March 2026 Summaries

As the RSA Conference approaches, discussions in the cybersecurity landscape are shifting from traditional detection algorithms to the challenges posed by data gravity and the data explosion driven by AI. The conventional security architecture, dominated by SIEM solutions, faces scalability and cost issues due to increasing log volumes, leading many CISOs to explore open table formats like Apache Iceberg for constructing their own security data lakes. This shift signifies a move away from proprietary silos towards more flexible, decoupled architectures where data ownership remains with organizations, allowing vendors to compete on analytics capabilities. The evolution of the SIEM model is now focusing on high-context analysis, while the Open Security Lake model handles vast forensic data volumes. Confluent is addressing observability economics by offering advanced data streaming solutions that reduce costs and enhance data handling efficiency. Additionally, partnerships like that with SOC Prime introduce real-time threat detection capabilities via Apache Flink, enhancing the speed and effectiveness of security operations. This new approach promotes a smarter, open data supply chain where flexibility, real-time data inspection, and cost-effectiveness are prioritized.

As businesses transition from traditional batch-based ELT data pipelines to more efficient streaming-first architectures, the need for real-time data integration becomes increasingly critical. Historically, batch processing has been sufficient due to fewer data sources and manageable volumes; however, the evolving landscape of high-scale data movement, driven by AI and complex analytics, demands more robust solutions. Streaming architectures, centered around technologies like Apache Kafka, Iceberg, and Delta Lake, provide a scalable, reusable, and real-time data movement layer that addresses the limitations of traditional ELT, such as inefficiencies, complexity, and vendor lock-in. These modern architectures not only reduce operational costs by optimizing resource usage but also enhance data governance, quality, and accessibility, thus enabling organizations to innovate and respond proactively to market demands. By leveraging a unified data infrastructure, companies can bridge the gap between operational and analytical workloads, ensuring that high-quality data powers everything from basic dashboards to advanced AI systems, ultimately fostering innovation and growth.

Adopting streaming machine learning (ML) can be effectively achieved by integrating a high-value inference step into existing data flows, rather than overhauling entire platforms. This approach allows for a smooth transition from batch-processing to real-time decision-making, leveraging event-driven systems like Apache Kafka for low-latency scoring and immediate predictions. By embedding ML logic directly into stream processing pipelines and focusing on a single ML function rather than a full platform, organizations can validate the performance of real-time inference with minimal risk and complexity. This incremental migration ensures a stable system while enabling real-time insights, avoiding pitfalls like overengineering or unnecessary retraining loops. As organizations mature, they can gradually expand their ML capabilities, building on a solid foundation for future scalability and sophisticated ML pipelines.

Confluent Cloud for Government (CCG) has achieved FedRAMP Moderate authorization, allowing federal, state, local, and tribal government agencies, as well as supporting commercial organizations, to deploy enterprise-grade data streaming infrastructure rapidly while ensuring compliance with strict federal security standards. Unlike traditional self-managed Apache Kafka deployments, CCG provides a fully managed, cloud-native platform that eliminates the operational burden of infrastructure management, offering proven cost efficiency with significant savings and enterprise-grade reliability with a 99.99% uptime SLA. This platform facilitates the modernization of legacy systems, breaking down data silos, and delivering real-time citizen services, thereby transforming government operations by enabling real-time situational awareness, secure data sharing, and enhanced citizen experiences. The availability of CCG is immediate for agencies and contractors, with live demonstrations planned at the 2026 Public Sector Summit to showcase how Confluent is supporting government missions through AI operationalization and mission-critical capability delivery. Additionally, Confluent has been recognized in the 2025 Gartner Magic Quadrant for Data Integration Tools and is set to be acquired by IBM, highlighting its influence and growth in the data streaming platform category.

