To adapt to an ever-changing threat landscape, effective cybersecurity must align policy interpretation, operational enforcement, and real-time metrics in a continuous feedback loop. Generative AI has a large role to play in retrieving relevant data and coordinating and adapting this complex task across a disparate set of tools and information sources. In this session you will learn about different options for developing GraphRAG capabilities using Amazon Neptune that supply highly relevant connected data to agentic workflows, and how Deloitte used the AWS GraphRAG Toolkit to build a Security Intelligence Center that produces predictive security guidance based on timely organizational context.

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Organizations are creating and managing more data than ever. As stewards of this data, we are tasked with ensuring that it is highly available, secure from threats, and only accessible to those that it is intended for.

This session dives into the many areas that keep security officers awake at night, including: • Principle of least privilege • Data governance • Data compliance laws and regulations • Common exploits • Security best practices for developers • Encryption • Industry-specific security guidelines

As data platforms grow and evolve, the benefit of centralizing and standardizing security solutions is greater than ever. The frequency of data breaches has increased over time, and despite continuing to improve our security posture, the complexity and effectiveness of attacks continues to keep pace.

Data security is a key implementation of risk management. All organizations are targeted by cyber threat actors. Success is dissuading those malicious parties from persisting in their attacks. Knowing how to effectively layer security and create effective access methods between users and data will provide the highest chances of success given an ever-changing threat landscape.

Please note that we will be using Microsoft Teams for the online portion of this meeting. You may want to join a few minutes early to ensure you do not have any issues. If you are attending in person, there are large TVs at the office, and you do not need to bring a laptop or use Teams.

Supported by Our Partners • Sonar —  Trust your developers – verify your AI-generated code. • Vanta —Automate compliance and simplify security with Vanta. — In today's episode of The Pragmatic Engineer, I'm joined by Charity Majors, a well-known observability expert – as well as someone with strong and grounded opinions. Charity is the co-author of "Observability Engineering" and brings extensive experience as an operations and database engineer and an engineering manager. She is the cofounder and CTO of observability scaleup Honeycomb. Our conversation explores the ever-changing world of observability, covering these topics: • What is observability? Charity’s take • What is “Observability 2.0?” • Why Charity is a fan of platform teams • Why DevOps is an overloaded term: and probably no longer relevant • What is cardinality? And why does it impact the cost of observability so much? • How OpenTelemetry solves for vendor lock-in  • Why Honeycomb wrote its own database • Why having good observability should be a prerequisite to adding AI code or using AI agents • And more! — Timestamps (00:00) Intro  (04:20) Charity’s inspiration for writing Observability Engineering (08:20) An overview of Scuba at Facebook (09:16) A software engineer’s definition of observability  (13:15) Observability basics (15:10) The three pillars model (17:09) Observability 2.0 and the shift to unified storage (22:50) Who owns observability and the advantage of platform teams  (25:05) Why DevOps is becoming unnecessary  (27:01) The difficulty of observability  (29:01) Why observability is so expensive  (30:49) An explanation of cardinality and its impact on cost (34:26) How to manage cost with tools that use structured data  (38:35) The common worry of vendor lock-in (40:01) An explanation of OpenTelemetry (43:45) What developers get wrong about observability  (45:40) A case for using SLOs and how they help you avoid micromanagement  (48:25) Why Honeycomb had to write their database  (51:56) Companies who have thrived despite ignoring conventional wisdom (53:35) Observability and AI  (59:20) Vendors vs. open source (1:00:45) What metrics are good for  (1:02:31) RUM (Real User Monitoring)  (1:03:40) The challenges of mobile observability  (1:05:51) When to implement observability at your startup  (1:07:49) Rapid fire round — The Pragmatic Engineer deepdives relevant for this episode: • How Uber Built its Observability Platform https://newsletter.pragmaticengineer.com/p/how-uber-built-its-observability-platform  • Building an Observability Startup https://newsletter.pragmaticengineer.com/p/chronosphere  • How to debug large distributed systems https://newsletter.pragmaticengineer.com/p/antithesis  • Shipping to production https://newsletter.pragmaticengineer.com/p/shipping-to-production  — See the transcript and other references from the episode at ⁠⁠https://newsletter.pragmaticengineer.com/podcast⁠⁠ — Production and marketing by ⁠⁠⁠⁠⁠⁠⁠⁠https://penname.co/⁠⁠⁠⁠⁠⁠⁠⁠. For inquiries about sponsoring the podcast, email [email protected].

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