top of page

Mastering SIEM Deployments: Lessons from the Trenches

Writer's picture: Mario SalomoniMario Salomoni

A stylized shield on a black background.

Deploying and managing a Security Information and Event Management (SIEM) solution is both an art and a science. Drawing from years of experience deploying SIEM systems, particularly Splunk and Splunk Enterprise Security, I’ve witnessed the pitfalls, triumphs, and nuances of what it takes to turn a deployment into a success story. Here, I’ll share insights from the trenches, distilling years of hands-on experience into practical advice for technical experts and managers alike. While applicable to any SIEM, some sections include a deeper dive into Splunk and Enterprise Security (ES). This article is aimed at basic to intermediate practitioners who want to improve their SIEM strategies and maximize their tool’s value.


Understand the Why Before the How

Before diving into the technical nuts and bolts of a SIEM deployment, take a step back and ask, “Why are we doing this?” SIEM solutions are expensive and resource-intensive, so it’s vital to have a clear understanding of your goals. When multiple objectives coexist, prioritize them based on their impact on your organization’s security posture and compliance needs. For instance, compliance might be a foundational requirement, while threat detection can evolve with the SIEM's maturity. Rank objectives by urgency and potential return on investment, and ensure alignment with your broader security strategy to maximize the system’s value. Are you trying to achieve compliance? Improve your threat detection capabilities? Gain visibility on areas where you are completely blind? Streamline incident response? Or all of the above? Without this clarity, the deployment risks becoming a patchwork of disconnected use cases that fail to deliver meaningful value.


Stakeholder Alignment is Key

A SIEM isn’t just a technical tool; it’s an organizational one. Its success depends on aligning the technical team’s efforts with the broader business objectives. This means engaging all stakeholders early, from security analysts and IT teams to compliance officers and executive sponsors. Everyone should understand what the SIEM will deliver and what it won’t. Set realistic expectations and define clear roles to avoid the common trap of misaligned priorities.


The Core Foundations: Identity and Asset Management

At the heart of every successful SIEM deployment are two pillars: identity and asset management. These foundational elements enable you to correlate logs with meaningful context, transforming data into actionable insights.

Understanding your assets means more than just listing servers and workstations. It involves assigning ownership, determining criticality, and keeping this information up to date. For example, knowing whether an alert is linked to a production server or a testing sandbox drastically changes its priority and required response.

Similarly, robust identity management involves mapping users to their various accounts (personal, administrative, or service) and monitoring privileged identities for anomalies. Integrating asset and identity data with your SIEM ensures logs are enriched with relevant details like IP addresses, roles, and historical activity, making correlations seamless and effective.

For organizations with limited or incomplete asset inventories or identity databases, building a reliable starting point can seem daunting. However, leveraging existing tools and data sources can provide a practical foundation. Active Directory, vulnerability management logs, DNS records, DHCP logs, and even HR systems can serve as initial sources to compile a basic asset and identity database. Correlating this information manually or using simple scripts can yield significant results. As a result, the effort not only improves immediate visibility but often highlights gaps that justify further investments in asset management tools or systems.


Data Strategy: Garbage In, Garbage Out - Balancing Data Abundance with Cost Constraints

One of the most underestimated aspects of a SIEM deployment is data strategy. A SIEM’s effectiveness is directly tied to the quality, relevance, and structure of the ingested data. However, more data doesn’t always mean better results. Balancing data volume and relevance against cost constraints is a critical task. Focus on:

  • Data Prioritization: Not all data sources are equal. Identify the most critical data sources for your use cases, such as firewall logs, endpoint data, and identity management logs. For example, while web proxy logs may seem important, they might be deprioritized if your immediate focus is on internal threat detection. Similarly, raw packet captures can be deferred if aggregated NetFlow data suffices for the use case. Start small, but plan for future expansions. Evaluate costs and benefits for each source.

  • Normalization: Ensure consistency in how data is ingested and stored. Normalized data is easier to search, analyze, and correlate. For example, in Splunk, leveraging the Common Information Model (CIM) ensures that your data conforms to a standardized format, making it simpler to create dashboards and correlations. Adopting CIM not only streamlines the process but also aligns your deployment with best practices for scalability and efficiency. For Splunk solutions, learn CIM philosophy and rules and adopt them.

  • Don’t Be Too Greedy: Excessive tuning or filtering on sources can save something on costs, but consider that the data you filter out may have value you just are not aware of yet. Consider the overall scope: it doesn’t make sense to filter out a few kilobytes of data when you are already ingesting gigabytes.

  • Segregation: Immediately define how the data should be separated, which sources contain critical information and require more strict access control.

  • Retention Policies: SIEM storage can be costly. Define retention policies that balance compliance requirements with cost efficiency.

