Hello, everyone. I'm Modi and I'm excited to talk with you today about how we can improve AI and network security using technologies like Kafka, secure proxies. and modern software architecture. As we depend more on data and connected systems, making sure they are secure and reliable becomes even more important. Today I'll go over some of the topics here and we'll understand more about AI and Kafka's presence in the AI. So introduction to AI and Kafka for real time data processing. And we'll go through the key features of Apache Kafka for AI workloads, the real time AI use case powered by Kafka, securing AI data pipelines with secure proxies, and how to integrate AI workloads with Kafka Connect, and how to implement zero trust architecture with Kafka. Before going this, we'll understand what is zero trust architecture. And a case study on AI and Kafka in action and we'll go through some best practices, industry best practices for building secure, scalable AI pipelines. Okay, let's start. So and again by the end of this talk, you'll see how these tools work together to create a strong foundation for AI and secure networking. Okay, let's get started. So the growing need for real time AI So as technology advances, there is a rising need for AI that operates in real time. If you notice, we are no longer just storing and processing data in large batches. Now, industries like finance, healthcare, manufacturing, and e commerce rely on AI to make instant decisions, anomaly detection, and natural processing language based on the live data. So think of fraud detection banking where AI systems need to flag unusual transactions as they happen or in a healthcare where real time AI can support doctors in making quick informed decisions during surgeries. When it comes to Kafka role, Apache Kafka is a powerful tool for handling high speed data streams in real time. Essentially, it acts as a central hub that gathers, stores, and distributes data continuously with high throughput, which is crucial for real time AI applications. In a real time AI, data needs to move from multiple sources, sensors, using user interactions, and internal systems. to the AI models instantly. Kafka allows this by processing data as it's created, enabling real time decisions. For example, in an e commerce application, Kafka can track every click and purchase in real time, so AI models can instantly recommend real time decisions. products or spot potential fraud and Kafka also supports secure and reliable data handling with features like data replication and fault tolerance. Kafka makes sure data flows smoothly, even when issues arise. And, there are like challenges in the traditional systems, right? So traditional systems are often built around batch processing, where data is collected, stored, and then processed at set intervals. So this approach for certain use cases But it's a, yeah, it's good for certain use cases, but it's a big limitation for the applications that need real time insights, right? for example, in fields like finance and healthcare, Delays in data processing can mean missed opportunities or even risk to security and safety. Another major challenge is scalability, right? So traditional systems can struggle to keep up as data volumes increase. often becoming slow and requiring costly hardware upgrades. This makes it hard to scale applications that rely on constant data flow, like live monitoring or, predictive analysis. Kafka addresses these challenges by using a distributed and scalable architecture that allows for real time event processing. talk about the key features of Apache Kafka for AI workloads. Apache Kafka is an ideal choice for AI applications. To support real time AI and advanced analytics, We need tools that can process and analyze data as it flows and without any delays, right? So that's where Kafka Stream Processing API and ksql come into play. Both are designed to handle real time data streams effectively, enabling instant data transformation, filtering, and analysis. So Stream Processing API and ksql. So let's. discuss more about this. So this feature Allows developers to create sophisticated data pipelines for AI tasks such as a real time sentimental analysis anomaly detection and predictive maintenance So when it comes to stream processing API, particularly, and this is a powerful API within Kafka that allows us to build custom applications that process data streams. With this API, we can perform complex operations like joining streams, aggregating data, or even transforming messages as they're mixed. For example, in a real time fraud detection system, The stream processing. A PA could be used to continuously analyze transaction patterns and detect anomalies as soon as they're occur. That's pretty much it. Pretty important, right? yeah. When it comes to K sql, K sql, which is a calf of query language, is a SQL like language, built for, Kafka that allows us to write streaming queries on live data. Case QL makes it easier. To work, streaming data since we don't have to write complex code Just like this straightforward sql commands to filter transform or join data for instance If you wanted to filter specific type of transactions in a financial application We could use ksql to pull only the data we need in real time without heavy processing. So together The Stream Processing API and ksql offers flexibility and simplicity for working with streaming data. They enable us to perform real time data transformations and analyze directly on Kafka, which is essential for us. for supporting real time AI applications that need, fast and fast accurate data insights. When it comes to integration with, data systems, Kafka Connect is a tool that is a part of cap Kafka ecosystem, which is pretty much designed to simplify and automate the process of streamlining data between Kafka and other data systems, like simplifying the data flow of AI applications. and, the big advantage of, using, the Kafka's approach in AI is, so it gives you a high throughput and low latency. Like Kafka itself is designed to handle large scale, data ingestion, like a millions of, messages. And it'll process. in a lightning time and slow latency enabling its enables real time processing latencies within below 10 milliseconds. So these are pretty good, the kafkas. Let's discuss about a real time AI use case, Kafka. as we earlier discussed about the anomaly detection, right? this time we'll go a little bit deeper. A little bit deeper about what this anomaly detection is and how this will be useful for our end to end, day to day lives by AI. anomaly detection is essential in today's AI driven systems, especially for applications in security, finance, and operations, where spotting unusual patterns is critical, right? Anomaly detection helps identify unexpected or abnormal events in data. Think like unusual login items, irregular financial transactions, or system performance issues. In a real time, AI, anomaly detection takes on even greater importance because it allows organizations to respond instantly. For example, in the cyber security, detecting an anomaly might mean identifying a potential attack. As it's happening, allowing for immediate action to mitigate threats or the financial services, real time anomaly detection can help flag fraudulent transactions before they're processed. So one of the, the other, real time use case Is, natural language processing, which is aka NLP. Kafka facilitates