent 2024, Dec 2-6, Las Vegas, NV

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J.B. Brown, VP of Engineering at Smartsheet, shares how integrating Amazon Q with Smartsheet's flexible work management platform has streamlined productivity and enhanced employee support through AI-driven automation.

Topics Include:

• Introduction by J.B. Brown, VP of Engineering at Smartsheet.
• Story about improving productivity
• Context about Smartsheet as an enterprise-scale work management platform.
• Examples of Smartsheet use in healthcare, TV streaming, and small businesses.
• Focus on not changing how companies work, offering flexibility.
• Integration with popular enterprise tech stack tools like Okta and Slack.
• Automations in Smartsheet for notifications and data synchronization.
• Smartsheet’s customer base includes large enterprises and small businesses.
• Overview of Smartsheet’s scale: 15 million users and $1 billion revenue.
• Smartsheet’s employee support system, including 270+ "Ask Us" Slack channels.
• Mention of AWS and the introduction of Amazon Q Business.
• Building a Smartsheet Q Business app for streamlined employee support.
• Setting up an Amazon Q Business app with proprietary data sources.
• Implementation of Slack integration for Smartsheet employee support.
• Example of AI summarizing Slack threads for improved efficiency.
• Demo of Amazon Q Business outperforming human experts in knowledge retrieval.
• Emphasizing the value of reducing response time and decision-making delays.
• Future development plans: Smartsheet-Amazon Q connector.
• Using AI to interrogate and manage Smartsheet project data.
• Invitation to AI-minded Smartsheet customers to test the new connector.

Participants:

• J.B. Brown - VP of Engineering at Smartsheet

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

AWS's Shahid Mohammed and Darktrace's Michael Beck discuss how generative AI innovations are transforming cybersecurity by both enhancing defences and introducing new, sophisticated threat management strategies.

Topics Include:

• Shahid Mohammed introduces himself as a lead solution architect at AWS.
• Mike Beck is Global Chief Information Security Officer at Darktrace.
• Darktrace specializes in AI-driven cybersecurity solutions for digital environments.
• Darktrace secures multiple digital data pots: email, network, cloud, SaaS, and endpoint.
• The conversation focuses on innovation in cybersecurity through AI.
• Mike emphasizes the benefits of Gen AI despite its security risks.
• Gen AI enables more complex, targeted attacks against organizations.
• Attackers use Gen AI to tailor attacks through phishing and deepfakes.
• Gen AI increases phishing complexity by eliminating common detection cues.
• Data privacy risks arise when large models process sensitive business data.
• Businesses must be mindful of AI’s impact on data sovereignty and security.
• Shahid compares the cybersecurity space to an arms race due to Gen AI.
• Mike stresses the importance of choosing the right AI for each task.
• Darktrace uses unsupervised machine learning and Gen AI together for defense.
• AI is essential for scaling cybersecurity efforts given today's threat complexity.
• Darktrace relies on AWS cloud for compute power, scaling, and innovation.
• AWS infrastructure helps accelerate Darktrace's R&D and operations securely.
• Security leaders should implement Gen AI policies and training.
• Mike advises technical controls and monitoring for safe Gen AI use.
• Gen AI is here to stay, but businesses must handle its security implications carefully.

Participants:

• Michael Beck – Global CISO - Darktrace
• Shahid Mohammed – Solution Architect Manager – Amazon Web Services

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