  • Reliability: Put in place tests to ensure your sources are being collected correctly and that the format you expect does not change. For example, after a system upgrade, ensure log formats remain compatible to avoid parsing errors.


Correlation: The True Power of SIEM

The true power of a SIEM lies in its ability to correlate logs across disparate sources to uncover patterns and anomalies. By linking identity and asset data to diverse data sources, you create a unified narrative that transforms scattered logs into actionable intelligence.

Consider an advanced use case: detecting a phishing campaign by combining DNS queries, firewall logs, and endpoint activity. A query to a known malicious domain paired with unusual outbound traffic and a flagged executable download creates a clear picture of the threat. Regularly revisiting and refining correlation rules ensures they evolve with the threat landscape, maintaining their relevance and accuracy.


Use Case-Driven Approach

A SIEM without defined use cases is like a car without a destination. Start small and focus on high-value use cases that address immediate business needs. Common examples include:

  • Detecting anomalous login behavior (like irregular login times, unusual privilege escalations, failed login attempts followed by successful logins, logins from unusual places…).

  • Monitoring critical system changes (like changes in Group Policies or sudo rules).

  • Identifying possible malware infections through network traffic analysis (like traffic between endpoints that have no reason to communicate).

Document these use cases thoroughly, including the required data sources, detection logic, and response actions. Build iteratively; once initial use cases are stable, expand your scope. An iterative approach works best: deploy a small number of high-confidence use cases and expand as your team gains proficiency and data quality improves.

Try not to define use cases that are too vertical or specific as they tend to become obsolete fast and usually cover a very small target. Prefer "behavioral" or anomaly detection use cases, which can look for what is wrong or unusual and not just specific patterns. For example, monitoring login attempts outside normal working hours or detecting unusual file access patterns from privileged accounts can highlight potential insider threats or compromised credentials. Such use cases adapt better to evolving threats and provide more meaningful insights than static pattern-based detections. These tend to last much longer and are far more effective at detecting new unforeseen attacks (but they are much harder to engineer and tune).


Tune Before You Alert

One of the fastest ways to erode confidence in a SIEM is through false positives. Tuning is a continuous process that requires time and effort, but it’s essential for building a reliable alerting framework. Collaborate with your security analysts to fine-tune thresholds, filters, and correlation rules. Prefer adaptive thresholds whenever possible. Remember, it’s better to start with a narrow focus and expand as confidence in the system grows.


Automation and Orchestration: Proceed with Caution

Automation can amplify the capabilities of a mature SIEM deployment, but premature implementation can backfire. Security Orchestration, Automation, and Response (SOAR) solutions are invaluable for automating repetitive tasks like enriching alerts with threat intelligence or triaging low-priority alerts. However, these tools should only be integrated once your SIEM is stable and well-tuned.

For example, automating the tagging of suspicious IP addresses based on threat intelligence feeds reduces manual workload, but only if the underlying data and correlation rules are reliable. Premature automation risks magnifying inefficiencies and introducing errors.


Continuous Feedback Loop

A SIEM deployment requires ongoing attention, regular adjustments, and alignment with changing organizational and security needs. Avoid the temptation to treat it as a one-time implementation, as its value grows through continuous refinement and proactive management. Regularly review its performance, adapt to changing threats, and incorporate feedback from the team. Schedule periodic sessions to evaluate what’s working, what isn’t, and where the system can be improved. Metrics such as Mean Time to Detect (MTTD) and Mean Time to Respond (MTTR) are invaluable for measuring success. To track these effectively, consider using dashboards within Splunk or other monitoring tools that can visualize incident timelines and response workflows. Additionally, integrating metrics tracking with ticketing systems like ServiceNow or Jira ensures that data is logged and trends are analyzed over time. Leveraging these tools provides both real-time visibility and historical context for continuous improvement.

Test continuously that the use cases you are running are working as expected. Utilize structured testing methods like red teaming exercises or simulated attacks to validate the effectiveness of detection logic. Frameworks such as the MITRE ATT&CK can help map your use cases to real-world threats, ensuring they are comprehensive. Additionally, conduct regular "tabletop" exercises with your security team to evaluate workflows and fine-tune response actions based on different scenarios.


Choosing Between Risk-Based and Alert-Based Approaches

Selecting the right detection methodology for your SIEM involves carefully weighing the specific needs of your organization against the strengths of each approach. Risk-based methods focus on long-term trend analysis by assigning scores to users, assets, or events. These scores evolve as new data is ingested, enabling you to identify broader patterns and prioritize responses based on cumulative risk. This approach is particularly useful in environments with high volumes of data, where focusing on every alert individually is impractical.

On the other hand, alert-based methodologies are geared towards real-time detection. They generate immediate notifications for critical events, ensuring swift action can be taken when time is of the essence. For example, a sudden spike in failed login attempts might trigger an alert, prompting an immediate investigation.

