Project Name
Three Siloed Security Tools Unified Into One Real-Time Analytics Platform on Kafka, Spark, and MongoDB
A security operations team relied on three independent tools – firewalls, endpoint agents, and an email security gateway – each generating thousands of events per second in completely different formats. Analysts had to check three separate dashboards and manually correlate activity across them. By the time a phishing email, a suspicious process, and a blocked connection were connected by hand, the attack window had often already closed. Applying its AI-First approach, Ksolves delivered a unified, real-time platform that ingests, classifies, stores, and analyzes telemetry from all three sources in one place – eliminating the manual correlation gap that had been giving attackers their window.
- Fragmented Multi-Tool Visibility: Three siloed tools, three dashboards, and no way to correlate events across them. A phishing email, a suspicious endpoint process, and a blocked firewall connection could only be connected manually, long after the attack window had opened.
- Reactive Threat Detection: Related events across sources were only connected manually after the fact. By the time an analyst correlated a blocked connection with a malware alert and a phishing email, the attacker had already moved.
- Wildly Different Event Schemas: Firewall, endpoint, and email events share almost no common fields. No single rigid schema fits all three, forcing either multiple separate collections or schema compromises that produce NULL columns and wasted storage.
- High-Volume Ingestion Risk: Thousands of events per second from three sources needed reliable capture without loss during traffic spikes. Any dropped event was a potential missed threat indicator.
- Fault-Tolerant Stream Processing: Events had to be classified continuously with recovery from failures. A stream processing failure meant a gap in threat detection coverage - a direct security risk.
- Single-Node Storage Risk: A single-node store risked total data loss with no way to distribute load. If storage went down, events were lost, and attacks went undetected.
- Authenticated Multi-Node Cluster: Every node and client needed enforced authentication without breaking replication. Missing authentication had caused 1,400+ real MongoDB breaches - a security analytics platform could not be an unsecured target itself.
- No Unified Analytics Layer: No existing tool could query heterogeneous events for cross-source patterns. Analysts had no way to ask: which accounts had both a phishing email and a suspicious process in the same 10-minute window?
Ksolves designed a three-layer unified security analytics platform: Apache Kafka with three dedicated topics for reliable burst-tolerant ingestion from each source, PySpark Structured Streaming with three fault-tolerant pipelines for in-flight classification and idempotent writes, and a MongoDB sharded replica set with flexible schema for heterogeneous event storage and cross-source analytical queries. The governing principle: three sources, one platform, zero data loss, zero schema constraints.
- MongoDB Flexible Schema - 3 Event Types in ONE Collection: Each document carries only its relevant fields. Firewall events store connection and severity data. Endpoint events store hostname, process, and malware alert fields. Email events store sender, spam score, and phishing verdicts. Zero NULL columns, zero schema migrations needed across all three completely different event structures.
- Kafka 3 Topics and PySpark Structured Streaming 3 Pipelines: Kafka buffers bursts reliably across dedicated topics for firewall, endpoint, and email events. PySpark processes each topic with checkpointed, idempotent upserts into MongoDB - no data loss, no duplicate events regardless of failure or restart.
- 3-Node Replica Set With Automatic Failover: 1 Primary and 2 Secondaries. If the Primary fails, a new election completes in seconds with zero downtime. Events continue flowing and are never lost during failover. Storage downtime no longer means a security blind spot.
- Sharded Cluster With Hashed event_id Key: Data splits approximately 50/50 across 2 shards. Write load distributes automatically. Add more shards as infrastructure scales with no application redesign required - the platform grows with the security tooling estate.
- Auth and keyFile Hardening From Day One: Authentication enforced on every node. keyFile used for all internal cluster communication. Admin user configured on the config server from initial deployment - the security analytics platform is itself secured against the authentication gaps that caused 1,400+ real MongoDB breaches.
Technology Stack
| Category | Technology |
|---|---|
| Storage | MongoDB (Flexible Schema, 3 Event Types in One Collection) |
| Streaming | Apache Kafka (3 Topics, One Per Source) |
| Processing | PySpark Structured Streaming (3 Pipelines) |
| Resilience | MongoDB 3-Node Replica Set + Sharded Cluster |
| Security | Auth + keyFile Hardening |
- Three Siloed Tools Unified Into One Queryable Platform: Firewall, endpoint, and email security events from three completely different tools now ingest into a single MongoDB collection. Analysts query cross-source patterns in one place instead of manually correlating across three dashboards.
- Zero Data Loss During Ingestion Spikes: Kafka buffering and PySpark checkpointed idempotent upserts ensure zero events are dropped during burst traffic from any of the three sources. Every security event is captured reliably regardless of spike volume.
- Zero Downtime Storage With Automatic Failover: MongoDB 3-node replica set with automatic Primary election in seconds means storage failures no longer create security blind spots. Events continue flowing and are never lost during any node failure.
- Horizontal Scalability With No Application Redesign: Sharded cluster with hashed event_id distributes write load automatically across 2 shards. New shards can be added as the security tooling estate grows with no application code changes.
- Security Platform Hardened Against Its Own Attack Surface: Auth and keyFile enforcement from day one closes the authentication gaps responsible for 1,400+ real MongoDB breaches. The security analytics platform is itself secured against the class of attacks it is designed to detect.
“We had three security tools generating thousands of events per second and our analysts were manually checking three dashboards trying to connect them. By the time they correlated a phishing email with a suspicious process and a blocked connection, the window had closed. Ksolves gave us one platform where all three sources land together and we can query across them in real time.”
– Head of Security Operations / CISO.
A security operations team manually correlating events across three siloed tools with three different schemas and three separate dashboards, losing attack windows to manual correlation lag, was transformed through Ksolves Big Data services. A unified real-time security analytics platform on Kafka, PySpark Structured Streaming, and a hardened MongoDB sharded replica set now ingests firewall, endpoint, and email events from all three sources into one queryable collection. Zero data loss. Zero schema migrations. Automatic failover. Horizontal scalability. Cross-source threat correlation in real time. The platform is itself secured from day one against the authentication vulnerabilities it is built to detect.
Are your security analysts still correlating threats manually across three separate dashboards while the attack window closes?
