Source URL: https://medium.com/anton-on-security/output-driven-siem-13-years-later-c549370abf11?source=rss—-8e8c3ed26c4c—4
Source: Anton on Security – Medium
Title: Output-driven SIEM — 13 years later
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AI Summary and Description: Yes
Summary: The text discusses the evolution and relevance of output-driven Security Information and Event Management (SIEM) over 13 years, highlighting its necessity in effectively managing security data. The author emphasizes that effective logging and data collection practices are essential for reducing alert fatigue and optimizing security operations. In light of advancements and future predictions about AI in Security Operations Centers (SOCs), the text presents insightful implications for compliance and security professionals in the realm of data management.
Detailed Description:
The author reflects on the concept of “output-driven SIEM,” initially proposed in 2011, and its enduring significance in 2025. The main themes and insights of the text include the following points:
– **Definition of Output-Driven SIEM**:
– Focuses on collecting data intentionally, only when its utilization is clear.
– Distinguishes from simply not collecting data unless there’s a direct detection reason.
– **Collecting with Purpose**:
– Emphasizes having a clear rationale (“the WHY”) for any data collected.
– Highlights the necessity of context, incident response (IR), and regulatory compliance in logging.
– **Evolution of SIEM Practices**:
– The text notes a period where “just collect it” was viable due to cheaper storage; however, storage demands have since surged.
– SOAR (Security Orchestration, Automation and Response) was introduced as a workaround for unrefined collection practices, not addressing fundamental issues in detection.
– **Role of AI in Security**:
– Introduces a concept called “AI SOC,” which may enhance detection efforts but also carries implications for data collection.
– Predicts a shift from “output-driven SIEM” to “outcome-driven SOC” facilitated by AI agents, defining strategic security outcomes rather than merely reacting to alerts.
– **Architecting the Future of SIEM**:
– Suggests a potential model combining modern storage techniques with traditional practices (e.g., two-tier systems with SIEM and centralized log management).
– Discusses the cost implications of log collection and retention, urging consideration of long-term costs versus system capabilities.
– **Real-World Applications and Challenges**:
– The author presents a realistic outlook on the limitations and future of AI in SOCs, questioning the feasibility of automating the entire process.
– Encourages ongoing dialogue within the security community to navigate these evolving challenges.
Overall, the discussion around output-driven SIEM serves as a critical reminder for security and compliance professionals of the importance of strategic approaches to data management and incident response, as well as the implications of integrating AI within security operations. This could lead to enhanced efficiency, effectiveness, and reduced fatigue in managing security alerts and responses.