Can data intelligence replace SIEM? Why the next security battle is being fought over your data lake

The cybersecurity industry is undergoing a foundational shift that few enterprises can afford to ignore. What was once dominated by static dashboards, rule-based alerts, and log aggregation is now turning into a race to harness data platforms and artificial intelligence. The recent debut of Databricks’ Data Intelligence for Cybersecurity suite has intensified the debate across enterprise IT and investor circles alike: are legacy Security Information and Event Management systems finally being replaced by unified, AI-driven data intelligence platforms?

For years, SIEM software like Splunk and IBM QRadar formed the bedrock of threat detection. Yet, as attackers adopt artificial intelligence to scale reconnaissance and social engineering, defenders are scrambling to unify petabytes of telemetry that live across clouds, networks, and endpoints. Increasingly, the platform doing that unification is not a traditional SIEM at all but a data lakehouse—most notably those built by Databricks, Snowflake, and Google Chronicle.

Why are legacy SIEM systems struggling to keep pace with AI-driven and cloud-native security threats?

The limitations of legacy SIEM architectures stem from the world they were built for: predictable on-prem environments with manageable event volumes. Their job was to centralize logs, correlate events, and issue alerts when specific thresholds were met. This approach worked when threats evolved slowly and security data was relatively uniform.

Today’s digital environments have shattered those assumptions. Enterprises operate across dozens of clouds, thousands of virtual machines, and constantly changing application layers. Each generates data at a scale and complexity traditional SIEM systems were never designed to handle. Even upgraded versions that claim cloud readiness struggle with ingestion costs, schema rigidity, and performance bottlenecks.

As attackers automate their campaigns through AI, polymorphic malware, and adaptive phishing, static detection rules are proving inadequate. Analysts note that many enterprises now spend more time tuning false positives than stopping real attacks. The economic burden has also risen sharply—subscription and storage costs for legacy SIEMs can exceed the price of newer AI-native pipelines. These factors together are driving the search for a more scalable, data-centric alternative.

How is Databricks using its Lakehouse architecture to position itself as a SIEM disruptor?

Databricks is betting that the answer lies in data intelligence rather than security event correlation. The company’s Data Intelligence for Cybersecurity platform extends its well-known Lakehouse architecture into the threat-defense arena. It can ingest raw telemetry from multiple systems, normalize it automatically, and apply machine learning models to detect anomalies in real time.

Instead of forcing data into a proprietary format, Databricks allows enterprises to query, visualize, and act within their existing cloud ecosystem. The inclusion of Agent Bricks—a framework for custom AI agents—adds a layer of governed automation. These agents can be programmed to respond to suspicious activity according to compliance policies, turning what used to be a static alert into a controlled, auditable action.

For organizations already using Databricks for analytics or AI, adopting its cybersecurity layer becomes a natural progression rather than a costly rip-and-replace. The broader proposition is simple: security is not a separate stack but an application of the enterprise’s existing data infrastructure. This shift reframes cybersecurity as an analytics and governance problem, not just an operations one.

Are other data-centric players like Snowflake and Google Chronicle following similar strategies?

The trend is far bigger than Databricks. Snowflake has been building its own bridge between cloud data platforms and security analytics. Through integrations with partners such as Panther Labs, Cribl, and Securonix, it markets itself as the “data cloud for security,” offering near real-time query performance on massive log datasets. By decoupling analytics from ingestion, Snowflake allows customers to build security data lakes that rival or replace traditional SIEMs.

Google Chronicle, part of Alphabet’s security portfolio and tightly integrated with Mandiant, is approaching the problem from a hyperscale perspective. It leverages the same distributed infrastructure that powers Google Search to store and correlate years of security telemetry. Chronicle’s architecture provides extraordinary scalability and global reach but remains a largely closed ecosystem, limiting the open data flexibility that enterprises increasingly demand.

Microsoft Sentinel remains the most entrenched SIEM alternative, thanks to deep integration with Microsoft 365 and Azure. Yet even it faces growing criticism over cost predictability and cross-cloud visibility. That leaves a strategic opening for Databricks and Snowflake—both of which emphasize openness, interoperability, and lower storage costs per event.

Industry observers expect the competitive frontier to center on how open these ecosystems remain. Databricks’ “bring your own model, bring your own stack” philosophy may appeal strongly to enterprises that have grown wary of vendor lock-in after years of consolidating under hyperscalers.

