This talk was recorded at NDC Manchester in Manchester, England. 
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#applicationsecurity 

Data streaming is powering everything from fraud detection and real-time analytics to patient monitoring and order fulfillment. But as the role of streaming grows, so does the risk - because many streaming platforms, like Apache Kafka, aren't secure by default.

In this talk, we’ll take a practical look at data streaming security through the lens of Kafka, one of the most widely adopted streaming platforms in the world. We'll walk through what can go wrong - like Gamooga's public Kafka broker leaking 17GB of customer data in two hours, or GonnaOrder exposing years of real-time delivery info to the internet - and what it takes to do it right.

We'll cover the key pillars of securing a streaming system: encryption in transit and at rest, access control, monitoring, and key management. Along the way, we’ll look at the trade-offs like disk encryption vs. end-to-end encryption, what is behind field-level and envelope encryption, and the realities of using customer-managed keys in regulated industries.

We'll also explore how streaming security has evolved, how real vulnerabilities (like CVE-2019-12399) highlight the need for patching and monitoring, and what successful multi-layered security looks like in production - from financial institutions to healthcare platforms.

If you're building, running, or scaling streaming systems, this talk will help you see the security blind spots and give you concrete steps to protect the data flowing through your pipelines.

NDC Conferences

Comprehensive overview of security practices for data streaming systems, focusing on Apache Kafka, Flink, and Iceberg. Covers real-world security breaches (exposed healthcare chatbots, food delivery leaks, crypto mining attacks), encryption strategies (TLS, at-rest, topic-level, field-level, end-to-end), access control mechanisms (ACLs, authentication, authorization), network segmentation, and threat mitigation for data exfiltration, denial-of-service attacks, and AI/LLM prompt injection. Emphasizes situational awareness through monitoring, auditing, dependency management, and proper configuration to prevent common vulnerabilities in real-time data processing architectures.

Deep dive into data streaming security - Olena Kutsenko - NDC Manchester 2025