As cyber threats grow in complexity and volume, AI-driven Security Automation Solutions (AI SOC) have emerged to automate and accelerate threat investigation and response. These platforms leverage Agentic AI to analyze security signals, investigate anomalies, and automate swift actions. However, the efficacy, cost, and scalability of an AI SOC largely depend on how it ingests and processes security data.

A critical factor is alert ingestion, as it forms the core input for AI SOCs. AI SOCs can source alerts from three primary methods:

1. Directly from security tools generating alerts (e.g., firewalls, EDRs).

2. Through a SIEM, which centralizes and correlates security events.

3. From a Data Fabric-powered security data lake, offering scalable, flexible data access.

Traditionally, SIEMs have served as the backbone for security data aggregation, doubling as Security Data Lakes. However, Data Fabric architectures are now emerging as a more scalable, cost-effective, and flexible alternative for data collection, storage, and distribution.

In this blog, we evaluate the pros and cons of these approaches and how AI SOCs can optimize automation through strategic data ingestion choices.

1. Directly from the Source System

✅ Pros:

• Most cost-effective – No intermediary systems, reducing infrastructure costs.
• Fastest ingestion – Data flows in real-time from the source.
• More reliable – Fewer failure points in data transmissio.

❌ Cons:

• Raw data vs. alerts – Not all source systems generate actionable alerts, making threat detection complex.
• Scalability challenges – Each security tool requires custom integration, which becomes unmanageable at scale.
• Data duplication – Organizations often still need a centralized security data repository (SIEM or data lake), leading to redundant connectors.

Conclusion:

Fetching alerts directly from the source is effective for small-scale integrations but does not scale efficiently across an enterprise security ecosystem.

2. From a SIEM

✅ Pros:

• Centralized alerting – SIEMs aggregate alerts from multiple sources, acting as a single integration point.
• On-demand access to raw events – While cumbersome, SIEMs provide a way to access historical security data, which can be useful for investigations.
• SIEM-generated detections – Next-Gen SIEMs use correlation-based detections, adding value beyond raw security signals (e.g., Microsoft Active Directory correlations).

❌ Cons:

• Integration bottlenecks – SIEMs are designed for ingesting and storing data, not for real-time export, making them inefficient for AI SOC automation.
• Limited ad-hoc search – AI SOC automation thrives on rapid data retrieval, but SIEMs often lack the speed needed for real-time investigations.
• Cost overhead – SIEM licensing and storage costs can be significant.

Conclusion:
While SIEMs offer a convenient centralized repository, their rigid architecture and inefficiencies can hinder AI SOC automation.

3. From Data Fabrics

✅ Pros:

• Best of both worlds – Data Fabric allows real-time ingestion from sources while also providing centralized storage for historical analysis.
• Data democratization – Unlike traditional storages, Data Fabric solutions provide flexible, searchable, and shareable security data access, improving investigations.
• Scalability & cost savings – Cloud-based data lakes offer cost-effective, long-term storage without SIEM’s expensive licensing model.
• Optimized for AI SOC automation – AI SOCs can extract, analyze, and act on security signals without performance constraints.

❌ Cons:

• Architectural shift required – Organizations must redefine data flows between SIEM, Data Fabric, and AI SOC to optimize cost and performance.
• Implementation complexity – Transitioning to Data Fabric-driven security architectures requires a strategic assessment of tools, workflows, and operational impact.

Conclusion:
AI SOC + Data Fabric offers a scalable, cost-effective, and flexible approach to threat detection and response. However, implementation requires careful planning and alignment with security and compliance goals.

The Road to a Modern SOC: Making the Right Choice

While Data Fabric represents the future of security data management, the reality is that most AI SOC solutions today—including AiStrike—opt for the path of least resistance. This means prioritizing integrations that minimize customer overhead, such as ingesting alerts from SIEMs or directly from security tools.

However, as organizations redefine their SOC architectures, they must evaluate the trade-offs between these approaches. AI SOC automation thrives on structured, contextual, and real-time data—making Data Fabric an ideal enabler of autonomous cybersecurity operations.

The evolution is already underway. The question is—how fast will your SOC adapt?