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

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

Daryl Martis of Salesforce and Rashna Chadha of AWS share how Amazon Bedrock integrates with Salesforce to enhance AI applications, highlighting the partnership's strategic benefits, AI model customization, and secure deployment options.

Topics Include:

• Introduction to Amazon Bedrock and Salesforce partnership.
• Overview of Amazon Bedrock as an API-based generative AI service.
• The strategic collaboration between AWS and Salesforce.
• Salesforce Data Cloud and its integration with Amazon Bedrock.
• Overview of Salesforce Einstein and its use of Amazon SageMaker.
• Recent AI launches between Salesforce and AWS, including Slack AI and MuleSoft.
• Use cases of AI services like Amazon Textract within Salesforce.
• Bringing Your Own Large Language Model (BYO LLM) with Bedrock.
• Foundation models offered by Amazon Bedrock (Anthropic, Cohere, Llama).
• Overview of security, privacy, and compliance in Bedrock AI services.
• Salesforce Data Cloud’s unified customer data and real-time AI capabilities.
• Bedrock’s support for custom AI model evaluation and metrics.
• Consumption models in Bedrock: on-demand vs. provision throughput.
• Bedrock’s agent capabilities for real-world applications like scheduling.
• Demo of using Amazon Bedrock models within Salesforce.

Participants:

• Daryl Martis – Director of Product Management, Einstein AI - Salesforce
• Rashna Chadha – AI/ML Specialist – Principal 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/isv/

Ashish Arora, Head of Engineering & Machine Learning, Product Analytics at Autodesk, shares how personalized AI-powered experiences and real-time data can drive product adoption, featuring insights into Autodesk’s transformation journey, leveraging machine learning, and delivering actionable recommendations to millions of users.

Topics Include:

• Real-time data description and examples
• Leveraging AWS for real time and generative AI services
• Autodesk’s journey leveraging AWS to transform architecture and deliver personalized insights
• Overview of Autodesk and product portfolio
• Utilizing data gathered for customers
• Personalized data-driven insights for customers on Autocad and other products
• Descriptive insights: providing usage data for customers
• Prescriptive insights: Making recommendations to customers based on their workflows
• Predictive insights: Using ML to recommend products and features
• Autodesk processes 100+ billion events across all products, delivered 350+ million insights to customers, served to 3.5 million customers
• Example walkthrough – Rachael
• Architecture of Autodesk data and insight process
• Leveraging LLMs – Sagemaker and Bedrock
• Bringing it altogether
• Session wrap up

Participants:

• Ashish Arora – Head of Engineering & ML, Product Analytics - Autodesk
• Brian Slater – Principal Solutions Architect – Amazon Web Services

A panel discussion on presenting on security and data to Boards of Directors, focusing on metrics, ROI, generative AI, securing intellectual property, and strategic board engagement.

Topics Include:

• What security metrics are most valuable for Board of Directors
• Articulating ROI of security program
• Quantifying benefits of security program
• Sales enablement of the security team
• Driving efficiencies within the business with generative AI and more
• Quantifying business and template-based reporting to Boards with Diligent software
• Leveraging consultants and 3rd parties to leverage messages to Board of Directors
• Managing and communicating data posture and risk management
• Working with data leadership, securing intellectual property
• Challenges of labelling data
• Educating boards on software architecture principles and generative AI
• High focus and techniques for securing IP
• Enrolling Boards with generative AI use cases and innovation
• Creating communities of excellence
• Gamifying security and generative AI to increase internal knowledge
• Q&A 1: Taking action on a summarized document
• Q&A 2: Mental model for evaluative SaaS partners
• Q&A 3: Using the board to influence budgetary decisions
• Session wrap up

Participants:

• Satheesh Ravala - Chief Technology Officer, Diligent
• Josh Blackwelder – Deputy CISO, Sentinel One
• Robert Huber - Chief Security Officer, Head of Tenable Research, President Tenable Public
• Karen Henlsey - Principal WW Data & Generative AI Strategist, AWS

Lacework’s VP and Head of Engineering Arash Nikkar, and Product and Security Software Engineer Teddy Reed, discuss their work with generative AI and Amazon Bedrock, focusing on threat detection, chat interface improvements, and the rapid development of AI-driven solutions.

