Customer Data Validates What Security Teams Always Suspected

We ran the numbers twice because we couldn't believe them.

During our early evaluation, we discovered that SIEMs of all generations take 30 times longer to onboard data compared to our early work with Databricks. We literally didn't have baseline data for how bad the current state was.

The security industry has been selling you solutions that can't handle basic database operations from the 1970s.

The Great Expectation Reset

SIEMs of all generations helped us fall short at scale. Too much effort went into proprietary engines instead of integration and content that actually enables security outcomes.

They locked data into silos while claiming to democratize it.

Sales and marketing trained customers to lower their expectations because the legacy tech simply can't handle what should be standard functionality. When we show CISOs that their million-dollar SIEM can't perform basic joins at scale, the reaction is always the same.

They were convinced by marketing that performance wasn't important.

We validate that their expectations are actually reasonable. Instead of trying to talk customers out of using the product properly, we want them to actually use it.

The Demo That Changes Everything

Speed gets attention, but it's not the real breakthrough.

When skeptical security teams ask us to prove our claims, we show them live. The moment that changes everything is when we demonstrate something they simply can't do.

We normalize hundreds of thousands of employees with multiple user accounts and IDs across hundreds all raw log streams into a single user activity view.

Yes commoditizing what was once rare air.

This is where traditional systems completely break down.

The technical solution is embarrassingly simple: joins. That's a concept from relational databases in the 1970s.

Even with millions of new rows per second and tables with hundreds of thousands of rows for enrichment, the join latency is negligible on our Databricks-powered platform.

The joins are one example, but it's not just that. Separation of storage and compute with cloud-native scaling allows us to scale to the job, not the ingest volume.

We don't deploy based on gigs per day. We scale based on your machine and human analytic workload, something that changes minute to minute.

This permits a game-changing consumption cost model. You control spend without giving up responsiveness.

While the security industry obsessed over proprietary engines, the solution was sitting in proven database fundamentals that have worked for decades.

Proof Points

Our partnership with Databricks delivers measurable improvements:

Data onboarding: Days instead of months, thats from 9 months to 5 days. Read the full case study by A.Team

Analytic Efficiency: Less compute than SIEMs of all generations, 17.7x compared to a major on premise SIEM, 25x compared to a major cloud SIEM.

Storage efficiency: 30-50% savings with Delta Lake architecture, compared to 3 leading SIEMS.

Analytics throughput: 25x traditional systems

The Databricks Lakehouse delivers.

Implementing the Lakehouse architecture for cybersecurity has enabled us to deliver previously unattainable results. The architecture fundamentally enables the semantic layer strategy at scale, making it accessible to all organizations from small businesses to large enterprises, MSPs and governments, and everyone in between.

Our Claims Are Not Just Claims

• A. Team - Read the white paper

• DARPA has chosen to fund ziggiz.ai after a independent technical review to accelerate engineering and operationalize our market leading entity enrichment solution for critical identity and asset data.

• Built on Databricks as a leading Cyber Security partner

• Palantir - selected ziggiz.ai for their CEO startup fellowship tranche 000. Leveraging the Cyber Lakehouse for business decision support focused on physical asset management.

The Architecture That Actually Works

Our platform leverages four key Databricks technologies that SIEMs of all generations can't match:

Apache Spark for distributed analytics at scale

Unity Catalog for governance without vendor lock-in

Delta Lake for reliable storage with time-travel capabilities

The sentence should be split into two separate sentences: "Unlike other security monitoring systems that become slow when searching through current and old data. Our system quickly analyzes both real-time and historical information at the same time."

We enable risk-free migration through parallel operation with existing systems. No rip-and-replace required. No vendor lock-in. No artificial limitations.

If needed, we can help maintain your old platforms while you transition. We leverage semantic layers to ensure necessary data streams continue supporting your current use cases.

ziggiz brings the data to the Cyber Lakehouse and the outcomes for security

Tired of being forced to start over? Our approach delivers net new without breaking the bank or the old architecture. Transition over time or quickly focus on new outcomes or cost reduction, blend both your choice.

ziggiz knowledge A subscription service delivering semantic models to organize data into clear, context-rich graphs, enabling source-agnostic exploration and consistent understanding across security data ecosystems.

ziggiz courier A data collection solution gathering, transporting, and performing analytics at the edge while preserving full data context for downstream analytics and compliance-ready storage.

ziggiz vault A resilient, enterprise-grade storage solution built for security data, enabling analytics-ready execution in open format while ensuring regulatory compliance through data governance, granular access controls, high availability, and disaster recovery.

ziggiz machine An adaptive analytics platform for real-time threat detection, agent-based and advanced statistical analysis. The system processes essential data and selects optimal analytic engines for each task, delivering precise results in milliseconds while conserving compute resources.

ziggiz discovery A just-in-time analytic engine supporting human-driven investigations, enabling analysts to select specific data based on mission context. The system delivers consistent, relevant data to preferred tools, from data science notebooks to security SIEMs and link analysis platforms.

What Happens When Security Teams Get Their Lives Back

When we implement this platform and security teams can suddenly use their tools the way they always thought they should work, something remarkable happens.

They go to lunch on time. They go home and see their kids' ball games. They check their email without panic.

Giving people their time back is the most rewarding part.

CFOs gain cost transparency and measurable ROI. CIOs escape vendor lock-in and integration complexity. CISOs achieve comprehensive oversight and regulatory compliance.

Security operations teams benefit from streamlined automation, reduced false positives, and the ability to focus on actual threats instead of wrestling with their tools.

ziggiz Cares

Security outcomes are currently limited to those who can afford them and remain challenging for vulnerable populations. We offer our solutions at cost to nonprofit healthcare organizations, enabling better protection for society's most vulnerable members during their most critical moments.

Built for Security Professionals

SOC Analyst: Pivot from detection into all of the security telemetry. No more context switching between tools.

Hunter: Workbook experience and data tools built for someone just like you. Hunt across unified data without artificial boundaries.

Detection Engineer: Brittle detections with fragile embedded include and exclude logic is a thing of the past. Build robust, maintainable rules.

Everyone: Data you can huddle around, investigate, decide and act. Collaborative security that actually works.

The Real Partnership Story

Our strategic partnership with Databricks represents more than technical integration. We're commoditizing the Cyber Lakehouse and establishing industry-standard semantic layers.

This transforms security from a cost center into an innovation driver.

The platform extends beyond security to support multiple business functions, creating unified analytics across the entire organization. When security data architecture is done right, it becomes the foundation for enterprise-wide intelligence.

We're not just announcing a partnership. We're validating that the security industry's fundamental problems have fundamental solutions.

Built on Databricks, we're the first to properly harness the Lakehouse architecture for enterprise security as a service. The foundation was there. We made it work.

Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Defense Advanced Research Projects Agency (DARPA).