A hybrid strategy often provides the most comprehensive coverage. By combining risk scores with real-time alerts, you can prioritize high-risk activities without ignoring critical, time-sensitive incidents. Regularly calibrating both risk scoring models and alert thresholds is essential to avoid bias and ensure accuracy. This approach balances long-term security trends with the need for rapid responses to emerging threats.


Integrating Threat Intelligence

Threat intelligence enriches raw data with external context, making it actionable. By tagging IPs, domains, or file hashes with reputation scores, analysts can prioritize their focus on high-risk events. However, not all feeds are created equal. Prioritize high-confidence sources to avoid alert fatigue and reduced productivity.

For instance, integrating a threat feed that provides real-time updates on known malicious domains can significantly enhance detection capabilities, particularly for phishing-related use cases. To evaluate the effectiveness of such feeds, consider metrics like the reduction of false positives and how often the feed highlights genuine threats. Regularly review feed performance and ensure it aligns with your organization’s specific threat landscape to avoid unnecessary noise. Automation can streamline feed updates and enrichment workflows, ensuring consistency and efficiency.


Unlocking the Power of CIM and Datamodels in Splunk

Splunk’s Common Information Model (CIM) and datamodels are foundational elements for achieving scalable, efficient, and insightful analytics. The CIM provides a standardized framework for data normalization, ensuring that logs from diverse sources adhere to consistent field naming conventions. This uniformity simplifies correlations and accelerates the creation of dashboards, reports, and alerts.

Datamodels build on this foundation by structuring data into tables optimized for specific use cases. When accelerated, these datamodels allow for lightning-fast searches, even across months of data. For example, an investigation into potential brute-force attacks can query months of authentication logs within seconds, providing security teams with rapid insights.

To unlock the full potential of CIM and datamodels, ensure that fields are correctly mapped during data ingestion. This requires close attention to detail and a thorough understanding of your data sources. Additionally, investing in adequate hardware resources is crucial to support the performance demands of accelerated datamodels.

While powerful, these features are not without challenges. One recurring debate revolves around whether the performance trade-offs of accelerated datamodels justify their use. Critics argue that the resource demands can outweigh the benefits, particularly in constrained environments. However, when properly implemented, these datamodels enable lightning-fast searches and enhance investigative efficiency, especially in high-volume environments where speed is critical. For instance, accelerated datamodels can significantly streamline investigations into brute-force attacks by allowing analysts to query months of authentication logs in seconds. Regular monitoring of system performance and strategic resource allocation are key to overcoming potential drawbacks, ensuring these features deliver their full value. By leveraging CIM and datamodels effectively, you can transform your Splunk deployment into a high-performance, insight-driven security platform.


Building and Training Your Team

Even the best SIEM solution is only as effective as the team operating it. Continuous training ensures analysts are equipped to manage evolving threats and maximize the system’s potential. Teams should not only understand the technical aspects of the SIEM but also the broader security context, enabling them to connect the dots during investigations.


Avoid Common Pitfalls

Drawing from extensive experience with SIEM deployments, it’s clear that certain pitfalls can significantly undermine success. Avoiding these common traps can save time, reduce costs, and ensure your system delivers its intended value:

  • Overloading the SIEM: Ingesting every log source possible without a clear plan leads to bloated costs and poor performance.

  • Useless Use Cases: Use cases that generate excessive noise, without being actionable or delivering real value, must be avoided. They undermine the credibility and effectiveness of the entire SIEM system.

  • Neglecting Documentation: Good documentation accelerates onboarding, troubleshooting, and compliance audits.

  • Underestimating Costs: Beyond licensing, factor in infrastructure, maintenance, and personnel costs.

  • Ignoring Change Management: A SIEM is a living system. Failing to account for organizational and technical changes will leave it outdated.


Mastering the Art of SIEM

SIEM deployments are complex and often underestimated. A successful SIEM deployment combines technological excellence, strategic planning, and a focus on people and processes. By anchoring your deployment in strong asset and identity management, prioritizing data quality, and methodically expanding use cases, you can create a resilient system that delivers lasting value. Automation and threat intelligence, when implemented thoughtfully, further amplify this value, turning your SIEM into a cornerstone of your organization’s security strategy and invaluable tools for security operations.

If you’re looking for a more technical deep dive into any of the areas discussed, feel free to share your feedback and suggestions or contact me. Remember, success lies not just in collecting logs but in transforming them into actionable intelligence that strengthens your organization’s security posture.

Whether you’re a seasoned expert or a manager overseeing the process, I hope these lessons from the trenches will help you navigate the complexities of mastering SIEM deployments.

57 views0 comments

Comments


Follow

Contact

Address

  • facebook

+41 91 945 04 77

Via Industria 1
6814 Cadempino
Switzerland

© 2024 by Kstone SA. All rights reserved.

bottom of page