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

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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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

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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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

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