Why are enterprises shifting from rule-based detection to AI-native, data-driven security?

The most transformative force behind this evolution is the shift from manual, rule-based correlation toward machine learning models capable of learning normal behavior. Traditional SIEMs depend on pre-defined rules, which must constantly be updated to recognize new attack patterns. This reactive model cannot scale with the complexity of modern cloud workloads.

AI-native approaches, by contrast, treat every data source as a potential signal. They identify outliers, adapt to new behaviors, and continuously refine baselines without explicit human intervention. Databricks’ Lakehouse architecture makes this possible by keeping analytics, training, and inference in one unified layer. Rather than exporting logs to another platform, models can run directly on live data, cutting detection latency from hours to seconds.

Security teams benefit not only from speed but from context. With natural-language interfaces and conversational dashboards, Databricks and its competitors are enabling analysts and executives alike to interrogate their data without writing complex queries. This democratization of security insight is reshaping how Security Operations Centers operate—moving from alert triage to model supervision and automated decision validation.

How is regulatory pressure accelerating the move toward data-intelligent security systems?

Beyond technology, compliance is emerging as a major catalyst. Regulators in both the United States and the European Union have introduced stringent rules requiring transparency, auditability, and timely disclosure of cyber incidents. The U.S. Securities and Exchange Commission now mandates that material cyber events be reported within days. The EU’s NIS2 Directive and Digital Operational Resilience Act impose similar requirements on critical infrastructure and financial services.

Legacy SIEM tools can flag events but often fail to provide the data lineage and governance trail needed for regulatory audits. In contrast, data platforms such as Databricks and Snowflake include metadata catalogs, version control, and fine-grained access policies by default. These features make compliance a built-in capability rather than an add-on.

This convergence of governance and defense has also drawn global consultancies deeper into the space. Deloitte, Accenture Federal, and Varonis have all partnered with Databricks to co-engineer solutions for regulated industries, particularly in federal and healthcare sectors where traceability is non-negotiable. For many enterprises, the ability to meet compliance obligations while improving threat detection could justify migration to a data-intelligent architecture on its own.

What are investors and institutions expecting from this convergence of data and cybersecurity?

Institutional investors increasingly view the unification of data infrastructure and cybersecurity as the next multibillion-dollar frontier. Analysts estimate that the global SIEM market, currently worth around USD 7 billion, will plateau by the late 2020s as enterprises transition toward cloud-native, AI-powered data platforms. They expect half of large organizations to run their primary security analytics on such systems by 2027.

Databricks, still privately held and valued above USD 43 billion, now sits squarely in this narrative. Its platform already underpins analytics and AI workloads for major banks, telecoms, and manufacturers. Extending that footprint into security could significantly expand recurring revenue streams. The company’s recent partnerships with Deloitte and Accenture hint at an enterprise-scale go-to-market strategy that goes far beyond proof-of-concept deployments.

For publicly listed peers such as Snowflake and Alphabet, the market implications are equally strong. Investors are treating the rise of AI-driven data security as a natural successor to the cloud computing boom of the 2010s. The difference is that this new cycle is not just about hosting data but about defending it—an area where pricing power and switching costs could be even higher.

Can Databricks truly make data intelligence the foundation of cybersecurity in an era of AI-driven threats and regulatory pressure?

The boundary between data management and cybersecurity is disappearing. Databricks’ strategy—building cybersecurity into the very structure of data intelligence—captures the direction in which the industry is headed. Rather than relying on downstream tools, it positions data architecture itself as the first line of defense.

If the model succeeds, security teams will no longer manage dozens of separate tools for detection, storage, and reporting. Instead, they will operate unified data pipelines that are intelligent, governed, and capable of autonomous action. The partnerships Databricks has assembled, along with case studies from firms such as Arctic Wolf and SAP, suggest that the transition is already underway.

Skeptics note that replacing entrenched SIEM deployments will take time, particularly in highly regulated enterprises where procurement cycles move slowly. However, as data volumes and compliance costs continue to rise, the argument for unifying security and analytics grows stronger. The future of cybersecurity may not be a dashboard—it may be a data lakehouse capable of learning, predicting, and responding faster than human teams ever could.

If Databricks and its peers can deliver that vision at scale, the term “SIEM” itself could soon become a historical artifact of the pre-AI security era.