this, the real time processing of language data for applications such as, chatbots or voice assistants. on the social media platforms, right? So using this, natural language processing, integrating this lab, having this natural language processing with Kafka, it enables real time analysis and processing of text data streams. Open it's open up the possibilities for applications like, sentiment analysis. Contact, content link, content filterings, chatbots, and more in dynamic environments. By combining NLP models with Kafka's powerful data streaming capabilities, organizations can create scalable responsive systems that analyze test data as it flows through various sources. So the other, another important thing is a predictive maintenance. So AI models leverages its real time streaming capabilities to prevent when a failure occurs, thereby reducing the downtime and maintenance cost. Decision Supporting Systems aka DSS with Kafka, which enables organizations to make real time data driven decisions by processing, Analyzing and integrating information from various sources by utilizing Kafka's robust data streaming capabilities, decision support systems can gather and process information in a real time that helps. in a critical decision making across various domains like finance, healthcare, e commerce, and operations. for example, in a healthcare, real time AI processing can provide doctors with insights based on the patient data, assisting in diagnostic and treatment planning, right? this is very important, like additional support systems. With Kafka, we can create wonders using the help of AI. Let's talk about, the security, securing AI data pipelines with secure proxies. securing AI data pipelines with secure proxies is critical for, protecting sensitive information as it moves between different components in a distributed real time processing environment. It acts as intermediaries. It acts as an intermediary that enforces security policies, monitoring traffic, and masks underlying system details, ensuring that AI data pipelines are robust against attacks, unauthorized access, and data leaks. There are many examples of using secure proxies for securing AI data pipelines. And we can discuss some of them here. For instance, like compliance with data regulations. This helps organizations meet their private, meet privacy laws. For example, GDPR, General Data Privacy Regulation. And, CCPA California Consumer Privacy Act, then ensuring the sensitivity, the sensitive data is not, is handled securely throughout the pipeline. And one other example is the risk mitigation. while incorporating secure proxies, in a Kafka based CA system, it reduces the risk of data breaches. Nowadays, the big financial institutions and, other companies, they're focusing main on the data breach. We have seen many instances, the data got leaked. So considering that you using Kafka, we can avoid the situation. performance considerations, right? When it comes to performance, Like despite adding all these layers of security, secure proxies are optimized to ensure that there is no significant impact on data processing latency. Let's talk about integrating AI workloads with Kafka Connect. integrating AI workloads with Kafka Connect creates a powerful scalable solution for, streaming data into machine learning models and AI systems in real time. Kafka Connect as a. Character framework allows for seamless data movement between Apache Kafka and other data systems like databases. file storage and cloud services. This integration enables AI applications to leverage real time data flows, providing insights and actions based on the latest data without manual data movement. So by scaling these AI pipelines with Kafka Connect, we can achieve seamless AI pipelines, sorry, seamless data integration. So it allows organizations to easily connect AI workloads with external data systems, such as cloud data warehouses, data lakes, and, on prem databases. and one, one other, thing using AI pipelines with Kafka Connect we can achieve is the scaling improvements. Kafka Connect can deliver up to a 50 percent improvement in data scalability. and optimization, accommodating growing data volumes, pre built and custom connectors. So we can utilize a wide range of pre built connectors for a proper data sources or develop customer, custom connectors tailored for our specific use case. one real world example is that, a retail company integrated its machine learning models with Kafka Connect to enable. real time inventory management, which leads to significant reductions in stockouts and their, and the excessive inventory. Let's talk about implementing a zero, trust architecture with Kafka. So implementing zero trust architecture with Kafka is essential for securing, data flows in real time applications. and distributed systems. Zero trust assumes that every user, device, and application inside or outside the organization should not be trusted by default. So this approach enforces strict identity verification and continuous validation, ensuring that data integrity and privacy are maintained across Kafka based architectures. We can, enhance, security with zero trust architecture. Let's talk about some key items, zero trust security principles, primarily zero trust security principles. So employ a never trust always verify approach. So this approach, where every data access request is authenticated and encrypted. Okay. Like a continuous authentication, Kafka integrates with zero trust frameworks to ensure the data is continuously protected at every point in the pipeline. So middleman attacks, right? so enforcing by enforcing this encryption and secure authentication protocols, we can help, preventing the middle, middle man in the middle attacks and other security threats. Some of the, yep, some of the best practices, for building, secure scalable AI pipelines are listed here. So this guy, we can have different items listed here, which is like design recommendations. So using event driven and microservice architecture to manage the complexities of real time data processing, security considerations, incorporating, encryption, secure policies, and zero trust principles to protect the data throughout the pipeline. And as we already discussed about the performance optimization tips, so we can use, we can optimize the Kafka configurations, such as, topic partitioning and replication factors. To make sure it has a high throughput and low latency. That's pretty much important So when it comes to monitoring and alerting by while setting up the continuous monitoring and alerting for Pipelines performance and security event to detect and in response to issues promptly So the moment when when we set this proper alerting in case of any issues It will alert you on time and we can mitigate the risk. So on to conclude this, so integrating Apache Kafka with EA systems. secure proxies and zero trust principles, which creates a secure high performance data pipeline. So this combination allows real time data processing while ensuring only trusted users and devices access the data, protecting sensitive information and enabling AI applications to act on up to date available data. It's a powerful way to make data more secure, scalable, and responsive across systems. By adopting these modern architectures, organizations can enhance the accuracy and reliability of AI systems while maintaining robust data protection. Thank you so much.