As data infrastructure scales, it directly impacts energy consumption, often outpacing business value, particularly in streaming systems like Apache Kafka. Sustainable data infrastructure aims to optimize performance while minimizing resource usage through efficient practices such as elastic scaling, incremental processing, and intentional data retention. This approach, known as GreenOps, focuses on resource efficiency rather than cost reduction, aligning infrastructure consumption with actual workload demands. GreenOps principles, such as right-sizing clusters, minimizing redundant data movement, and optimizing storage and network efficiency, are crucial for building sustainable streaming architectures. These practices help reduce energy waste and improve system reliability and predictability. While streaming systems have inherent potential for efficiency, they must be intentionally designed to avoid common inefficiencies like over-provisioning, duplicate pipelines, and excessive data retention. GreenOps complements traditional operational practices like FinOps by focusing on reducing unnecessary work, thereby creating a feedback loop that enhances both efficiency and cost-effectiveness without increasing operational complexity.

Confluent is actively preparing for a future where quantum computers could potentially compromise current cryptographic algorithms by aligning with the latest National Institute of Standards and Technology (NIST) Federal Information Processing Standards (FIPS) to develop a post-quantum cryptographic infrastructure. This preparation includes enhancing data-in-transit encryption by implementing TLS 1.3 as a standard and adopting a hybrid key exchange model that combines classical and quantum-resistant algorithms. For data-at-rest encryption, Confluent is relying on 256-bit symmetric cryptography, which is considered secure in the quantum era, and is exploring additional support for PQC-compliant keys, especially in Microsoft Azure environments. Confluent emphasizes the shared responsibility for security between themselves and their customers, ensuring that the latest post-quantum cryptographic features are accessible either by default or as optional enhancements. Additionally, the text briefly introduces the Kafka Copy Paste tool, which facilitates rapid migration to Confluent Cloud.

Queues for Kafka, now generally available on Confluent Cloud and soon to be on Confluent Platform with the Apache Kafka 4.2 release, introduces queue semantics directly into Kafka, addressing the challenges of maintaining separate messaging systems for streaming and task distribution. This innovation allows organizations to consolidate their messaging infrastructure, reducing operational overhead and total cost of ownership while maintaining the benefits of both traditional queues and Kafka. Key features include the introduction of share consumer APIs and share groups that enable elastic scaling beyond partition limits and per-message processing controls, such as acknowledgment, release, and renewal of messages. These capabilities allow Kafka to handle both streaming and queuing workloads on a single platform, leveraging Kafka's durability and scalability without the constraints of traditional consumer groups. The release is available on both Confluent Cloud, offering a fully managed experience with advanced monitoring and management tools, and Confluent Platform, which provides deep integration with UI visibility and advanced tuning options. The choice between using share consumer API or traditional consumer API depends on workload requirements, with the former catering to operational tasks requiring elastic scaling and the latter to analytical tasks requiring strict ordering.

Akshay Ben, a software engineer at Confluent, has spent over three years on the Product Security team, focusing on building core security features and accelerating product launches. Motivated by a desire to learn and prove himself in software engineering, Akshay chose Confluent for its challenging environment and the opportunity to work with driven colleagues. He is currently involved in transforming the Identity and Access Management organization to improve product development efficiency. At Confluent, he values the company's "Get Stuff Done" mentality and the culture of addressing conflicts and embracing diverse ideas. Akshay has gained numerous technical skills and continues to hone his engineering abilities, particularly emphasizing the importance of knowing what actions to prioritize, failing fast, and being nice. Excited by the potential of agentic AI, he looks forward to furthering his learning in that area. Confluent's fast-paced, remote-first environment fosters a sense of belonging and continuous personal and professional growth for Akshay.

As data architectures evolve, there is a significant shift from systems designed for historical reporting to those influencing future outcomes through real-time decisioning and autonomous data systems. Real-time decisioning involves evaluating live data and triggering immediate actions without human intervention, representing a system-level implementation of data-driven decision-making. Autonomous data systems enable continuous self-correction and adaptation via closed feedback loops, bridging the gap between event streams, automation, and artificial intelligence. These systems move beyond traditional batch processes to deliver immediate actions, meeting modern user expectations for instant gratification and personalization, while AI models integrated into data flows enhance decision-making speed and accuracy. Autonomous data systems, unlike automated ones, are resilient and context-aware, capable of adapting their behavior based on feedback, thus transferring agency from human operators to the system itself. This transition from manual operations to autonomous systems is facilitated by components like continuous data ingestion, real-time processing, decision logic, and automated execution. These systems are proving essential as digital ecosystems become increasingly interconnected, requiring immediate responses to state changes to maintain competitive advantage.