Topics Include:

• Introducing Lacework with Arash Nikkar
• Working with AWS and Bedrock
• Focusing on threat detection
• Lacework ingests over a trillion events each day
• Composite alerts for detecting anomalies
• Developing and improving the chat interface
• Teddy Reed talking chat interface improvements
• Reviewing the architecture
• Fast engagement with generative AI / AWS Bedrock
• The rapid delivery with Bedrock – took 20% of expected time
• Best practice – build in time to review and verify responses
• Measuring customer’s engagement and return frequency and feedback
• Understanding customer sentiment and topic analysis
• Achieving 60% completed results and feedback
• Transparency of source, response
• Enabling assistant to have more access to data from LLM
• Using AWS’ model evaluator, raising the accuracy scores
• Moving everything over to Bedrock for multiple reasons
• Familiarity of AWS tools helpful for all development and security teams
• Always challenging assumptions – is chat interface the right interface?
• Q&A 1: Using vector database for analysis, using results as system prompt
• Q&A 2: Approaching cost-optimization and balancing ROI
• Q&A 3: Using as automated remediation for findings
• Q&A 4: Data source Bedrock in leveraging

Session wrap up

Participants:

• Arash Nikkar – VP, Head of Engineering – Lacework
• Teddy Reed – Product and Security Software Engineer - Lacework

Massimo Ghislandi from AWS interviews Frank Contrepois, Chief Innovation Officer of Strategic Blue and Dvir Mizrahi, Head of FinOps for WIX sharing perspectives and best practices for FinOps, implementation, challenges and future FinOps trends for software companies.

Topics Include:

• FinOps is a new approach to managing cloud finances, bridging the gap between finance, engineering, and business stakeholders
• The implementation of FinOps can take a top-down or bottom-up approach, requiring collaboration and a common language across teams
• Shifting the conversation from "costs" to "investment" and "efficiency" has been an effective strategy for driving cultural change
• Focusing on automation, governance, remediation, and anomaly detection can have a greater impact than just cost observability
• Educating finance teams on technical cloud concepts and vice versa is crucial for building trust and alignment
• The future of FinOps may involve expanding beyond just cloud to encompass all IT assets and aligning with a company's overall values and business objectives
• Potential changes in legislation or accounting practices related to cloud could drastically impact the FinOps landscape

Participants:

• Frank Contrepois – Chief Innovation Officer, Strategic Blue
• Dvir Mizrahi – Head of Financial Engineering (FinOps), WIX
• Massimo Ghislandi – ISV Marketing Leader, EMEA, AWS

We feature a panel of executives from ASAPP, Glean, Smarsh, Socotra and AWS sharing essential strategies, including the role of Generative AI, trust-building, and addressing legal challenges to secure executive buy-in for transformative initiatives.

Topics Include:

• Introductions to panel
• Example of ideal Executive Seller
• Depth of business acumen and problem solving are important skills for Executive Sellers
• Framing a customer problem and create the argument
• Making selling to a C-Suite a team sport
• Keeping sellers and produce C-suite relevant
• Revolution and renaissance positioning
• Trust with the C-suite – do salesperson have a texting relationship w decision makers?
• How is Generative AI driving customer’s satisfaction and dissatisfaction?
• Data is the new oil
• Defining the business problem for insurance industry
• Helping customers reimagine business with Generative AI
• 80% of customers report the chatbot makes them mad
• The data journey is the precursor to the Generative AI journey
• The top unique legal challenges with Generative AI
• Legal now brought into early stage of sales process
• Turning legal concerns into an opportunity
• The book “Never Split the Difference” by Christopher Voss
• Calls to action from each panellist
• Session wrap-up

Participants:

• Daniel Rood – Senior Vice President Marketing, ASAPP
• AJ Tennant – Vice President Sales & Success, Glean
• Neva DePalma – General Council, Smarsh
• Ekine Akuiyibo – Chief Business Officer, Socotra
• Lauren Larscheid – Sr. Sales Leader, Business Applications, AWS

Today Anthropic’s Zach Witten takes us on a deep dive into Anthropic’s cutting-edge AI models—Claude Haiku, Sonnet, and Opus—exploring their safety-first approach to generative AI and sharing essential tips for prompt engineering.

Topics Include:

• Introductions, about Anthropic
• 3 models: Haiku, Sonnet and Opus
• Scaling laws for hardware, data and compute
• Competing to be safest AI solutions, safety-first organization
• Leader in jailbreak resistance
• Interpretability features and breakthroughs for AI models
• Basics of prompt engineering
• Improving prompts with Claude
• Details matter – small changes to spelling, context will greatly improve results
• System prompt – role setting will improve results (i.e. “You are an expert mathematician…” for math query
• Be clear and direct – use XML tags where possible
• Encourage Claude to think step-by-step – answering fast comes with accuracy risk
• Use examples to provide additional clarity to Claude
• Bonus tips for image-based prompt engineering
• Q&A 1) Who wrote the meta-prompts in the cookbook?
• Q&A 2) Guidance for writing prompts for prompt generator
• Q&A 3) Best practices for tabular and structured data
• Q&A 4) Maintaining “tone” across hundreds/thousands of responses
• Q&A 5) Reverse engineering a prompt