Olena Kutsenko

Olena Kutsenko

Olena is a Staff Developer Advocate at Confluent and a recognized expert in data streaming and analytics. With two decades of experience in software engineering, she has built mission-critical applications, led high-performing teams, and driven large-scale technology adoption at industry leaders like Nokia, HERE Technologies, AWS, and Aiven. A passionate advocate for real-time data processing and AI-driven applications, Olena empowers developers and organizations to use the power of streaming data. She is an AWS Community Builder, a dedicated mentor, and a volunteer instructor at a nonprofit tech school, helping to shape the next generation of engineers. As an international speaker and thought leader, Olena regularly presents at top global conferences, sharing deep technical insights and hands-on expertise. Whether through her talks, workshops, or content, she is committed to making complex technologies accessible and inspiring innovation in the developer community.
Streaming Systems, Hidden Risks, And AI-driven Consequences
AI Coding Summit LondonAI Coding Summit London
Upcoming
Streaming Systems, Hidden Risks, And AI-driven Consequences
Modern AI systems don’t just rely on static datasets—they depend on continuous streams of real-time data to train, update, and make decisions. But what happens when that data can’t be trusted?
In this talk, we explore how streaming data pipelines—often built on systems like Apache Kafka—are becoming a critical and undersecured attack vector for AI-driven applications.
Rather than targeting models directly, attackers can manipulate the data flowing into them. By injecting, modifying, or replaying events in real-time streams, adversaries can:
- Poison training data and degrade model accuracy over time
- Manipulate real-time features used in fraud detection or recommendation systems
- Trigger unintended behaviors in downstream AI systems
- Quietly influence decisions without ever touching the model itself
We’ll examine how these attacks work in practice, from subtle data drift manipulation to targeted event injection, and why they are difficult to detect using traditional security tools.
The talk will break down the weak points in modern data pipelines:
- Lack of validation and trust boundaries in event streams
- Over-reliance on infrastructure-level security (encryption, ACLs)
- Blind spots in monitoring data integrity and semantic correctness
We’ll also explore how these risks evolve in systems that continuously retrain or adapt, where corrupted data doesn’t just affect a single decision—but becomes embedded in the model itself.
Finally, we’ll discuss defensive strategies that go beyond securing infrastructure: treating data as an attack surface, implementing validation and anomaly detection at the data level, and designing pipelines that can detect and recover from adversarial inputs.
This talk offers a new perspective on AI security - not by focusing on models, but on the data pipelines that feed them, where some of the most impactful and least visible attacks can occur. 
Apache Kafka Simply Explained With TypeScript Examples
JSNation 2023JSNation 2023
27 min
Apache Kafka Simply Explained With TypeScript Examples
Top Content
You’re curious about what Apache Kafka does and how it works, but between the terminology and explanations that seem to start at a complex level, it's been difficult to embark. This session is different. We'll talk about what Kafka is, what it does and how it works in simple terms with easy to understand and funny examples that you can share later at a dinner table with your family.
This session is for curious minds, who might have never worked with distributed streaming systems before, or are beginners to event streaming applications.
But let simplicity not deceive you - by the end of the session you’ll be equipped to create your own Apache Kafka event stream!