Unlock Real-Time AI with Streaming Data

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Real-time AI

Unlock Real-Time AI with Streaming Data

Secure Critical Data Streams with Enterprise-Grade Kafka Security

Govern, secure, and ensure the quality of your streaming data—so that you can improve the precision, relevance, and effectiveness of your AI initiatives.

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Kafka is fragmented, complex—and critical for real-time AI

Real-time AI needs real-time data. Use cases such as fraud detection, recommendation engines, dynamic pricing, and personalized customer interactions require fast, fresh data.

Kafka is the best way to stream this data—but without control, it turns into chaos

Real-time AI needs real-time data. Use cases such as fraud detection, recommendation engines, dynamic pricing, and personalized customer interactions require fast, fresh data.

Kafka is the best way to stream this data—but without control, it turns into chaos

Real-time AI needs real-time data. Use cases such as fraud detection, recommendation engines, dynamic pricing, and personalized customer interactions require fast, fresh data.

Kafka is the best way to stream this data—but without control, it turns into chaos

Real-time AI needs real-time data. Use cases such as fraud detection, recommendation engines, dynamic pricing, and personalized customer interactions require fast, fresh data.

Kafka is the best way to stream this data—but without control, it turns into chaos

Poor data quality breaks AI outcomes

Missing fields, duplication, and schema drift corrupt real-time inference and lead to bad decisions..

No governance means no trust

Teams build in silos, creating inconsistent topics and shadow data that no one owns.

Security gaps expose data

Most pipelines lack encryption, access controls, and audit trails—risking customer data and compliance.

The Fastest Path to an AI-Ready Kafka

In-House Solution

AI-Ready Kafka with Conduktor

Conduktor’s Encryption

Speed to Production

Months of dev work, setup, and ongoing maintenance

Deploy in days with built-in governance and security

Data Governance

Custom scripts, scattered tools, zero consistency

Centralized policies, schema enforcement, full visibility

Security & PII

Fragile access rules, no encryption, audit gaps

End-to-end encryption, role-based access, full audit logs

Team Efficiency

Engineers stuck fixing pipelines, not building AI

Self-service controls + automation = faster delivery

Operational Cost

Hidden costs from maintenance, compliance, and downtime

One platform, predictable cost, proven scale

Future Readiness

Difficult to adapt for new AI/ML use cases

Built to scale real-time AI workloads with trust and speed

Why Trust Conduktor for Kafka?

Implement + automate governance

Ensure high-quality data at the source.

Monitor Kafka pipelines + performance

Identify and resolve issues before they escalate.

Standardize autonomy

Enable teams to provision Kafka resources while enforcing centralized policies.

Align tech, teams, & processes

Never sacrifice security for innovation—have both.

Conduktor helps me daily with tracking all my Kafka topics, operations have been more smooth as we can detect quickly bottlenecks. I definitely recommend Conduktor to everyone, the affect on daily operations is huge.

Charles Emmanuel

Data Scientist at Intelligent Locations

Charles Emmanuel

Data Scientist at Intelligent Locations

Charles Emmanuel

Data Scientist at Intelligent Locations

Charles Emmanuel

Data Scientist at Intelligent Locations

Six Steps to Deliver
Trusted Streaming Data for AI

Data sources

01

Define Governance Standards

Platform, security, and architecture teams establish naming rules, schema contracts, and access policies.

02

Enforce Data Quality Policies

Platform, security, and architecture teams establish naming rules, schema contracts, and access policies.

03

Secure the Data Streams

Security teams apply encryption, role-based access, and audit logging across Kafka.

04

Monitor Data Flow and Health

SREs and DevOps track pipeline performance and catch issues before they impact downstream systems.

05

Enable Team Autonomy with Guardrails

Application teams self-serve Kafka resources while the platform team keeps central control.

06

Deliver High-Quality Data to AI Systems

ML and data teams rely on clean, real-time data streams for training and inference.

kafka

AI Systems

Real-World Use Cases for Streaming + AI

Kafka + Conduktor power the AI that runs on live data—where precision, speed, and trust are everything.

Real-Time Fraud Detection

AI needs instant access to transaction data to stop fraud before it happens. Conduktor ensures clean, secure, and compliant data flows—so threats don’t slip through

Security Threat Detection

AI must process login events, firewall logs, and user behavior as they occur. Conduktor gives you visibility and control over every stream feeding your detection systems.

Predictive Maintenance

AI relies on IoT telemetry to detect early signs of failure and avoid downtime. Conduktor enforces upstream data quality, so models run on accurate, trusted inputs.

Recommendation Engines

AI adapts instantly to real-time behavioral data to personalize offers and content. Conduktor manages behavioral streams with precision and policy—so every interaction counts.