Dr. Yanbing Li, Chief Product Officer at Datadog, outlines how the company has integrated AI and automation into its incident response framework, helping customers manage both traditional security challenges and emerging AI-specific risks.

Topics Include:

• Introduced talk about incident response and CISO liability
• Datadog founded 14 years ago for cloud-based development
• Platform unifies observability and security for cloud applications
• Current environment has too many fragmented security products
• SEC requires material incident reporting within four days
• Datadog's incident response automates Slack room creation
• Response team includes Legal, Security, Engineering, and Product
• System tracks non-material incidents to identify concerning patterns
• Real-time telemetry data drives incident management automation
• On-call capabilities manage escalation workflows
• Datadog uses own products internally for incident response
• Company focuses on reducing time to incident detection
• AI brings new risks: hallucination, data leaks, design exploitation
• Bits.ai launched as LLM-based incident management co-pilot
• Tool synthesizes events and generates incident summaries
• Bits.ai suggests code remediation and creates synthetic tests
• Security built into AI products from initial design
• Prompt injection prevented through structured validation approach
• Sensitive data anonymized before LLM processing
• Engineering and security teams collaborate closely on AI
• LLM observability becoming critical for production deployments
• Customers need monitoring for hallucinations and token usage
• Datadog extends infrastructure monitoring into security naturally
• Company maintains strong partnership with AWS
• Q&A covered Bits.ai proactive capabilities and enterprise differentiation

Participants:

Yanbing Li – Chief Produce Officer - Datadog

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

Sanjay Kalra of Zscaler and Randy DeFauw of Amazon Web Services explore the hidden dangers of generative AI security—from invisible text manipulation and deep fakes to data poisoning and dark AI models—while offering practical strategies for protecting your enterprise in this era of generative AI.

Topics Include:

• AI security threats grouped into data, malicious use, trust/safety
• Data security critical for SaaS-based AI services
• Model training data vulnerable to poisoning and manipulation
• GenAI lacks traditional data deletion capabilities
• Access controls difficult once data becomes model embeddings
• Prompt injection attacks becoming widespread, with libraries available online
• Deepfake scams increasing in sophistication and frequency
• AI enhancing phishing attacks with better written content
• Dark AI models emerging specifically for malicious purposes
• Model hallucinations being exploited for security attacks
• AI accelerating analysis of stolen data
• Shadow AI usage by employees poses security risks
• Existing vendor AI integration creating unexpected security challenges
• Fine-grained access controls essential for AI applications
• PII protection critical in both inputs and outputs
• Comprehensive prompt and response logging necessary
• Invisible text manipulation emerging in resumes and RFPs
• Model fine-tuning can compromise built-in security guardrails
• Multi-language inputs create new security considerations
• Competition-sensitive content requires careful AI management
• AI firewalls needed for input/output monitoring
• Regular security testing required for AI models
• AI compliance standards emerging globally
• Multi-modal AI creating new security challenges
• Browser isolation helping control AI application usage

Participants:

• Sanjay Kalra – Product Management at Zscaler
• Randy DeFauw – Senior Principal Solutions 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/

This illuminating conversation with CyberArk's SVP of Finance, Nili Serr-Reuven, reveals how the 25-year-old cybersecurity leader successfully transformed from a traditional software company to a SaaS business model in just five quarters - far faster than the industry standard of 2-2.5 years - while maintaining strong margins and customer trust throughout the transition.

Topics Include:

• Introduction to SaaS transformation challenges and opportunities.
• Tomaz Perc introduces Nili Serr Reuven from CyberArk.
• Overview of CyberArk's 25-year history and milestones.
• Transition from a perpetual model to SaaS.
• CyberArk's accelerated transformation in just five quarters.
• Challenges of shifting from product-centric to customer-centric.
• Importance of market research and peer consultations.
• Key role of cross-functional collaboration in success.
• Explanation of "swallowing the fish" in SaaS.
• Managing short-term revenue drops during SaaS transformation.
• CyberArk's 70% SaaS revenue share post-transformation.
• Impact of global economic challenges on business strategy.
• CyberArk's robust demand for identity security solutions.
• Strategic leadership's role in transformation execution.
• CyberArk's disciplined financial planning during uncertainty.
• Establishing KPIs like ARR and customer satisfaction.
• Managing rising cloud costs with FinOps practices.
• CyberArk's approach to pricing and packaging SaaS solutions.
• Leveraging acquisitions to speed up SaaS capabilities.
• Impact of transformation on CyberArk's finance department.
• Evolution of finance roles to support SaaS growth.
• Communication with investors during transformative periods.
• The importance of cultural shifts in transformation success.
• Continuous learning, transparency, and collaboration as cornerstones.
• Advice for future SaaS leaders: plan, communicate, adapt.

Participants:

• Nili Serr Reuven – SVP Corporate Finance, CyberArk
• Tomaz Perc – SaaS Business Lead, 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/